International Journal of Embedded Systems (112 papers in press)
by Jingzhao Li, Dayu Yang, Xiaoming Zhang
Abstract: Cyberphysical systems (CPS) have made great strides in industrial control, intelligent transportation, remote medical and other fields. Coal mine transportation control system is a representative multi-subsystem of CPS. In this study, to optimise the scheduling mechanism for event response and precise control of tramcar behaviour in the system, we propose an event-orient scheduling algorithm (EOSA) to achieve a rapid response based on building a no-memory continuous time model. Therefore, the control system can match tasks in the queue according to the real-time load situation, and each physical entity can accurately execute the instruction under discrete event environment. Simulation results have shown that the proposed algorithm has higher execution speed compared with the hybrid genetic algorithm and fuzzy clustering scheduling algorithm. Our approach realises load balancing of global task scheduling and is more suitable for mine transportation scenarios.
Keywords: cyberphysical systems; mathematical model construction; scheduling strategy; priority queuing; mine transportation.
by Quanzhi Lei, Lijun Xiao, Osama Hosam, Haibo Luo
Abstract: Watermarking is a popular technology to protect the copyright of a digital image from illegal infringement. Traditional watermarking algorithms cannot fully resist the illegal tampering attacks, causing serious security issues. This paper aims to propose an image watermarking algorithm with ability against illegal tampering attacks. The original image is firstly pre-processed, and its fragmentary characteristic function of local image block is used to generate watermark locations randomly. When the watermark is illegally tampered and damaged, the generated fragmentary characteristic factor can be used to re-construct the original image. Finally, the forensics of the original image is realised. The experimental results show that the proposed algorithm can effectively embed a watermark into the image. Besides, the original image can be restored even when the watermark is damaged. It is also robust to the typical attacks and has ability against tampering over a large area of the image.
Keywords: local image; watermark; fragmentary image; characterization factor; tampering attack.
On the security of a security-mediator-based sharing stored data in the cloud
by Jianhong Zhang, Qiaocui Dong, Jian Mao, Xu Min
Abstract: As an important service of cloud computing, the cloud storage can relieve the burden for storage management and maintenance since the data owners' data are moved to the cloud from their local computing system. However, after data are outsourced to the cloud, data owners no longer physically possess the storage of their data. To ensure the correct storage of the outsourced data, data owners need to periodically execute the integrity verification of data. However, in most existing data integrity checking protocols, a data owner's identity is inevitably revealed to public verifiers in the process of integrity verification. Recently, in order not to compromise the privacy of data owners' identity and not to increase overheads significantly, Wang et al proposed an efficient publicly verifiable approach to ensure cloud data integrity by including a security mediator. The identity of the data owner is hidden through the signature, which is produced by the security mediator. Unfortunately, in this paper, we show that their scheme is insecure. It is prone to unforgeability attack, tamper attack and active attack.
Keywords: attack; tamper attack; active attack; data integrity checking; security analysis; cloud storage.
A local HMM for indoor positioning based on fingerprinting and displacement ranging
by Ayong Ye, Jianfei Shao, Zhijiang Yang
Abstract: Hidden Markov models (HMMs) are powerful probabilistic tools for modelling sequential data, and have been applied to indoor positioning tentatively by combining RSSI fingerprinting method with inertial sensors. In that case, positioning is considered as from an isolated location estimating to a sequential locations transition process. Then the positioning is transformed to the prediction problem in HMMs. However, because the location estimating depends on the previous estimated location and its estimating error, the cumulative error and the resonance error may increase in a continuous positioning process over time. This paper presents an approach to divide the whole continuous positioning process into specified-size sub-processes, and each of them is independent. For this approach, the cumulative and resonance error caused by iterative estimation could be reduced efficiently. Meanwhile, we develop a prototype system, and conduct comprehensive experiments. The evaluation results demonstrate the effectiveness of the proposed approach.
Keywords: indoor positioning; hidden Markov models; RSSI fingerprinting; displacement ranging; Wi-Fi; accelerometer.
Identification and addressing of the internet of things based on distributed ID
by Rui Ma, Yue Liu, Ke Ma
Abstract: It is a key issue that identification and addressing the entity which access the Internet through the wireless network with various short distance transmission protocols. To solve the problem, combining with the existing identification and addressing technology of the Internet and the IOT, this paper proposes a method based on distributed ID. This method can be divided into two stages. It first designs the structure of distributed ID. Using the distributed address allocation algorithm, it implements the automatic allocation of the distributed ID as well as the distributed ID resolution among the global IOT. After that, an addressing scheme is designed to meet demands of the IOT addressing. It first defines the structure of addressing and then implements the routing addressing algorithm which combines the cluster-tree algorithm with the ad-hoc on-demand distance vector routing algorithm. This scheme could improve the routing efficiency as well as achieve lower cost, lower energy consumption and higher reliability of the addressing. By the simulation on NS-2 platform, the experimental results highlight the feasibility and effectiveness of the proposed method from three aspects: the correctness of identification allocation, the effectiveness of addressing scheme, and the stability of data transmission.
Keywords: internet of things; identification; addressing; AODV; cluster-tree.
Constant-size ring signature scheme using multilinear maps
by Xiangsong Zhang, Zhenhua Liu, Fenghe Wang
Abstract: Ring signature is a group-oriented digital signature with anonymity. Most existing ring signature schemes use bilinear pairings, are provably secure in the random oracles, or are linear signature size to the number of ring member. In this paper, we use multilinear maps, which have been widely used to construct many novel cryptographic primitives recently, to present a ring signature scheme with constant signature size. The proposed scheme is proven to be anonymous against full key exposure and unforgeable against chosen-subring attacks based on the multilinear computational Diffie-Hellman assumption in the standard model. Furthermore, our scheme has the advantage of tighter security reduction by using an optimal security reduction technique.
Keywords: ring signature; multilinear maps; security reduction; provable security; standard model.
Identification of cascading dynamic critical nodes in complex networks
by Zhen-Hua Li, Dong-Li Duan
Abstract: Catastrophic events occur frequently on the internet, power grids as well as other
infrastructure systems, which can be considered, to some extent, to be triggered by minor events. To study the dynamic behaviour of these systems, we generally should simplify them as networks. The reason lies in that the network can be seen as the
skeleton embedded in the internet system, power grid, transportation and traffic system, as well as other infrastructure systems. We should pay more attention to these backbone networks so as to explore the dynamic behaviour and mechanisms of the embedded systems more deeply and broadly. One of the major problems in the field of networks is how to identify the critical nodes. In this paper, we explore the identification method of cascading dynamic critical nodes in complex networks. By the average load oscillation extent of the affected nodes caused by attacking one node, we define the importance indicator of the attacked node with a cascading failure model based on a load preferential sharing rule. The indicator has two characteristics: one is that the failure consequence of the considered node can be clearly pointed out by its value. If I(i) ≥ 1, the node I will trigger the next round overload. If I(i) < 1=ki , the node i will be a single failure. If 1=ki ≤ I(i) < 1, the outcome will not be determined, namely the failure of i may trigger
the overload of its neighbour node or may not. The other is that the evolution mechanism of node importance can be analysed with the factors of load redistribution mechanism, node capacity, and structural characteristics of the network. For example, we can see that the value of i determines the distribution of the node importance: in the case i = 1 we have I(i) ∼ k0 i , namely the node importance is independent of the nodes degree. If ̸= 1 we have I(i) ∼ k-1
i , the node importance scales with the node degree and P(I) is driven by P(k). The experiments demonstrate the effectiveness and feasibility of the indicators
and its algorithm, with which we also analyse the node importance evolution mechanism
Keywords: complex networks; node importance; cascading failure; load redistribution rule; overload mechanism; scale-free networks; ER networks; power grid.
Multi-objective fuzzy optimisation of knowledge transfer organisations in the big data environment
by Chuanrong Wu, Feng Li
Abstract: With the advent of the big data era, the information from big data has become a type of important knowledge that enterprises need for innovation. The knowledge transfer mode and the influence factors of big data knowledge providers are different from those of traditional knowledge providers. Based on an analysis of organisational composition and characteristics of knowledge transfer in the big data environment, the influence factors and evaluation index systems of big data knowledge providers are established. A multi-objective fuzzy optimisation model is constructed to derive satisfactory knowledge providers by finding optimal sequences. Meanwhile, this model can provide a cooperative decision-making method for knowledge transfer organisations to enhance the efficiency of knowledge transfer in the big data environment.
Keywords: big data; knowledge transfer; multi-objective optimisation; organisation; decision making method.
A novel algorithm for TOP-K optimal path on complex multiple attribute graph
by Kehong Zhang, Keqiu Li
Abstract: In the rapidly-changing information world, the various users and personalised requirements lead to an urgent need for complex multiple attribute decision-making. In addition, the optimal solution of a single attribute decision cannot meet the actual needs. The TOP-K optimal path is an effective way to solve the above problem. The TOP-K mainly has non-repeatable vertex algorithm, repeatable vertex algorithm, index and other algorithm. But these techniques are mainly based on the single attribute. There are few documents introducing the complex multiple attribute decision-making problem so for. Therefore, a Tdp algorithm is presented in this paper. Firstly, it uses the technology of interval number and extreme value to solve the uncertain attribute value. Then TOPSIS technique solves the complex multiple attribute decision-making problems. In this way the comprehensive score was achieved. Secondly, by analysing the Yen algorithm, the paper proposes blocking and bidirectional shortest path algorithm for TOP-K optimal path. Finally, comparison and analysis between Tdp and the Yen were made. Results confirm that the Tdp algorithm improves the TOP-K optimal technology.
Keywords: multiple attribute; optimal path; blocking; bidirectional; deviation vertex; TOP-K; decision-making.
A new identity-based public auditing against malicious auditor in the cloud
by Kun Qian, Hui Huang
Abstract: With the development of cloud computing, the integrity of data is becoming increasingly important. The auditing schemes for data integrity allow data owners to verify the integrity of the data stored in an untrusted server. Most of public auditing schemes are based on the public key infrastructure (PKI), which may lead to certificate management problems. Recently, an identity-based public auditing scheme was proposed and it could effectively reduce computation cost of auditors and solve certificate management problems. However, the scheme was proved to be insecure. In this paper, we consider the malicious auditor and propose a new identity-based public auditing against malicious auditors in cloud computing. The new construction is proved to be secure by assuming the hardness of the computation Diffie-Hellman problem (CDHP). Finally, compared with the existing identity-based auditing scheme, our scheme is efficient and reduces the computation overhead of the auditor.
Keywords: data storage; cloud computing; public auditability.
A new mobile opportunity perception network strategy and reliability research in coal mines
by Ping Ren, Jing-Zhao Li, Da-Yu Yang
Abstract: Aiming at the characteristics of application of Internet of Things in underground coal mines, this paper proposes a new method of mobile opportunity perception in coal mines, which makes full use of underground mine mobile resources. The method mainly uses the existing resources, without increasing or adding sensing devices, by deploying wired nodes, wireless fixed nodes and wireless mobile nodes, to establish the collaborative working mechanism of mobile nodes and fixed nodes, and accordingly builds environment opportunity perception and information transmission of the whole place in the mine and achieves a comprehensive perception of the mine. A novel sparse heterogeneous fusion network and its mixed node arrangement strategy and low-order error detection model between nodes are established, and the loss probability and redundancy perception between the mobile and fixed nodes are analysed, which improves the reliability of the system. The moving speed, the average moving distance and the communication coverage time of mobile nodes are analysed, and an interactive data quantity is obtained. On this basis, the system hardware design and experiment are carried out. Experimental results show the superiority of this method, which provides a new method and idea for industrial and mining enterprises in the sensing layer and transport layer application of the Internet of Things.
Keywords: mobile sensing; opportunistic routing; heterogeneous fusion; low false alarm; lost probability.
DPVFS: a dynamic procrastination cum DVFS scheduler for multicore hard real time systems
by Shubhangi Gawali, Biju Raveendran
Abstract: Optimising energy consumption has become primary focus of research in recent years. Static and dynamic energy optimisation during task scheduling is one of the most prominent measures available. This is achieved mainly by shutdown and slowdown techniques. In uniprocessor real-time systems, the most widely used shutdown and slowdown techniques are Dynamic Procrastination (DP) and Dynamic Voltage and Frequency Scaling (DVFS). This paper proposes DPVFS a hard real-time task scheduler for multicore system to optimise overall energy consumption without deadline misses. DPVFS combines DP and DVFS for multicore systems to save overall energy consumption. DPVFS shut the processor down whenever possible with the help of procrastination. If shutdown is not possible, it adjusts the voltage and frequency to reduce dynamic energy consumption. The experimental evaluation of DPVFS with synthetically generated benchmark program suites shows savings of 18.8% and 33.2% of overall energy over DP-based schedulers and DVFS-based schedulers respectively.
Keywords: procrastination; dynamic voltage and frequency scaling; multi-core real-time scheduling.
Energy oriented EDF for real-time systems
by Gil Kedar, Avi Mendelson, Israel Cidon
Abstract: Energy is a major concern when designing real-time systems. A common method for saving energy while still guaranteeing the real time constraints is to embed dynamic voltage and frequency scaling (DVFS) mechanisms and dynamic power management (DPM) mechanisms within a real-time scheduling algorithm, such as EDF. This paper proposes a new extension to the EDF scheduler, termed energy oriented EDF (EO-EDF). The new scheduler makes it possible to change the original EDF task execution order to better use the slack time and thus decrease the energy consumption, while still meeting the task deadlines. The new task order is defined according to a novel criterion we invented, termed task prediction order (TPO). The paper introduces two new versions of the EO-EDF scheduler, termed TPO-EDF and STPO-EDF. While STPO-EDF applies the TPO criterion in a static manner, TPO-EDF allows it to be used dynamically. We simulate the new proposed algorithms using both synthetic workloads and real time benchmarks. The evaluations show that integrating both the TPO-EDF and STPO-EDF scheduling algorithms with DVFS and DPM mechanisms achieves an energy saving of 30% on average, in comparison with current known EDF-based using DVFS and DPM mechanisms.
Keywords: low energy; real-time; scheduling; EDF; EO-EDF; TPO.
Model construction and application of coal mine CPS perception and control layer
by Ping Ren, Jingzhao Li, Dayu Yang
Abstract: Coal mine cyber-physical system (CPS) is a complex system that combines the information resources and the physical resources, which are established above and below ground. The system realises the integrated design of the information world and the physical world, so that the integrated system is more reliable and efficient. The key technology of coal mine CPS is the construction of a physical system model and information processing. Aiming at the situation of the coal mine industrial system consisting of several different physical systems, in this paper we first analyse the parametric model of infection control layer and construct the continuous-time system model, including the non-memory continuous time system model, the memory system model, the feedback control system, and the discrete-time system model. During the actual production process, considering that some systems of coal mine CPS are continuous time and discrete time hybrid systems, by analysing the parameters of the inclined lane signal subsystem, the anti-wrong turnout subsystem, the anti-slip car subsystem, the frequency control system, such as system function, input signal, state transition, and control output, a hybrid system state model with n inputs and m outputs is proposed, and the parameters application model of perception and control layer is obtained. In practical application, the inclined lane transportation architecture has achieved good results.
Keywords: cyber-physical system; perception and control layer; coal-mining industry; inclined tunnel transportation; model; status.
MOESIF: an MC/MP cache coherence protocol with improved bandwidth usage
by Geeta Patil, Biju Raveendran, Neethu Bal Mallaya
Abstract: This paper proposes a novel cache coherence protocol MOESIF - to improve the off-chip and on-chip bandwidth usage. This is achieved by reducing the number of write backs to next level memory and by reducing the number of responders to a cache miss when multiple copies of data exists in private caches. Experimental evaluation of various SPLASH-2 benchmark programs on CACTI 5.3 and CACOSIM simulators reveals that the MOESIF protocol outperforms all other hardware-based coherence protocols in terms of energy consumption and access time. MOESIF protocol on average offers 94.62%, 88.94%, 88.88% and 4.47% energy saving, and 96.37%, 92.83%, 92.77% and 9.21% access time saving over MI, MESI, MESIF and MOESI protocols, respectively, for different numbers of cores/processors.
Keywords: cache coherence; MC/MP cache; energy-efficient coherence protocols;.
A detection model of malicious Android applications based on Naive Bayes
by ChunDong Wang, Yi Zhao, XiuLiang Mo
Abstract: With the popularity of mobile devices, thousands of malicious applications targeting mobile devices, including the popular Android platform, are created on a daily basis, which cause substantial losses for their users. How to detect malicious applications efficiently has become a new and ever-growing challenge. However, previous studies overlooked malicious potential permission combinations as a feature in detection. In this paper, according to the Android permission mechanism, we propose and implement a detection model based on Naive Bayes. The model uses the Apriori algorithm to effectively mine the potential correlation in permissions among the various malicious applications. Then, in order to improve the performance of the Android malware detection system, the additional feature methodology proposed in this paper is used to deal with samples which have dangerous permission combinations. Combined with the improved Naive Bayes classifier, samples are classified into two categories. The experimental result reveals that the optimal detection rate in our detection model is 95.63%. Thus, it significantly improves the accuracy of the Naive Bayes in the detection of malicious Android applications.
Keywords: Android permission; malware detection; machine learning.
An efficient privacy-preserving friendship-based recommendation system
by Bingpeng Ou, Jingjing Guo, Xiaoling Tao
Abstract: With the development of Internet, recommendation system plays a significant role for providing personalised services in our life. However, it also raises serious concerns about privacy since the system collects a lot of personal information. Thus, plenty of schemes have been proposed to address the privacy issues by using cryptographic techniques. However, with the rapidly increasing of users and items, most of existing cryptography-based schemes become inefficient because of the huge computation cost. In this paper, we propose an efficient privacypreserving scheme for recommendation system. Compared with existing schemes, our scheme does not require that friends of user are online during computing predicted rating. Finally, we evaluate the performance of our scheme with the MovieLens 20 m dataset and it shows that our scheme can reduce the overhead of computation and communication.
Keywords: recommendation system; privacy-preserving; homomorphic encryption; proxy re-encryption.
A result correctness verification mechanism for cloud computing based on MapReduce
by Ziao Liu, Tao Jiang, Xiaoling Tao
Abstract: MapReduce is widely applied as a parallel programming model to process massive amounts of data in cloud computing environment. However, in open systems, the workers of MapReduce framework are provided by various administration domains and may be unreliable or malicious. The existing schemes of MapReduce processing model based on multiply duplicate tasks can effectively detect the lazy and non-collusive workers. However, they cannot cope with the vulnerability that malicious workers collude to return incorrect results and thereby undermine the final computation results of users outsourced tasks. In this paper, we present an effective result correctness verification mechanism for MapReduce in public cloud computing environment. By using task duplication and weighted correctness attestation graph, our mechanism can effectively detect both non-collusive and collusive malicious workers in public cloud environments. In order to further improve the detection speed, we introduce a worker selection method based on trust values and consistency relationship. Finally, analysis and experimental results indicate that our mechanism can guarantee higher detection rate with proper additional computation overhead.
Keywords: cloud computing; result correctness; MapReduce; attestation graph.
A new approximate image verification mechanism in cloud computing
by Mengping Yin
Abstract: With the growing prevalence of cloud computing, more and more data especially images and videos are stored in cloud servers. To ensure the security of private data, data owners usually encrypt their private data before outsourcing the data to cloud servers. It is discovered that highly correlated data exist in storage outsourcing and much useful information can be extracted from these correlated data and used for cloud-based services. In the paper, we propose a scheme of encrypted image verification in cloud computing for mobile devices. Many existing schemes focus on verification of query results for outsourced text data or identical images. Different from that, the proposed scheme aims to verify the correctness of query results for similar images. Through the successful query of similar images, power and memory resources of mobile devices can be saved. The security of our scheme is analysed in the random oracle model, and analysis shows that the scheme is secure against adaptive chosen-keyword attack. And what's more, the experimental results demonstrate that our scheme is an efficient one.
Keywords: correctness verification; encrypted image; cloud computing; local sensitive hashing.
Local connectedness over soft rough topological space
by Li Fu, Hua Fu, Zhen Liu, Fei You
Abstract: In this paper, the connectedness of the soft topological space is further discussed, based on the soft connected rough topological space. Continuous mapping is defined in the soft rough topological space, and the property of soft connectedness under continuous mapping is discussed. The connectedness of soft points and local connectedness are defined in the soft connected rough topological space, and the local properties of connectedness are given.
Keywords: soft rough formal context; soft connected rough topological space; soft point; local connected space; connected branch.
Efficient publicly verifiable conjunctive keyword search over encrypted data in cloud computing
by Kai Nie, Yunling Wang
Abstract: Cloud computing has brought appealing features for its users, such as on-demand computing resources, flexible and ubiquitous access and economical cost. Individuals and enterprises are motivated to outsource data to cloud servers. However, the privacy and security of users' data are obstacles preventing application of cloud computing. Searchable encryption is a way to protect sensitive data, while preserving search ability over encrypted data. However, the server may be lazy and return part of results for self-benefit. Therefore, a verification mechanism should be established to guarantee the integrity of search results. In this paper, we present an efficient publicly verifiable conjunctive keyword search scheme. Our scheme ensures the correctness and completeness of search results even if the result is an empty set. Compared with existing keyword search schemes, our scheme is more efficient to verify the search results. Furthermore, we prove that the proposed scheme can achieve the desired security properties.
Keywords: cloud computing; privacy-preserving; keyword search; completeness.
A design methodology for mobile and embedded applications on FPGA-based dynamic reconfigurable hardware
by Darshika G. Perera, Kin Fun Li
Abstract: With the proliferation of mobile and embedded devices, multiple running applications are becoming a necessity on these devices. Apart from optimised hardware-software architectures, state-of-the-art techniques and design methodologies are needed to support complex applications running on mobile and embedded systems. We envision in the near future, many mobile devices will be implemented and delivered on FPGA-based reconfigurable chips. Our previous analysis illustrated that FPGA-based dynamic reconfigurable systems are currently the best option to deliver embedded applications that have stringent requirements. However, computation models and application characteristics also play significant roles in determining whether FPGA-based reconfigurable hardware is indeed a good match for specific applications on a mobile or embedded platform. In addition, there are different methods of reconfiguring the hardware on chip dynamically. Selecting a specific reconfiguration method and designing the corresponding hardware architectures for an application are important and challenging tasks in reconfigurable computing systems. In this work, we propose a design methodology for FPGA-based dynamic reconfigurable hardware that serves as a guideline to the embedded hardware designers in mapping the computation models and characteristics of an application to the most suitable reconfiguration methods. The most common pipelined and parallel (functional) computation models are used as case studies to illustrate the design methodology.
Keywords: design methodology; embedded applications; FPGAs; mobile devices; dynamic reconfigurable hardware.
Development of a charging system for current-controllable batteries based on a multi-stage mechanism
by Hsiung Cheng Lin, Chi-Wei Liu, Jhih-Siang Lin
Abstract: Increasing demand for back-up electric power supply has enforced the development of efficient batteries charger in industry. However, the study of an effective charging management mechanism still needs a further improvement to overcome some limitations of traditional methods. For this reason, this paper proposes a current-controllable batteries charging system using multi-stage charge mechanism for achieving more effective performance. Firstly, a high frequency two-transistor forward converter is designed to provide necessary DC power supply for both charger and control unit. For the charger, it is designed based on an ideal multi-state strategy, and it can provide a desired/changeable constant charging current. Also, each battery is ensured at a balanced level by the proposed battery equilibrium circuit before the charging process. The experimental results confirm that the proposed charging system is superior in term of flexibility, high efficiency, and self-management capability.
Keywords: battery equilibrium; lead-acid batteries; current-controllable; self-management; charger.
A method of crime rate forecast based on wavelet transform and neural network
by Li Mao, Wei Du
Abstract: Accurate prediction of crime is highly challenging. In order to improve the efficiency of situational crime prevention, the temporal distribution of the crime rate within 24 hours is analysed and a forecast model combining Discrete Wavelet Transform and Resilient Back-Propagation Neural Network (DWT-RBPNN) is presented. First, historical crime incidence sequences obtained by the sliding window are decomposed by DWT. Then RBPNN-trained decomposition sequences are used to predict the incidence of future trends and details. Finally, the trends and details are reconstructed to get the final prediction sequence. The experimental results show that the proposed model has relatively high accuracy and feasibility on the crime rate prediction compared with the single method of BPNN. The utility of the DWT-RBPNN model can offer an exciting new horizon to provide crime rate forecasting and early warning in situational crime prevention.
Keywords: crime rate forecasting; sliding window; discrete wavelet transform; neural network; resilient back-propagation.
From real-time design model to RTOS-specific models: a model-driven methodology
by Rania Mzid, Chokri Mraidha, Jean-Philippe Babau, Mohamed Abid
Abstract: The refinement of a Real-Time Operating System (RTOS)-independent real-time design model to a RTOS-specific model is a critical phase in a model-based approach. Model-based approaches allow early verification of the timing properties at the design level. At this phase, if the hardware architecture is supposed to be known, the technological platform (here the RTOS) is not defined. Hence, some assumptions on the platform are implicitly made to achieve timing verification on the one hand and to keep RTOS-independence of the design model on the other hand. However, at the implementation level, these assumptions may be not implementable for the target RTOS. In addition, a difference between the semantic of the software resources used to build the design model and those provided by the RTOS may occur, which may lead to a mismatch between the original design model and the implementation one and affect thus the timing properties. In this paper, we propose a Design Refinement toward Implementation Methodology (DRIM) to address the refinement problem. Having the real-time design model as entry and based on an abstract and a concrete platform models, the methodology firstly evaluates the feasibility of deployment of the given design model on the considered RTOS. When no feasibility problem is raised, the mapping phase generates the appropriate RTOS-specific model. Nevertheless, when the design model is not implementable, the methodology informs the designer about the problem before the effective deployment and guides him for the selection of the appropriate RTOS.
Keywords: design-level verification; real-time operating systems; model-driven approach; design model; RTOS-specific model; UML; MARTE.
A new method of vision-based seat belt detection
by Zhongming Yang, Hui Xiong, Zhaoquan Cai, Peng Yu
Abstract: In the traffic management system, it can greatly improve the management efficiency through the algorithm that monitors and automatically detects whether the driver fastens the seat belt. However, currently prevalent detecting methods cannot achieve satisfactory results in aspects of the detecting rate, the image quality requirement and the colour difference between seat belt and the surrounding environment. In this paper, we propose a method of seat belt detection based on visual positioning. The algorithm locates the window according to the licence plate position and the contour statistics obtained from the gradient. The face detection is used to adjust and determine the seat belt detection area in the window. Finally, the method of seat belt detection based on the connected area is used to detect whether the seat belt is fastened. Experiments show that the successful rate of the proposed method is much higher than other existing methods, and satisfactory results are obtained.
Keywords: seat belt detection; connected components; big data in traffic; structured image data.
Data residency as a service: a secure mechanism for storing data in the cloud
by K. Rajesh Rao, Ashalatha Nayak
Abstract: Recently, researchers have been working on cloud data assurance models to ensure that the data is in compliance with the policies. In such a model, the data is placed in the intended data centres without focusing on data residency issues. However, there are data residency issues, such as undesired data location, multi-jurisdiction laws, extraterritorial access and unauthorised access, that may violate the data privacy and confidentiality. So far, no framework has been developed to address these data residency issues and challenges that combine data security together with the work on compliance. Thus, in order to solve these issues, we have proposed a framework known as the Data Resident Storage (DRS) which is intended to achieve data residency protection, thus providing data residency as a service (DRaaS). DRaaS is a secure mechanism to protect the data in terms of data privacy and confidentiality, which are in compliance with data residency laws. In this paper, the model checking approach and cloud simulation environment are used for verification and validation of DRaaS, respectively. The finite state machine model is developed for the purpose of verifying the methodology in terms of data location and data access, which satisfies the identified specification. The validation is carried out in the CloudSim toolkit with the help of defined services. The developed test automation framework places the data only in data centres that are in compliance with data residency laws. Further, different scenarios are used to execute the experiment manually among end users having different roles, which can be used to validate data location along with data access. Finally, this framework can be hosted by the cloud service provider to provide DRaaS to the end users.
Keywords: data residency; data residency protection; Data residency laws; Data privacy; Data confidentiality.
Efficient public integrity auditing with secure deduplication in cloud computing
by Huixia Huo, Tao Jiang, Shichong Tan, Xiaoling Tao
Abstract: With the rapid development of cloud computing, storing data to cloud servers has become an increasing trend, which promotes integrity auditing and data deduplication to be two hot research topics. Recently, some existing schemes addressed a problem about integrity auditing with deduplication. However, these schemes did not support aggregating authentication tags of different users in the integrity auditing process, which caused a heavy computation cost to the third party auditor (TPA), especially in the batch auditing process. In this paper, we propose an efficient public auditing with secure deduplication scheme using the idea of aggregate signature, which allows the TPA to verify the correctness of integrity proof generated by the cloud service provider with a constant computation cost. Our scheme can also efficiently support batch auditing, whose auditing complexity on the TPA is independent of the number of auditing tasks. Finally, we prove that our scheme is secure and efficient through security analysis and performance evaluation.
Keywords: efficient integrity auditing; secure deduplication; aggregate signature; batch auditing; cloud computing.
A novel Monte Carlo based neural network model for electricity load forecasting
by Binbin Yong, Zijian Xu, Jun Shen, Huaming Chen, Jianqing Wu, Fucun Li, Qingguo Zhou
Abstract: The ongoing rapid growth of electricity use over the past few decades greatly promotes the necessity of accurate electricity load forecasting. However, despite a great number of studies, electricity load forecasting is still an enormous challenge for its complexity. Recently, the developments of machine learning technologies in different research areas have demonstrated its great advantages. General Vector Machine (GVM) is a new machine learning model, which has been proven very effective in time series prediction. In this article, we firstly review the basic concepts and implementation of GVM. Then we apply it in electricity load forecasting, which is based on the electricity load dataset of Queensland, Australia. A detailed comparison with traditional back-propagation (BP) neural network is presented. To improve the load forecasting accuracy, we specially propose to use the weights-fixed method, ReLu activation function, an efficient algorithm for reducing the time and the influence of parameter matrix β to train the GVM model. Analysis of our approach on the historical Queensland electricity load dataset has demonstrated that GVM could achieve better forecasting results, which shows the strong potential of GVM for general electricity load forecasting.
Keywords: electricity demand forecasting; BP neural network; general vector machine.
Parallel fast Fourier transform in SPMD style of CILK
by Tien-Hsiung Weng, Teng-Xian Wang, Meng-Yen Hsieh, Hai Jiang, Jun Shen, Kuan-Ching Li
Abstract: In this paper, we propose a parallel 1-D non-recursive fast Fourier transform (FFT) based on conventional Cooley-Tukeys algorithm written in C and parallelised using CILK in SPMD (Single Program Multiple Data) style. We compare our code with a highly tuned parallel recursive FFT using CILK, which is included in CILK package version 5.4.6, implemented by Matteo Frigo. The experimental results are running on a four dual-core CPUs AMD-OpteronTM 8200. Our newly designed non-recursive FFT code is highly compact, and the experimental results show that the performance of our CILK FFT parallel code on an eight-cores shared-memory machine is competitive.
Keywords: FFT; SPMD; CILK.
A novel colour image watermarking scheme based on Schur decomposition
by Qingtang Su, Lin Su, Gang Wang, Leida Li, Jianting Ning
Abstract: A novel colour image watermarking scheme based on Schur decomposition is proposed in this paper. By analysing the 3
Keywords: Schur decomposition; colour image watermarking; blind extraction.
Reliable routing schemes in 3D network on chip
by Habib Chawki Touati, Fateh Boutekkouk
Abstract: 3D Network on Chip (3D-NoC) is the replacement for traditional infrastructures and the new design paradigm for communication for future very large scale System on Chip (SoC), because it provides flexibility, extensibility and low power consumption. One of the most important issues of a 3D-NoC design is the implementation of an efficient and reliable routing algorithm, which has a direct impact on the overall network performance. A routing algorithm aims predominantly at fulfilling three distinct objectives: deadlock freedom, congestion awareness and fault tolerance, which is a highly desired but somewhat a challenging task. In this paper, a non-exhaustive list of the most relevant routing algorithms in 3D-NoC are surveyed and classified based on their objectives, the advantages and drawbacks of each algorithm are also presented, as well as the possible enhancements to improve their reliability.
Keywords: 3D network on chip; network on chip; reliability; routing algorithms; congestion awareness; fault tolerance; deadlock freedom.
A separable reversible data-hiding scheme in encrypted image for two cloud servers
by Haidong Zhong, Xianyi Chen
Abstract: This paper presents a separable reversible data hiding in encrypted image (SRDH-EI) based on code division multiplexing (CDM) for two cloud servers. In this method, the image owner can encrypt the cover image by using exclusive-or encryption, and two pre-processed images are uploaded to two cloud servers respectively. In these two cloud servers, two encrypted pixels in the same position of uploaded images consist of the embedded vector, and secret bits can be encoded to different spreading sequences based on the principle of CDM. After that, the sequences can be embedded into the vector. That means each one secret bit can be embedded into two encrypted pixels. At the receiver side, the phase of data extraction and image recovery is commutative. There are three cases as follows. (1) If the receiver only has the data-hiding key, the secret bits can be extracted from the embedded vector. (2) If the receiver only has the encryption key, the original image can be recovered. And the decrypted image is same as the original image. (3) If the receiver has the two keys, the original image and the secret bits can be recovered without any error. Different from the classic dual-images RDH method, the proposed method can protect the content security of cover image when the images are outsourced to the cloud server. Compared with current RDH-EI method, the proposed method has a better performance on the visual quality of decrypted image and the embedding rate.
Keywords: reversible data hiding; dual images; encryption key; cloud sever.
An IoT-oriented real-time storage mechanism for massive small files based on Swift
by Dongjie Zhu, Haiwen Du, Yuhua Wang, Xuan Peng
Abstract: In the Internet of Things (IoT), large amounts of small files are generated from various structure sensors in cloud storage platforms. Real-time storage of massive small files will put great pressures on the traditional file system. The impact on IOPS performance for massive small files is considerable. We provide a unique aggregation storage strategy, Sequential Data Aggregation Strategy (SDAS), for storage of small files. We design a two-level index structure to improve writing rate by transfering randomly write to sequentially write. To improve overall data access efficiency and solve the performance bottleneck of proxy node, we use a files potential relevance of timing to prefetch related files that are merged in other blocks. Simulation results show that relative to the original system, SDAS has shorter response time of writing operation, lower cost of index maintenance cost, more balanced node load, and 30% reduction in disk overhead.
Keywords: cloud storage; IoT; massive small files; object storage; Swift.
A farmland-microclimate monitoring system based on the internet of things
by Maoling Yan, Pingzeng Liu, Cezhong Tong, Xiujuan Wang, Fujiang Wen, Changqing Song, Russell Higgs, Gregory M.P. O’Hare
Abstract: Farmland microclimate is a vital environmental factor that affects crop growth and yield formation. With the rapid and mature development of sensor technology and wireless communication technology, the Internet of Things (IoT) is gradually replacing the ineffectiveness of traditional means of environmental monitoring. It brings new approaches and a broader space for further environmental science research. Based on clear perception, reliable transmission and intelligent processing of IoT concepts, a monitoring system for farmland microclimate is developed in this paper. The farmland environmental-monitoring system consists of three layers. The perception layer integrates meteorological, soil, hydrological and other sensors to form a ground-to-air sensor cluster. The transport layer uses the GPRS (a general packet radio service) technology, which covers the entire country for long distance and effectively transfers the collected data to the server in real time. The application layer is developed for receiving PC software and data storage. On this basis, it establishes a platform for big data service, thus implementing the modelling and analysis of farmland microclimate. After three years of system operation, we have done statistical analysis on the length of life, the loss of data and the reliability of data. Results reveal that the system could ensure more than 80% of data integrity, and it can also secure good stability and reliability of the data. At present, this system has been popularised and applied in the granary project area of Bohai and the desert area of China, which provides accurate data support for the local precise agricultural production.
Keywords: internet of things; microclimate; wireless sensor networks; monitoring; precision agriculture.
Visual field movement detection model based on low-resolution images
by Guangli Li, Lei Liu, Tongbo Zhang, Hang Yu, Yue Xu, Shuai Lu
Abstract: In robotic mapping and navigation, simultaneous localisation and mapping (SLAM) is the computational problem of constructing a map of an unknown environment and simultaneously keeping track of an agent's location. The popularity of sweeping robot has made SLAM famous in the last few years, while the recent visual simultaneous localisation and mapping (VSLAM) based on three-dimensional vision makes it more mainstream. To detect the direction and distance of visual field movement, we build a visual field movement detection model on a low-resolution image. Considering the features of image edge and corners, we mainly use the similarity computation of feature points and matching methods in this model to detect the moving direction and distance of vision field. The experimental results show that the proposed detection model is more accurate and efficient in three different conditions, and can precisely figure out where the vision field moves in a short period of time.
Keywords: low-resolution image; visual field movement detection; template matching.
Exploration and application of the value of big data based on data-driven techniques for the hydraulic internet of things
by Yue Qiang, Liu Fusheng, Song Changqing, Liang Jing, Liu Yanmin, Cao Guangsheng
Abstract: The use of big-data technology to screen the massive amounts of hydraulic engineering data in the internet of things is important for its efficient application. This research applies big-data methodology to water management to solve numerous problems, such as the demand diversification of related interest groups, overall water difficulties, and other problems that arise in hydraulic engineering. A historical database that contains a large amount of data and feedback information is used to design an early-warning health model for a reservoir using big-data methods and based on the C5.0 decision-tree algorithm. The health status of Dingdong reservoir is forecast using the model as a case study. The results show that the reservoir is in a healthy state corresponding to no warning level. The early-warning health model is feasible and effective for using abundant case resources, and could be used widely in reservoir health management.
Keywords: big-data methods; early health warning; water resources data; internet of things; decision tree.
A trust and attribute-based access control framework in internet of things
by Junshe Wang, Han Wang, Hongbin Zhang
Abstract: The integration of the Internet of Things (IoT) and cloud computing is the most up-to-date trend of new network technology, which will bring about great changes for future life. With the rapid development of wireless sensor networks and the gradual maturity of cloud computing technology, IoT, which realises information communication from objects to objects, will be widely used in kinds of environment where cloud computing provides superstrong computing support and ultrastrong data storage capacity for thin smart nodes. However, it has become a key problem that each node accesses data under control with the human intention and that data is shared securely in a distributed IoT environment. Moreover, nodes may belong to different security domains in IoT, and data must be accessed only by selected types of user, which can ensure the security of the IoT system. Therefore, an effective access control technique is the key to solve this issue. To address the problems of security, scalability and cross-domain, this paper proposes a fine-grained and dynamic access control model that combines ABAC with a trust mechanism and considers dynamic trust attribute and static multi-attribute as synthetic constraints. The simulation results show the feasibility and effectiveness of the proposed scheme, which has superior characteristics and improves the security of the IoT system.
Keywords: internet of things; attribute-based; trust evaluation; access control.
A branch-and-bound approach to scheduling of data-parallel tasks on multicore architectures
by Yang Liu, Lin Meng, Ittetsu Taniguchi, Hiroyuki Tomiyama
Abstract: This paper studies a task scheduling problem that schedules a set of data-parallel tasks on multiple cores. Unlike most of previous literature where each task is assumed to run on a single core, this work allows individual tasks to run on multiple cores in a data-parallel fashion. Since the scheduling problem is NP-hard, a couple of heuristic algorithms that find near-optimal schedules in a short time were proposed so far. In some cases, however, exactly-optimal schedules are desired, for example, in order to evaluate heuristic algorithms. This paper proposes an exact algorithm to find optimal schedules. The proposed algorithm is based on depth-first branch-and-bound search. In our experiments with up to 100 tasks, the proposed algorithm could successfully find optimal schedules for 135 test cases out of 160 within 12 hours. Even in case where optimal schedules were not found within 12 hours, our algorithm found better schedules than state-of-the-art heuristic algorithms.
Keywords: task scheduling; multicore; data parallelism; branch-and-bound.
Traffic flow combination forecasting method based on improved LSTM and ARIMA
by Boyi Liu, Jieren Cheng
Abstract: Traffic flow forecasting is hot spot research of in intelligent traffic system construction. The existing traffic flow prediction methods have problems such as poor stability, high data requirements, or poor adaptability. In this paper, we define the traffic data time singularity ratio in the dropout module and propose a combination prediction method based on the improved long short-term memory neural network and time series autoregressive integrated moving average model (SDLSTM-ARIMA), which is derived from the Recurrent Neural Networks (RNN) model. It compares the traffic data time singularity with the probability value in the dropout module and combines them at unequal time intervals to achieve an accurate prediction of traffic flow data. Then, we design an adaptive traffic flow embedded system that can adapt to Java, Python and other languages and other interfaces. The experimental results demonstrate that the method based on the SDLSTM-ARIMA model has higher accuracy than the similar method using only autoregressive integrated moving average or autoregressive. Our embedded traffic prediction system integrating computer vision, machine learning and cloud has the advantages such as high accuracy, high reliability and low cost. Therefore, it has a wide application prospect.
Keywords: traffic flow forecasting; LSTM; embedded system; depth learning.
Energy-aware fixed-priority scheduling for periodic tasks with shared resources and I/O devices
by Yiwen Zhang
Abstract: Many researches have focused on energy management for the processor. However, DPM via I/O device scheduling for real time periodic tasks with shared resources has drawn little attention. We address the problem of the I/O device energy consumption minimisation for shared resources. We present an energy-aware I/O devices fixed-priority scheduling algorithm based on RM scheduling. It contains two parts: job scheduling and device scheduling. Job scheduling ensures that each task can complete its execution within its deadline. Device scheduling decides to turn off devices to save energy. The experimental results show that it can achieve significant energy savings.
Keywords: I/O device scheduling; energy management; real time scheduling; embedded systems.
A hybrid optimisation algorithm based on genetic algorithm and ACO algorithm improvements for routing selection in heterogeneous sensor networks
by Mei Wu, Ning Cao, Haihui Wang, Lina Xu, Guofu Li
Abstract: Wireless sensor applications have been pushed to the forefront in last several years mostly owing to the advert of the internet of networks. Using genetic algorithms and ant colony optimisation algorithms, many achievements have been made on engineering design problems, but the results optimised by these methods are often not satisfied by wireless sensor applications. In this paper, GAAC, a hybrid routing algorithm, is designed aiming to the defects of simple genetic algorithms and ant colony algorithms. With improvement, simulation results show that GAAC has great effects on convergence precision.
Keywords: routing; clustering; genetic algorithm; improvement; sensor networks.
Wind weather prediction based on multi-output least squares support vector regression optimised by bat algorithm
by Dingcheng Wang, Yiyi Lu, Beijing Chen, Youzhi Zhao
Abstract: As a kind of clean energy, wind energy is widely disseminated and has been widely studied. Compared with other methods, the support vector machines algorithm is more strictly logical, and the least squares method can improve the training efficiency. Therefore, the method to forecast the wind speed and wind direction using multi-output least squares support vector regression is presented in this paper. The bat algorithm is simple in structure and easy to understand. It has been applied to solve optimisation problems with MSVR in this paper. Compared with single output support vector machines, multi-output support vector machine can enhance the output regression ability. The measured wind speed value is simulated and the prediction model established to predict the wind speed and wind direction. Compared with other optimisation algorithms of MSVR, the simulation results show that the multi-output least squares support vector machines prediction model based on bat optimisation algorithm has better feasibility and effectiveness.
Keywords: wind speed and wind direction forecasting; multi-output least squares support vector regression; bat algorithm.
Partial-duplicate image retrieval using spatial and visual contextual clues
by Wendi Sun, Tao Wang, Zhili Zhou
Abstract: The traditional BOW model quantifies the local features to the visual words to achieve efficient content-based image retrieval. However, since it causes considerable quantisation error and ignores the spatial relationships between visual words, the accuracy of partial-duplicate image retrieval based on BOW model is limited. In order to reduce the quantisation error and improve the discriminability of visual words, many partial-duplicate image retrieval methods have been proposed, which make use of the advantages of the geometric clues between visual words. In this paper, we propose a novel partial-duplicate scheme by using both spatial and visual contextual clues, which not only encodes the relationships of orientation, distance and dominant orientation between the referential visual word and its context, but also takes the colour information between visual words into consideration. The proposed scheme can effectively filter out the false matches and improve the accuracy of partial-duplicate image retrieval. Experimental results reveal that our proposed algorithm achieves performance superior to the state-of-art methods for partial-duplicate image retrieval.
Keywords: partial-duplicate image retrieval; image copy detection; near-duplicate image retrieval; image retrieval; image search; BOW model.
Bi-objective scheduling with cooperating heuristics for embedded real-time systems
by Sonia Sabrina Bendib, Hamoudi Kalla, Salim Kalla
Abstract: This paper proposes a makespan and reliability-based approach, a static scheduling strategy for distributed real-time embedded systems that aims to optimise the makespan and the reliability of an application. This scheduling problem is NP-hard and we rely on a heuristic algorithm to obtain efficiently approximate solutions. Two contributions are outlined. First, a hierarchical cooperation between heuristics ensuring to treat alternatively the objectives, and second, an adaptation module allowing to improve solution exploration by extending the search space. It results a set of compromising solutions offering the designer the possibility to make choices in line with their needs. The method was tested and experimental results are provided.
Keywords: embedded real-time systems; cooperating heuristics; bi-objective scheduling; reliability; Pareto Front.
A novel localised network coding-based overhearing strategy
by Zuoting Ning, Lan He, Dafang Zhang, Kun Xie
Abstract: In recent years, more and more researchers research in wireless overhearing by using network coding, as network coding is a very effective approach to improve network throughput and reduce end-to-end delay. However, the existing approaches cant thoroughly solve the problem of how to deal with newly overheard packets when the buffer is full; meanwhile, the coding node doesnt schedule the packets in the coding queue according to the packets information in the overhearing buffer. Therefore, these methodologies lack of flexibility and demand quite a few assumptions. To address these limitations, we propose a new network coding overhearing strategy which is based on a data packet switching and scheduling (DPSS) algorithm. First, when the overhearing buffer is full and the sink nodes have overheard new packets, the sink nodes will drop the recently overheard packets but record their IDs. Second, sink nodes report the packets information to the coding node that schedules the packets in the coding queue for ease of encoding. Finally, sink nodes delete the packets that have been used for decoding, and call for the ever dropped packets when decoding ratio reaches the threshold. Theoretical analysis and simulation demonstrate that, compared with traditional overhearing policies, our scheme achieves higher coding ratio and less delay.
Keywords: network coding; data packet switching and scheduling; overhearing.
Secure data deletion in cloud storage: a survey
by Minyao Hua, Yinyuan Zhao, Tao Jiang
Abstract: With the rapid development of cloud computing, individual people, firms, industries and governments are moving their data to the cloud to meet the data explosion
challenge. Secure data deletion is becoming a hot issue in cloud storage research. Different from traditional data deletion, the securely deleted data should be non-recoverable. In other words, executing secure data deletion can make the data completely erased. For private cloud storage, the application of traditional secure data deletion solution is relatively easy. However, it becomes challenging for public cloud storage because the cloud users lose the physical control over their data, where the lazy, selfish or malicious cloud storage service providers may not completely delete the requested data. In this paper, we present a survey of current secure data deletion technologies and compare them for both private cloud storage and public cloud storage. For private cloud storage, we introduce the mainstream secure data deletion technologies that can be classified into the physical destruction and the disk replication. For public cloud storage, we analyse the existing secure data deletion methods, such as the secure data deletion based on balanced tree, secure data deletion based on trusted third parties, policy-based secure deletion and so on, in accordance with the two aspects of verifiable secure data deletion and verifiable non-recoverability of data. Finally, we analyse the deficiencies among current researches and propose some future directions for improvement in the application of secure data deletion.
Keywords: cloud storage; secure data deletion; private cloud; public cloud.
A randomized Kaczmarz method based matrix completion algorithm for data collection in wireless sensor networks
by Ying Wang, Guorui Li, Sancheng Peng, Cong Wang, Ying Yuan
Abstract: This paper proposes a novel matrix completion algorithm for data collection in wireless sensor networks through incorporating a randomised version of the Kaczmarz method. By splitting the matrix completion problem into two convex sub-problems and solving the optimal probability computing problem in the randomised Kaczmarz method approximately with the D-Optimal Design solution, we reduce the reconstruction error and accelerate the convergence speed of the matrix completion computation. The synthetic data experiments show that the proposed algorithm presents more accurate reconstruction accuracy and faster reconstruction speed than the state-of-the-art matrix completion algorithms. Furthermore, we verify the practicality of the proposed matrix completion algorithm in real data collection scenario of wireless sensor networks through the experiments based on the real sensed dataset.
Keywords: wireless sensor networks; data collection; matrix completion; randomized Kaczmarz method; optimization; reconstruction.
Design for the external frame of a resonant accelerometer sensor
by Jing Li, Jing Jiang, Yunchan Zhou, Pengfei Guo, Xinze Li, Dongchen Xu
Abstract: In this paper, a model of the resonant accelerometer is studied in order to improve its sensitivity and quality factor. We establish both the mechanical and mathematical models of the accelerometer, and analyse the system energy dissipation. Some conclusions are obtained for decoupling the internal structure and the external frame, which can help to improve the performance of the system. Simulation results show that the choice of the structure parameters can influence the energy dissipation of the system greatly, and all of this can be establish the theoretical basis of designing a high quality resonant accelerometer.
Keywords: resonant accelerometer; external frame; energy dissipation; quality factor.
Multimedia push system based on a wellness slot machine
by Yi-Chun Chang, Jian-Wei Li, Chia-Ching Lin, Fu-Syuan Yang
Abstract: In an aged society, more than 80% of elders who are still healthy or sub-healthy people with self-care abilities, sociability and the society support systems should access mental care, which can be effectuated in a play therapy program. Based on electronic games supplemented by a media push system, the wellness slot machine practised in this study is a play therapy platform through which a preventive care service program for recreational effects and health promotion is planned for elders. For necessary information available to elderly participants of wellness slot machines such as ads for community activities and promotional films naturally and constantly, the multimedia push system in this study is designed and practised in a wellness slot machine with which the media content is pushed to the elderly participants actively without excessive manual operations.
Keywords: BLE beacon; presence service; multimedia; subscribe; publish.
Analysis of Single-hop Routing Protocol Evaluation Models in Wireless Sensor Networks
by Ning Cao, Guofu Li, Hua Yu, Yingying Wang, Mei Wu, Chenjing Gong
Abstract: Wireless sensor networks have been pushed to the forefront recently owing to the advent of the internet of networks. There are some crucial parameters that affect the reliability and lifetime performance of typical applications in wireless sensor networks. In this paper, we analyse the closure relationships among the density, radius, reliability and lifetime, and disclose the trade-off analysis results among them. Then, we implement the single-hop protocol with J-Sim simulation tool. Next, we propose that two intelligent evaluation models can be applied in such situations. Thus, wireless sensor network users can predict the lifetime and reliability directly and simulations will be not necessary. This paper also discusses the disadvantages of this approach.
Keywords: wireless sensor networks; single-hop; evaluation model.
Research on hierarchical trackback technique for individual big data
by Hong Zhang, Bing Guo, Yan Shen, Yun-Cheng Shen, Xu-liang Duan, Xiang-qian Dong
Abstract: In order to solve the privacy protection problem of individual big data, this paper proposes a hierarchical data trackback technique (HDTT). This technique can realise the data trackback through inter-domain and intra-domain path reconstruction without increasing the core network storage load. The main method is as follows: record the AS domain involved by data packets and IP address information with GBF data structure by use of the idle part of the packet header, determine the AS domain first with GBFAS data during the path reconstruction, and then determine the intra-domain router with GBFIP data to complete the data trackback. Finally, through the verification of the data collect treasure platform by project group, the contact ratio between inter-domain and intra-domain paths is up to over 98% and 92%, respectively, so HDTT technique can accurately reconstruct the data flow path, realise the data trackback and achieve the privacy protection of individual big data.
Keywords: individual big data; GBF data structure; IP trackback.
Image fusion based on convolution sparse representation and pulse coupled neural network in non-subsampled contourlet domain
by Linguo Li, Shujing Li
Abstract: In a sparse representation, image data can be described as a linear combination of basis functions. The sparse representation of the image data is sparsely described in units of data blocks, disturbing the continuity between the data blocks and causing coding redundancy and blurring of details. Using a convolutional sparse representation, the image can be sparsely coded in its entirety, and the image sparse coding is performed by replacing the product of the coding coefficient and the dictionary matrix by the convolution sum of the characteristic response and the filter dictionary to achieve an optimised representation of the entire image. In view of the above defects, this paper studies a fusion technique based on convolutional sparse representation and NSCT-PCNN (abbreviated as NSCT-CSR-PCNN fusion algorithm) and uses it in the image processing field. The algorithm uses the alternating direction method of multipliers (ADMM) instead of the orthogonal matching pursuit algorithm (OMP) to perform sparse approximation of the low frequency sub-band so as to obtain the characteristic response coefficients and complete the fusion of low frequency sub-band. Experimental results show that the fusion effect of the NSCT-CSR-PCNN algorithm is better than that of other algorithms, and the advantages of NSCT transform, sparse representation and PCNN model are maintained. The fusion image has good visual effect with clear texture, high discrimination and high contrast.
Keywords: non-subsampled contourlet; convolution sparsity; alternating direction method of multipliers; image fusion.
Research of a reliable constraint algorithm on MIMO signal detection
by Shujing Li, Linguo Li
Abstract: For common nonlinear QR decomposition detection algorithm in MIMO system, this paper proposes the Reliability Constraints QR (RC-QR) algorithm with low complexity. The reliability of soft estimates is judged by shadow area constraint method. Besides, constellation points are introduced as the candidate points. Then the optimal candidate point is chosen from multiple candidate points for feedback. Relative to conventional QR DE composition algorithm, the RC-QR algorithm can significantly improve system interference and greatly reduce error propagation in decision feedback only by increasing very small algorithm complexity. The simulation results show that the performance of our algorithm improves very obviously when the antenna configuration is 4
Keywords: QR decomposition; shadow area constraint; reliability; optimal candidate.
Study on the observability degree of integrated inertial navigation system of autonomous underwater vehicle
by Qi Wang, Changsong Yang, Yuxiang Wang
Abstract: Strapdown Inertial Navigation System (SINS) initial alignment error model is presented under moving base. The state observability during the initial alignment under moving base was thoroughly studied according to the piece-wise constant system method and singular value decomposition method. Simulation experiments were carried out under different vehicle movements with the same integration and different integration with the same vehicle movements. Simulation experiments show that the observability degree of state variables can be improved under linear or rotation movement; furthermore, the outer measurements aided SINS can also improve the observability degree of system state variables. The observability degree increases of state variables improve the accuracy of estimation and the speed of convergence of Kalman filter applied in the integrated inertial navigation system.
Keywords: SINS; initial alignment; observability degree; simulation experiments; integrated inertial navigation system.
Topology control for constructive interference-based data dissemination in WSN
by Xiangmao Chang, Yizhen Chen, Yan Li
Abstract: Data dissemination is a fundamental service in wireless sensor networks. Traditional protocols often suffer severe medium contention problem or incur significant overhead in contention resolution. Constructive interference (CI) enables concurrent transmissions to interfere constructively, so as to enhance network performance. It has been proved that leveraging CI can achieve near-optimal network flooding latency. However, recent studies show that redundant nodes transmitting simultaneously may degrade the dissemination performance besides consume extra energy. Considering the limited energy of sensor nodes, how to choose the appropriate nodes for simultaneous transmission is important for CI-based data dissemination schemes. In this paper, we formulate this problem as a multi-objective combinatorial optimization problem theoretically. Considering the complexity of the problem, we design a distributed greedy node selection algorithm to select suitable nodes which forward simultaneously based on CI in each time slot. The main idea of the algorithm is that, by maintaining RecSet and unRecSet in the two-hop neighbor, each node select suitable nodes from RecSet to transmit simultaneously, such that the maximum number of nodes in unRecSet can receive the packet. Numerical simulations show the efficiency of our proposed algorithm.
Keywords: constructive interference; topology control; data dissemination.
An algorithm for determining data forwarding strategy based on recommended trust value in MANET
by Jianbo Xu, Shu Feng, Wei Liang, Jian Ke, Xiangwei Meng, Danping Shou
Abstract: In Mobile Ad-hoc Networks (MANET) with selfish nodes and malicious nodes, the network performance is seriously affected. We propose an algorithm based on the recommended trust value, i.e. Collaborative Computing Trust Model (CCTM) algorithm, to decide the data forwarding strategy. In the algorithm, the carrier node that carries the message collects recommended data of neighbour nodes, adopts K-Nearest Neighbour (KNN) algorithm principle to filter the false recommended data and select K neighbour nodes as collaborative computing nodes to calculate the recommended trust value of neighbour nodes respectively, and then selects the neighbour node with the highest recommended trust value as the next hop node. The simulation experiments show that when the selfish and malicious nodes number is 10, CCTM is higher than Epidemic and MDT by about 3% and 8%, respectively, in terms of transmission success rate, CCTM is higher than Epidemic by about 14% and lower than MDT by about 15% in terms of average transmission delay; CCTM is lower than MDT by about 3% in terms of routing overhead. Overall, the CCTM algorithm not only has better performance in terms of transmission success rate, delay and routing overhead, but also improves the security of data transmission.
Keywords: MANET; selfish nodes; malicious nodes; trust model.
TBPA: TESLA-based privacy-preserving authentication scheme for vehicular ad hoc networks
by Xincheng Li, Yali Liu, Xinchun Yin
Abstract: Vehicular Ad Hoc Networks (VANETs) aim to strengthen traffic safety and improve traffic efficiency through wireless communication among vehicles and fixed infrastructures. However, to protect the communication from different potential attacks, identity and message authentication is a necessary solution to guarantee security. In this paper, an efficient privacy preserving authentication scheme based on TESLA, called TBPA, is proposed. Bilinear mapping is adopted to produce pseudonyms off-line and realise anonymity. Besides, TBPA extends TESLA protocol and generates mapping values with time-related messages to achieve timely verification, which increases the efficiency of the scheme. Performance analysis shows that our scheme possesses excellent security and privacy properties and can resist different malicious attacks.
Keywords: vehicular ad hoc networks; privacy preserving; authentication; TESLA; anonymity.
Moving vehicle video detection combining ViBe and inter-frame difference
by Wei Sun, Hongji Du, Guangyi Ma, Shunshun Shi, Xiaorui Zhang, Yang Wu
Abstract: Traditional methods are difficult to use to realise real-time and robust detection of moving vehicles under complex traffic scenes. In this paper, a moving vehicle video detection method that combines ViBe and inter-frame difference is proposed. The proposed method improves the background update efficiency of the traditional ViBe method by adding a multi-threshold comparison step to the inter-frame difference method. The improved background update strategy can judge whether the detected pixel point belongs to the foreground or background, and dynamically adjusts the background update rate according to the inter-frame difference results. Experimental results showed the proposed method can effectively remove of ghosting phenomenon occurred in traditional ViBe method and realize accurate and complete detection of the moving vehicle in video.
Keywords: moving vehicle detection; ViBe; inter-frame difference.
Design of dry-type transformer temperature controller based on internet of things
by Yan Leng, Jian Qi, Yepeng Liu, Fujian Zhu
Abstract: Based on the application of the internet of things, we design a dry-type transformer temperature controller. In this design, the traditional dry transformer temperature controller is connected to the cloud, which not only increases the cloud service but also reflects the internet of things. In this paper, a temperature sensor based on the STC89C52 microcontroller is introduced regarding hardware structure, program design, and cloud. It mainly includes the PT100 temperature measurement module, digital-analogue conversion module, alarm module, relay module, a digital tube display module, a communication module, and storage module. The cloud service uses the ONENET cloud platform to store the temperature state data of the transformer. The controller not only realises the traditional dry-type transformer function but also monitors the temperature state of the transformer and shows the fan and alarm's state of the dry transformer temperature controller. The results show that the device is stable and reliable with high accuracy.
Keywords: dry-type transformers; cloud service; temperature measurement; internet of things.
ISIRS: information theory based social influence with recommender system
by Fang Long, Xiaoheng Deng
Abstract: With the explosive growth of information, recommender systems have made great progress during the past ten years. The improvement in accuracy of recommendation has great commercial value. However, the accuracy still has room to improve, and the cold-start problem also restricts the performance of recommender systems. Aiming at optimising these two problems, ISIRS model is proposed. ISIRS integrates social influence into recommendations. Considering celebrity effect in sociology, ISIRS applies information theory to capture the social influence in a social network. As a result, ISIRS can find famous persons in a social network by sorting social influence of all people. ISIRS then makes use of the preferences of these famous people to make recommendations more accurate. The results of experiments show that ISIRS model outperforms the recommendation based on users, the recommendation based on items and the MF recommendation algorithm, even though the rating matrix and trust relationship are sparse. These results prove ISIRS can help both the accuracy and the cold-start problem in recommendations.
Keywords: recommendation; information theory; matrix factorisation; trust network; social network.
The effects of varying levels of mental workload on motor imagery based brain-computer interface
by Bin Gu, Long Chen, Yufeng Ke, Yijie Zhou, Haiqing Yu, Kun Wang, Dong Ming
Abstract: As one of the most applied EEG-based paradigms, motor imagery based brain-computer interface (MI-BCI) is used not only to control external devices, but also to help hemiplegic patients to reconstruct impaired motor function. However, in practical application of MI-BCI, users are often faced with more varied external environments and complex cognitive activities, which could induce a high mental workload. This paper studies the effects of mental workload on motor imagery by designing a parallel task containing required motor and N-back task, taking motor execution as comparison. The experimental results showed that high mental workloads promoted the cognitive-motor process of motor imagery and restrained motor execution. Besides, the classification performance of MI-BCI was evaluated and compared at different mental workload levels between motor imagery and motor idle state. We also verified the possibility of detecting mental workload levels during motor imagery in offline analysis. The paper contributes to a wide range of MI-BCI applications and by exploring the cognitive-motor mechanism in motor imagery and execution.
Keywords: motor imagery; motor execution; mental workload; cognitive-motor; MI-BCI.
Scale-adaptive vehicle tracking based on background information
by Yuzhou Zhao, Wei Sun, Xiaorui Zhang, Yang Wu
Abstract: To solve the problem of low accuracy and poor robustness of vehicle tracking in complex traffic scenes, scale-adaptive vehicle tracking based on background information is therefore proposed to this paper. The traditional correlation filter tracking algorithm is less dependent on background information. This easily leads to tracking errors. We propose establishing the position classifier for the vehicle and surrounding background information as the sample set. It translates the target tracking problem into the classification of the target and the background. This also improves the position accuracy of the tracking target response point when the background is complex. The dimensions of the vehicle change as the relative distance between the vehicle and the camera changes, affecting the tracking reliability. This algorithm crops hog features of the different-scale vehicle images and establishes a scale classifier. It determines the best scale of the target built on the output response peak of the scale classifier. This improves the adaptability of classifier against vehicle scale change. Extensive experimental results demonstrate that the method improves the accuracy and robustness of vehicle tracking significantly.
Keywords: vehicle tracking; correlation filters; background information; position classification; scale classification.
Models and algorithms of the positioning and trajectory stabilisation system with elements of structural analysis for robotic applications
by Sergei Chernyi, Anton Zhilenkov
Abstract: The efficiency of the spacecraft application, and therefore the efficiency of solving the tasks assigned to them, is significantly determined by the functionality of these control systems, their technical and operational features. The control systems with microthruster possess the most extensive functional capabilities, but their essential disadvantage is the consumption of the working fluid, which limits the useful operation time of the spacecraft. The calculation and results of simulation of the magnetic control system for the spacecraft momentum are presented in the paper. The simulation includes an assessment of the reliability of calculating the Earth's magnetic field parameters, as well as an assessment of the quality of object stabilisation by resetting the total momentum with the aid of the system under review.
Keywords: neural models; trajectories planning; adaptive; control; identification; spacecraft; modeling; operating; coordinates;.
Intra- and inter-core power modelling for single-ISA heterogeneous processors
by Krastin Nikov, Jose Nunez-Yanez
Abstract: This research presents a systematic methodology for producing accurate power models for single Instruction Set Architecture (ISA) heterogeneous processors. We use the hardware event counters from the processor Performance Monitoring Unit (PMU) to accurately capture the CPU states and Ordinary Least Squares (OLS), assisted by automated event selection algorithms, to compute the power models. Several estimators for single-thread and multi-thread benchmarks are proposed, which are capable of performing power predictions across different frequency levels for one processor as well as between the heterogeneous processors with less than 3% error. The models are compared with related work showing significant improvement in accuracy and good computational efficiency, which makes them suitable for run-time deployment.
Keywords: big.LITTLE system-on-chip; linear regression; ordinary least squares; hardware performance events; automated event selection.
Automatic melanoma diagnosis framework based on common image feature learning
by Wei Sun, Hui Xu, Xiaorui Zhang, Aiguo Song
Abstract: Automatic melanoma diagnosis based on image processing can give more objective results. To facilitate examination for patients at home, we propose a new automatic melanoma diagnosis framework based on common images. Firstly, we use illumination assessment based on Variational Framework for Retinex (VFR) to filter the images with illumination problem caused by variation of capturing camera's viewpoint and ambient light. Secondly, the GrabCut algorithm based on colour difference is used to segment the lesion area. It can complete segmentation automatically and efficiently. Thirdly, we use Convolutional Neural Network (CNN) to extract high-level features and choose Support Vector Machine (SVM) classifier to complete melanoma classification. Compared with handcrafted features, CNN can acquire deep information of images. Because of the lack of medical images, the SVM classifier is better than other classifiers. Finally, we validated our approach from different perspectives and the accuracy is increased by about 5% over other methods.
Keywords: melanoma classification; illumination assessment; segmentation; CNN; SVM.
Hierarchical bucket tree: an efficient account structure for blockchain-based systems
by Weili Chen, Zibin Zheng, Mingjie Ma, Pinjia He, Yuren Zhou
Abstract: Recently, blockchain technology has been widely studied because of its potential in various decentralised systems, including medical records management, transaction processing system, etc. For example, Bitcoin, a well-known decentralised digital currency, is based on blockchain. However, systems built on top of blockchain are often inefficient. One reason for the inefficiency is that these systems include too many hash operations. To address this problem, we conduct an empirical study on
the transaction history of a real-world blockchain based system (i.e., Ethereum), which contains 300,821 accounts and 14,240,095 transactions. We found that the account usage frequency is highly heterogeneous. Based on this finding, this paper presents Hierarchy Bucket Tree (HBT), an efficient account structure with user transaction behaviour information embedded, to reduce the number of hash operations and thus enhance the efficiency of blockchain-based systems. Extensive experiments have been conducted and the experimental results show that HBT reduces nearly 80% hash operations compared with the existing account structure.
Keywords: blockchain; Ethereum; tree structure; hyperledger.
Home security alarm system for middle-aged people living alone
by Guangyi Ma, Hui Xu, Xijie Zhou, Wei Sun
Abstract: At present, how to ensure middle-aged peoples security has become an urgent social issue to be solved in our country. Because of the development of embedded technology, the smart home products can be used to solve this problem. Although there are many home security products available, the security equipment for middle-aged people is scarce, because the existing security systems are difficult to initialise with fast electrodes consumption. Therefore, we have designed a smart security device suitable for middle-aged and elderly users. We select STM32 and CC2530 as the master controller for the device, which is equipped with camera module, GSM/GPRS module, smoke sensors, flame sensors, and infrared sensors. The camera module is used to capture the live pictures of defence areas, then these pictures will be transmitted to the users by the GSM module. Multiple CPUs can increase the speed of operation, and ZigBee technology for wireless data transmission can reduce the loss of supply. Compared with other security systems, the proposed program optimises the interface to make interaction operation easier for middle-aged and older users. The experimental results show that the proposed system has low power dissipation, convenient operation and high stability, and is very suitable for middle-aged users.
Keywords: home security alarm system; wireless sensor network; embedded system; Zigbee; internet of things.
Container-based task scheduling for edge computing in IoT-cloud environment
using improved HBF optimization algorithm
by Srichandan Sobhanayak, Kavita Jaiswal, Ashok Kumar Turuk, Bibhudatta Sahoo, Bhabendu Kumar Mohanta, Debasish Jena
Abstract: In conventional cloud computing technology, cloud resources are provided centrally by massive data centres. Therefore, edge computing technology has been proposed, where cloud services can be extended to the edge of the network to decrease network congestion. The management of the resources is a major challenge for research. Therefore, in this paper, a task-scheduling algorithm based on hybrid bacteria foraging optimisation is proposed for allocating and executing application tasks. The proposed algorithm aims to minimise the completion time and maximise resource use in the edge network. A rigorous simulation has been done to test performance of the proposed strategy and compared with state-of-the-art algorithms. The proposed strategy shows better performance than the existing work.
Keywords: IoT; cloud; edge computing; container.
Extending the lifetime of NAND flash-based SSD through compacted write
by Hai-Tao Wu, Tian-Ming Yang, Ping Huang, Wen-Kuang Chou
Abstract: In the traditional file system, the partial page write, which only writes part of one flash page, will result in internal fragmentation and write amplification of NAND flash-based SSDs due to the page-aligned characteristic of write. Moreover, recent NAND flash devices have their page sizes larger and larger for the manufacturing reason. Although large page sizes are useful for increasing the flash capacity and throughput, they may decrease both the performance and lifetime of flash storage systems if the partial page writes are frequent. After analysing the various realistic workload traces, we observe that it is a common phenomenon for those heads and tails of large write requests to be partial page writes. This observation makes compacted write possible, which compresses two partial page writes (the head and the tail) from the same large write request into one page before data are written into flash. Therefore, we propose a Compacted Write for page-level FTL scheme, called CWFTL, to extend the lifetime of SSD. Furthermore, a compacted write-aware buffer management scheme is designed to take the advantage of spatial locality in compacted write pages. This scheme makes two relevant partial pages from the same request locate in one flash page. We use extensive event-driven simulations to evaluate CWFTL. The experiment results show that CWFTL really reduces the times of data written to flash and the average read or write response time.
Keywords: SSD; flash translation layer; write amplification; compacted write; partial page write.
Can finger knuckle patterns help strengthen the e-banking security?
by Abdallah Meraoumia, Djamel Samai, Salim Chitroub
Abstract: Communication via the internet has become vital for any kind of information exchange private, public, commercial, or military. Banks are the first that have used the internet for financial transactions (e-banking). However, the safe use of e-banking implies that all precautions have been considered to identify legitimate users and thus avoid economic and social damage that may be caused by any possible fraud. In this context, we propose in this paper a secure biometric system dedicated to e-banking for reducing the fraud risk and strengthening the customer confidence. The fuzzy commitment concept associated with the finger-knuckle-print (FKP) is the core of our proposed system. However, such a system will only be efficient if the FKP features are accurately extracted. For this, we have developed a new method of feature extraction called adaptive extended binary pattern (AELBP). The obtained experimental results have been judged promising for a high security of e-banking with guaranteed trust from costumers.
Keywords: information security; cryptography; fuzzy commitment; biometrics; feature extraction; finger-knuckle-print; FKP; local binary pattern; LBP; data fusion.
Interactive map matching and its visualisation: techniques and system
by Li Cai, Bingyu Zhu, Yifeng Luo, Shuigeng Zhou
Abstract: The trajectory data of taxies is an important kind of traffic data. Many traffic applications need to perform processing and analysis on trajectory data. Visualising trajectory data of vehicles on road maps is an important measure of reflecting and demonstrating the trend of traffic variation, where map matching from trajectory data to road network plays the most crucial role in such a visualisation process. We design and implement a novel interactive visualised map matching system in this paper, namely MMatchingVis, which provides multiple front-end functions including road selection, data extraction, map matching algorithm selection and result display, based on web techniques and Baidu Map. MMatchingVis employs the JStorm platform for trajectory data processing. We evaluate MMatchingVis' map matching results with the trajectory dataset collected from 6,599 taxies in Kunming, and evaluation results show that MMatchingVis could efficiently process and analyse trajectory data, support multiple user interaction models, and provide fine-grained visualisation presentation.
Keywords: visualisation; map matching; GPS trajectories data; user interaction; cloud computing.
IBBO-LSSVM-based network anomaly intrusion detection
by Peng Zhou, Wen-Kuang Chou
Abstract: Owing to the variety and complexity of network intrusion, the traditional network anomaly intrusion detection model cannot accurately classify and identify the abnormal intrusion behaviour of the network, resulting in poor performance when detecting the network anomaly intrusion. In order to improve the performance of network intrusion detection, we propose a novel network anomaly intrusion detection method, by means of IBBO-LSSVM. In this paper, the least squares support vector machine is applied to model and analyse the network abnormal intrusion detection, which can capture the relationship between network anomaly intrusion types and its corresponding features. Then, an improved biogeography-based approach is applied to optimise the parameters of the network intrusion detection model. Finally, the model is simulated and evaluated on a standard network anomaly intrusion test database. The accuracy of the network anomaly intrusion detection for the proposed method is higher than 90%, demonstrating that the proposed approach is superior to the traditional methods.
Keywords: abnormal network behaviour; intrusion detection; modelling and analysis; improved biogeography-based optimisation; IBBO; support vector machine; SVM.
Efficient authentication scheme for vehicular ad-hoc networks with batch verification using bilinear pairings
by Jingsong Cui, Hang Tu
Abstract: The potential for vehicular ad hoc networks (VANETs) in improving traffic, enhancing road safety and reducing traffic accidents has attracted attention from academia, industry, and governments. To ensure secure communication in VANETs, a number of authentication schemes, including those with batch verification, were proposed in recent years. However, studies have demonstrated that most of the existing schemes suffer from bad performance or weak security. To address those problems, we construct a new identity-based digital signature (IBDS) scheme using bilinear pairings. The IBDS scheme is then used to construct a new identity-based conditional privacy-preserving authentication (IBCPPA) scheme for VANETs without the need for a map-to-point hash function or double secret keys. Using simulations, we demonstrate that our provably-secure IBCPPA scheme not only achieves better performance than related schemes, but also overcomes the inefficiency problem of the double secret keys in related schemes (i.e., the system does have to manage two secret keys to provide security).
Keywords: vehicular ad-hoc networks; security; bilinear pairing.
A new efficient privacy-preserving data publish-subscribe scheme
by Ping Chen, Zhiying Wang, Xiaoling Tao
Abstract: Data publish-subscribe is an efficient service for users to share and receive data selectively. Due to the powerful computing resources and storage capacity, the cloud platform is considered as the most appropriate choice to publish and subscribe large-scale data generated in real-world life. However, the cloud platform may be curious about the content of published data and subscribers' interests. In this paper, we aimed at realising a secure and efficient privacy-preserving data publish-subscribe scheme on cloud platforms. On one hand, we adopt ciphertext-policy attribute-based encryption (CPABE) to encrypt the data based on it's access policy. Moreover, part of the decryption computation is shifted to the cloud platform to reduce subscribers' computation overhead. On the other hand, we utilise an efficient searchable encryption scheme based on Bloom Filter tree (BFtree) to protect subscribers' privacy and match their interests with encrypted data. Not only that, publishers and subscribers can also exchange their roles in our scheme. The security analysis and experimental results prove that our scheme is efficient and secure in privacy-preserving data publish-subscribe service.
Keywords: privacy-preserving; data publish-subscribe; CP-ABE; BFtree; cloud platform.
Lattice-based identity-based ring signature without trapdoors
by Yongxuan Sang, Zhongwen Li, Lili Zhang, Hai Jiang, Kuan-Ching Li
Abstract: So far, most ring signature schemes rely on hard number theory problems, such as discrete logarithm, bilinear pairings and so on. Unfortunately, the above underlying number theory problems will be solvable in the post quantum era. Lattice-based cryptography is a hotspot of research recently, due to its implementation simplicity and provable security reductions. When the hash-and-sign signature scheme was constructed based on the hardness of worst-case lattice problems, provably secure lattice-based ring signature schemes were finally constructed. However, the hash-and-sign ring signatures were rather inefficient (with megabytes long signatures). In this paper, we propose an alternative method for constructing lattice-based and identity-based ring signature scheme which does not use the hash-and-sign methodology. In the random oracle model, the proposed signature scheme based on the problem in general lattices is unforgeable and holds anonymity. Compared with the previous instantiations of the hash-and-sign ring signature schemes, the lengths of secret key, public key and signatures in the proposed scheme are much shorter. The signing algorithm is quite simple, with matrix-vector multiplications and rejection samplings.
Keywords: ring signature; lattice; rejection samplings; anonymity; unforgeable.
Special Issue on: ICESC 2014 Electronic System Design and Computational Intelligence
A novel filter algorithm for impulse noise removal from digital images in a library database system
by Yaqin Li, Lan Qiu, Cao Yuan
Abstract: Library database systems are generated from a lot of online images. They are stored in databases that grow massively and become difficult to capture, form, store, manage, share, analysse and visualise via typical database software tools. In this paper, a switching median and morphological filter is presented for removing impulse noise. The noise detector is first adopted to identify noise pixels by combining the morphological gradient based on the erosion and dilation operators with the top-hat transform. Then the detected impulses are removed by the hybrid filter, which combines the improved median filter using only the noise-free pixels with the conditional morphological filter using the improved morphological operations. The results of simulations demonstrate that the proposed filter can realise accurate noise detection, and it has significantly better restoration performance than a number of decision-based filters at the various noise ratios.
Keywords: impulse noise; noise detector; median filter; morphological filter.
Special Issue on: Security for Embedded and Related Systems
Zero-knowledge identification scheme with companion matrices of primitive polynomials
by Huawei Huang, Lunzhi Deng, Yunyun Qu, Chunhua Li
Abstract: This paper proposes the matrix power problem, that is, to find x given C^xD^x, where C and D are the companion matrices of primitive polynomials over finite field. A new zero-knowledge identification scheme based on matrix power problem is proposed. It is perfect zero-knowledge for honest verifiers. Owing to its simplicity, low-memory and low-computation costs, the proposed scheme is suitable for using in computationally limited devices for identification, such as smart cards.
Keywords: finite field; primitive polynomials; companion matrix; discrete logarithm problem; identification scheme.
Special Issue on: Smart X 2016 Pervasive Computing for Smart Life
An improved human physiological simulation model for healthcare applications
by Liang Yu, Nan Jia, Ruomei Wang, Jiao Jiao, Qingzhen Xu
Abstract: Healthcare is becoming more and more important in modern society. In order to prevent some health symptoms happening in daily life, it is important to develop an efficient model to simulate the human physiological performance for predicting and reducing accidents like dehydration, exertional heatstroke, syncope, even sudden death and so on. In this paper, a novel human physiological computer simulation model is introduced. A nonlinear heart rate regulation model and a two-node thermal regulation model are integrated together to simulate the human physiological indices like core temperature, dehydration amount and heart rate. Experiment results show that the proposed physiological simulation model can well simulate the human physiological mechanisms, and some important numerical computation results predict the same trends as the experimental measurements. These simulation results can be used to analyse human physiological symptoms and assist the health risk assessment in the healthcare.
Keywords: computer simulation; human physiological model; healthcare; thermal regulation; embedded systems.
A novel chain-based routing protocol, BranChain, in wireless sensor networks
by Li'e Zi, Wanli Chen, Xingcheng Liu, Xiang Chen
Abstract: In order to solve the deficiencies with the PEGASIS in the inevitability of long link, the overhead of the ineligible cluster head (CH), and the overhead and time cost of chain rebuilding, an improved protocol, the BranChain, is proposed. The proposed algorithm can avoid long links, re-adjust network topology and adopt CH re-election mechanism. Whenever a long link is formed, the node originally connected is supposed to form a new independent branched chain with the greedy algorithm. When all nodes get connected in the chain, the system will connect all the independent branched chains together by searching for the optimal paths between each two of the branched chains. When the sensor nodes die, the two broken branched chains will be connected with the same algorithm as that of the optimal paths searching. Simulation results show that the BranChain, compared with the PEGASIS, can significantly prolong the network lifetime.
Keywords: wireless sensor networks; WSNs; PEGASIS protocol; BranChain; energy efficiency; sensor nodes.
An improved incomplete AP clustering algorithm based on K nearest neighbours
by Zhikui Chen, Yonglin Leng, Yueming Hu
Abstract: With the fast development of internet of things (IoT), a large amount of missing data is produced in the process of data collection and transmission. We call these data incomplete data. Many traditional methods use imputation or discarding strategy to cluster incomplete data. In this paper, we propose an improved incomplete affinity propagation (AP) clustering algorithm based on K nearest neighbours (IAPKNN). IAPKNN firstly partitions the dataset into complete and incomplete dataset, and then clusters the complete data set by AP clustering directly. Secondly, according to the similarity, IAPKNN extends the responsibility and availability matrices to the incomplete dataset. Finally, clustering algorithm is restarted based on the extended matrices. In addition, to address the clustering efficiency of large scale dataset, we give a distributed clustering algorithm scheme. Experiment results demonstrate that IAPKNN is effective in clustering incomplete data directly.
Keywords: incomplete data; affinity propagation clustering; K nearest neighbours; incomplete information system.
A PID-FEC mechanism using cross-layer approach for video transmission over multi-hop wireless networks
by Longzhe Han, Xuecai Bao, Hongying Yu, Huasheng Zhu, Tanghuai Fan, Jia Zhao, Yeonseung Ryu
Abstract: Multi-hop wireless networks (MWNs) provide an important infrastructure for ubiquitous multimedia content access. However, the accumulated packet losses greatly decrease the quality of multimedia services, particularly video streaming services. In order to overcome packet losses in MWNs, in this paper we propose a proportional integral derivative control-based forward error correction (PID-FEC) mechanism to improve the quality of video streaming services. Our proposed method adopts the cross-layer approach, and leverages the functionalities of different network layers. The automatic repeat request (ARQ) on the media access control (MAC) layer is used as an indicator of packet losses. With the packet loss information, the redundancy rates are adaptively regulated based on the PID control algorithm. Experimental results, presented herein, show that the PID-FEC achieves a better quality of video streaming, as well as a higher FEC efficiency, as compared with conventional FEC schemes, over a variety of network conditions.
Keywords: forward error correction; FEC; cross-layer; video streaming; multi-hop wireless networks; MWNs.
An iterative shrinkage threshold method for radar angular super-resolution
by Xin Zhang, Xiaoming Liu, Chang Liu, Zhenyu Na
Abstract: This paper proposes a fast iterative shrinkage threshold (FIST) method for improving radar angular resolution. Based on radar signal processing theory, the implementation of angular super-resolution is equivalent to restoring the radar's target angular information without changing radar's work system. In this method we first establish a convex quadratic programming model by orthogonalising the antenna pattern matrix, which transforms the radar angular super-resolution problem into a constrained optimisation problem. Consequently, the restored angular information can be regarded as the optimal solution of a convex quadratic programming model. Then, the IST algorithm is employed by modifying the residual at each iteration to find this optimal solution of the model. The advantage of this method is to overcome the shortcoming of ringing effect at a low signal to noise ratio (SNR) situation, and the ill-posed problem existing in those classical super-resolution methods is addressed effectively. Simulations further confirm our theoretical discussion, and manifest that a desirable resolution performance is gained and comparisons of signal to restoration error ratio (SRER) provide an amazing result that our method is superior to other methods in terms of efficiency while SNR is less than 20 dB.
Keywords: radar; super-resolution; constrained optimisation; iterative shrinkage threshold; IST.
The power big data-based energy analysis for intelligent community in smart grid
by Yiying Zhang, Kun Liang, Ying Liu, Yeshen He
Abstract: Smart grid deploys large numbers of intelligent terminals, to monitor or control the operating status and improve the energy efficiency and functional applications. In this paper, we study the power efficiency problem of intelligent cell based on the power big data, and present the system architecture and key algorithm for the intelligent community and smart industrial park: 1) we propose a business intelligence architecture based on cloud computing to meet the requirements of efficient storage and analysis of massive data; 2) we also establish a multivariable, multi-dimensional intelligent electricity energy analysis model which improves the orderly power consumption efficiency; 3) also we present a novel parallel algorithm to achieve the data mining algorithms and data analysis algorithms, and solve the issue of processing speed of large-scale data analysis. The analysis results guided the efficient electricity, and increased the functions of the intelligent community and the intelligent life preliminarily.
Keywords: power big data; intelligent community; smart grid; power cloud.
The attack efficiency of PageRank and HITS algorithms on complex networks
by Yangqian Su, Yunfei Yi, Jun Qin
Abstract: With the growing scale of networks, network attack strategies with high attack efficiency and computational efficiency are becoming more and more important. Various attack strategies with various sorting methods have been proposed. However most of them neglected the computational efficiency. Inspired by the high computational efficiency of PageRank and HITS algorithms that used in web pages network, this paper introduces those two algorithms to the network attack separately and explores the feasibility of those two new strategies. The initial experiments choose degree and BC attack as the contrast group, and compare the attack efficiency in six virtual networks. The results indicate that considering the computational efficiency and attack efficiency simultaneously, PageRank strategy has a better attack performance than other compared strategies. Initial discussions about the results are also given.
Keywords: PageRank algorithm; HITS algorithm; attack strategy.
Comprehensive vulnerability assessment and optimisation method of power communication network
by Chenchen Ji, Peng Yu, Wenjing Li, Puyuan Zhao
Abstract: Vulnerability assessment and optimisation for power communication network can enhance the robustness and sustainability of network. However, current vulnerability assessment method lacks service and availability indicator considerations, and corresponding optimisation method ignores dynamic process for temporal factors as well. Aiming at above problems, a novel and comprehensive vulnerability assessment and optimisation method is proposed. Firstly, for vulnerability assessment, the influence factors are analysed from static and dynamic aspects respectively. Integrating these factors, a comprehensive vulnerability indicator is designed to assess the vulnerability of nodes and edges. And then, to relieve unbalanced vulnerability distribution in the network, a routing optimisation method is proposed through reconfiguration for service routes on the edge with high vulnerability. Finally, the simulation is taken under a real network. Vulnerability assessment with the defined indicator is executed, and the network vulnerability can be balanced with the optimisation method, which has effective theoretical and practical significance.
Keywords: power communication network; comprehensive vulnerability; vulnerability balance; routing optimisation.
Special Issue on: Recent Advancements in Internet of Things (IoT) Architecture, Protocols and Services
Research on intelligent obstacle avoidance control method for mobile
robot in multi barrier environment
by Ya Fei Wang, Ming Ma
Abstract: In a multi-obstacle environment, mobile robots can easily collide, so we need to control the obstacle avoidance ability of mobile robots intelligently, so as to achieve flexibility and avoid obstacles. In the process of obstacle avoidance, the current method has low utilisation rate of space. When the distance between the target node and the obstacle is relatively close, there will be a collision obstacle. Based on the minimum risk index, a method of mobile robot obstacle avoidance control in a multi-obstacle environment is proposed. This method means that the path of the mobile robot obstacle environment is segmented according to certain rules. The robot is constrained through the path time constraints according to the requirements of robot obstacle avoidance, calculating motion constraints to obtain necessary and sufficient conditions to satisfy the no collision avoidance relationship. Considering the spatial distance, inertia, and relative movement speed of multiple obstacle environments, the impact danger level of each movement stage of the robot is evaluated, and the result is that the risk index is minimised. The improved repulsive potential function is adopted to overcome the disadvantages of the mobile robot in the multi-obstacle environment where the moving target position is too close to the obstacle, resulting in collision with the obstacle. The global safety path is planned, the intelligent avoidance function is calculated, and intelligent avoidance control is implemented. Simulation results show that our proposed method makes full use of space and is a common method in intelligent obstacle avoidance and timely tracking of moving target.
Keywords: multi-obstacle; mobile robot; intelligence; obstacle avoidance control.
Heuristic approach to minimise the energy consumption of sensors in cloud environment for wireless body area network applications
by P. Kumaresan, M. Prabukumar, S. Subha
Abstract: The wireless sensor networks (WSN) are single user centric, and end users who do not own sensors are unable to have access to any wireless network specific application. The sensor nodes in WSN are highly resource constrained with respect to processing, memory, power, scalability and massive storage for real-time processing. A novel concept based on sensor cloud has been conceptualized to reduce the limitations of conventional WSN. This sensor cloud is a new paradigm to manage the physical sensors that are deployed in any WSN application. Existing research work on sensor cloud is limited to guaranteeing minimal energy consumption. In this paper, a novel mathematical model based on virtual sensor grouping is proposed to minimise the cumulative energy consumption of sensors in cloud environment. The consumption of energy is the energy expense due to transmission, receiving, sensing, and computation. Theoretical analysis with exemplary biological sensors for Body Area Network (BAN) applications was conducted with the proposed model and the results were analysed. Energy consumption of non-virtual sensor with virtual sensor group for two different applications was compared and the results were shown. Furthermore, the proposed sensor cloud infrastructure with power model is compared with traditional WSN with respect to energy efficiency, throughput and performance with quick synchronisation time for random run trials, and results were found to be better than the conventional WSN.
Keywords: physical sensors; virtual sensor group; sensor cloud infrastructure; wireless sensor network; energy consumption; WBAN applications.
Mass internet of things data security exchange model under heterogeneous environment
by Wenbo Fu
Abstract: At present, the data classification based on SOA data exchange method of Internet of Things (IoT) data is not perfect, the effectiveness of data filtering is low, and the security of data exchange is poor. In this paper, the mass data of IoT are classified by a transfer-boost method. The auxiliary training data was used to help source training data and build a reliable classifier to make the classifier more accurate in the test data. Hedge grammar was used to process massive data of heterogeneous IoT. The buffer mechanism was introduced to deal with the unstable data flow in the IoT, so as to enhance the effectiveness of data filtering, and realise the secure data exchange through modules such as server request, identity authentication and receipt of data. Experimental results showed that the proposed model can improve the classification accuracy and data filtering effect, and achieve a more secure data exchange effect.
Keywords: heterogeneous environment; internet of things data; security exchange.
Tracking algorithm of weak disturbance signal under Multi-device interference in the internet of things
by Shuai Yang, Zhihui Zou, Gihong Min
Abstract: It is easy to generate weak disturbance signals under the condition of multi-device interference and hence it is necessary to track and control the signals. There were some errors in the traditional signal tracking methods. However, tracking accuracy was low. Therefore, a new tracking algorithm for weak disturbance signals with multi-device interference in the internet of things is proposed. This is a new method that combines the full differential Thevenin equivalent parameter tracking method and the disturbance signal control method of the internet of things. Before tracking and controlling the weak disturbance signal, the complex wavelet detection method was used to detect the disturbance signal, and then the David superconducting magnetic method was used to track the weak disturbance signal. Experimental results showed that the proposed method can make the system run stably at minimum cost. Besides, the tracking accuracy of the proposed method was about 2.2%, while that of the traditional method was about 0.5%, which indicated that the proposed method can effectively track the weak disturbance signal of the internet of things under the interference of multiple devices, and sets the internet of things in a stable operation state.
Keywords: equipment interference; internet of things; weak disturbance signal; signal tracking.
Special Issue on: ICCIDS 2018 Computational Intelligence in Embedded Systems
A feature selection model for prediction of software defects
by Amit Kumar, Yugal Kumar, Ashima Kukkar
Abstract: Software is a collection of computer programs written in a programming language. Software contains various modules, which make it a complex entity and it can increase the defect probability at the time of development of software modules. In turn, cost and time to develop the software can be increased. Sometimes, these defects can lead to failure of the entire software. This will lead to untimely delivery of the software to the customer. This untimely delivery can responsible for the withdrawal or cancellation of the project. Hence, in this research work, some machine learning algorithms are applied to ensure timely delivery and prediction of defects. Further, several feature selection techniques are also adopted to determine relevant features for defect prediction.
Keywords: software; defect; prediction; classifier; feature selection; cognitive weight.
Attribute-based access control and authentication mechanism using smart cards for cloud-based IoT applications
by Brij Gupta, Megha Quamara
Abstract: With exploding growth in information technology, numerous services and applications with enhanced capabilities are available with the aim to serve users. The Internet of Things (IoT) along with its enabling cutting-edge technologies, is establishing a scenario where these services can be used effectively. However, with a large number of users and applications, it becomes challenging to safeguard the identifying information being transmitted to provide access to these services. This paper presents a refined version of an integrated attribute-based access control and authentication mechanism using smart cards for cloud-based IoT applications. System-wide attributes not only restrict the users to access the remote cloud services, but also ensure user anonymity. We also implement the proposed mechanism on ACPT and AVISPA tool for its validation and to verify its correctness. Moreover, we present an analysis of its security and performance efficiency on the basis of different parameters.
Keywords: attribute; access control; authentication; smart cards; IoT; ACPT.
Feature selection optimisation of software product line using metaheuristic techniques
by Hitesh Yadav, A. Charan Kumari, Rita Chhikara
Abstract: The role of software product line (SPL) is very important in representing the same system with multiple variants. The SPL minimises the use of resources, reduces the cost of the system, and maximises the possibility of achieving the objective. Feature models are used to define SPL. In this paper, genetic algorithm (GA), Hyper-heuristic algorithm and particle swarm optimisation (PSO) have been applied for feature selection optimisation in SPL. The algorithms are compared with each other and their performance and computation time are analysed. The paper examines and evaluates different size feature models, starting from a small set of size 20 to a large set of 1000 features. An improved fitness function is applied for optimisation of features to compare performances of SPL. The objective function is designed by taking reusability and consistency of features (components) into consideration. Furthermore, we used a case study and discuss the SPL in detail. A non-parametric test, i.e. Kruskal-Wallis test, has been performed to analyse performance and computation time of 20 to 1000 features sets and identify core type features. Through extensive experimental analysis, it is observed that PSO outperforms GA and hyper-heuristic algorithm; this means that PSO provide the best feature subset of solutions.
Keywords: genetic algorithm; product line; feature model; particle swarm optimisation; software product line; hyper-heuristic evolutionary algorithm.
Self-adjustive DE and KELM based image watermarking in DCT domain using fuzzy entropy
by Virendra P. Vishwakarma, Varsha Sisaudia
Abstract: With advances in machine learning and development of neural networks that are efficient and accurate, this paper explores the use of kernel extreme learning machine (KELM) to develop a semi-blind watermarking technique for gray-scale images in discrete cosine transform domain. Fuzzy entropy is employed for selection of the blocks where the watermark bits are to be embedded. A dataset formed from these blocks is used to train KELM. The nonlinear regression property of KELM predicts the values where watermark bits are embedded. Self-adjustive Differential Evolution (SeAdDE) controls the strength of the scaling factors finds their optimal values. The adaptiveness of Differential Evolution (DE) helps in self-adjustment and varies the DE parameters to explore best solutions. This saves time as the manual hit and trial method for finding the appropriate parameter values is avoided. The scheme presented shows robustness against various attacks like histogram equalization, resizing, JPEG compression, Weiner filtering, etc., and still also retains the quality of the watermarked image. Thus, the proposed technique can be used as a solution to ensure authenticity via watermarking.
Keywords: image watermarking; KELM; kernel extreme learning machine; self-adjustive DE; fuzzy entropy; gray-scale images; discrete cosine transform.
Special Issue on: CSS 2018 Lightweight Solutions for Cyberspace Security Research Advances and Challenges
Design of an outdoor position certification authority
by Roberto De Prisco, A. De Santis, P. Faruolo, M. Mannetta
Abstract: We present the design of an Outdoor Position Certification Authority (OPCA). Such an authority aims at certifying the geolocalisation of a mobile device equipped with a GNSS (Global Navigation Satellite System) receiver. In general, a GNSS receiver is capable of acquiring radio signals (low-level data) and navigation messages (high-level data) in outdoor environments coming from different constellations of global/regional satellite navigation systems and satellite-based augmentation systems (SBAS). To date, these data are unreliable from a security point of view because they can be easily forged by malicious attackers through specialised spoofing techniques. An OPCA defines a client/server architecture through which a user can certify his position by sending the geolocalisation information needed to verify it to one or more remote servers. Once the truthfulness and reliability of the data received has been verified, the OPCA will issue and send to the client a signed positioning certificate with legal value certifying the position of the user in a given moment. The scenarios for using this service can be multiple and with the advent of the internet of things age devices that might require such a service will grow in number. Some examples are: being able to digitally sign a document remotely only if you are in a certain place; certify that in a given moment a person is or has been in a certain position; certify the delivery of goods of a certain value; certify critical services provided at a specific time and place, such as rescue operations and police operations.
Keywords: certification authority; outdoor positioning; geolocalisation; GNSS; GPS; spoofing.
Decentralised control-based interaction framework for secure data transmission in the Internet of Automated Vehicles
by Brij Gupta, Megha Quamara
Abstract: Recent technological advances in the field of communication and control are transforming the transportation industry by extending the capabilities of conventional human-controlled vehicles to partially or fully automated vehicles. These vehicles create a network, also termed the Internet of Automated Vehicles (IAV), having the capability to sense the data from the surroundings and use it as feedback mechanism in order to assist drivers and the static infrastructure for safe navigation and control. However, a uniform framework is required to isolate the interactions among the vehicles and different entities for secure transmission and control. In this paper, we propose a decentralised control-based interaction framework for promoting smooth transmission of sensor data in automated vehicular system and verify the correctness of the underlying policy model on an Access Control Policy Testing (ACPT) tool. In addition, we present some case studies to show the effectiveness of the proposed framework in real-time applications.
Keywords: automated vehicles; decentralised control; security; policy; provenance.
Special Issue on: CPSCom2018 Human-Centered Cloud/Fog/Edge Computing in Cyber-Physical-Social Systems
Hash-based and privacy-aware movie recommendations in a big data environment
by Tingting Shao, Xuening Chen
Abstract: Movie recommendation is an important activity in peoples daily entertainment. Typically, through analysing the users ever-watched movie list, a movie recommender system can find out the similar friends (or neighbours) of a target user and then recommend appropriate new movies to the target user. However, traditional movie recommendation techniques, e.g., widely adopted Collaborative Filtering (CF), often face the following two problems and challenges. First, as CF is essentially a traversal technique for service recommendations, the recommendation efficiency is often low especially under the circumstance that there is a big volume of users and movies or users ever-watched movie list is updated frequently. This makes it hard to satisfy the quick response demands from potential users. Second, traditional movie recommender systems often assume that the users ever-watched movie list for recommendation decision-makings are centralised, which makes it hard to be applied to the distributed movie recommendation scenarios where the decision-making data for recommendation are multi-source. In view of these challenges, in this paper, we bring forth an efficient and privacy-aware online movie recommendation approach which is based on hashing technique. Through experiments tested on the famous MovieLens dataset, we show that our proposed recommendation approach shows a better performance compared with other approaches in terms of recommendation efficiency and accuracy while users private information is protected.
Keywords: movie recommendation; collaborative filtering; efficiency; privacy preservation; Simhash.
A hybrid model of empirical wavelet transform and extreme learning machine for dissolved oxygen forecasting
by Juan Huan, Weijian Cao, Yuwan Gu, Yilin Qin
Abstract: The accurate prediction of the trend of dissolved oxygen (DO) can reduce the risks to aquaculture, so a combined nonlinear prediction model based on empirical wavelet transform (EWT) and extreme learning machine (ELM) optimised by adaptive disturbance particle swarm optimization (ADPSO) is proposed. First of all, DO series are decomposed into a term of relatively subsequence by EWT, secondly, the decomposed components are reconstructed using the C-C method, and thirdly, an ELM prediction model of every component is established. Finally, the predicted values of DO datasets are calculated by using RBF to reconstruct the forecasting values of all components. This model was tested in the special aquaculture farm in Liyang City, Jiangsu Province. Results indicate that the proposed prediction model of EWT-ELM has better performance than WD-ELM, EMD-ELM, ELM and EWT-BP. The research shows that the combined forecasting model can effectively extract the sequence characteristics, and can provide a basis for decision-making management of water quality, which has certain application value.
Keywords: quality prediction; hybrid model; dissolved oxygen; empirical wavelet transform.
An improved TFIDF algorithm based on a dual parallel adaptive computing model
by Yuwan Gu, Yaru Wang, Juan Huan, Yuqiang Sun, Shoukun Xu
Abstract: A double parallel cloud computing framework based on GPU (Graphics Processing Unit) and MapReduce is proposed. The method aims to improve the low efficiency for large datasets on the stand-alone by text categorisation algorithm, constructs the adaptive computation process of double parallel computing, and combines the advantage of improved TFIDF (term frequencyinverse document frequency) algorithm, and improves TFIDF text categorisation algorithm with double parallel adaptive computing. In different operating environments, the efficiency of improved TFIDF algorithm is compared with different computing nodes. The results show that the improved TFIDF based on dual parallel adaptation has an increase of 6.48% on Macro_F1 compared with the TFIDF based on CPU, and the operating efficiency has increased by nearly seven times. With the number of nodes increasing, the algorithm execution efficiency with double parallel adaptive computing is getting more and more effective.
Keywords: improved TFIDF algorithm; MapReduce; GPU; parallel computation.
A homomorphic range-searching scheme for sensitive data in the internet of things
by Baohua Huang, Sheng Liang, Dongdong Xu, Zhuohao Wan
Abstract: With the popularisation and development of the internet of things, big data and cloud computing, the search of data in cloud-based internet of things has become a hot research topic. However, the sensitive data, such as the medical data collected by wearable devices, is inevitable to be stored in the cloud server. Homomorphic encryption has the ability to calculate the ciphertext without decryption. We separate the calculating and the decryption into different security domains to preserve the privacy of sensitive data, so the original plaintext would not be exposed to the cloud. Hence, we can compare two ciphertexts to get the difference between them in a privacy-preserving way. In order to accelerate the search process of range query, we build an encrypted self-balancing binary index tree. Based on oblivious RAM, the searching scheme can hide the access patterns of the nodes of tree. The actual nodes and logic relation of tree are stored on different servers. A sample implementation of the proposed scheme is given, and the experimental results and analysis are presented to illustrate the scheme's effectiveness and security.
Keywords: internet of things; range search; homomorphic encryption; oblivious random-access memory.
Special Issue on: Recent Advances in Information Security
A dynamic cluster job scheduling optimisation algorithm based on data irreversibility in sensor cloud
by Zeyu Sun, Jun Liu, Xiaofei Xing, Chuanfeng Li, Xiaoyan Pan
Abstract: The optimisation algorithm based on irreversible data for the job scheduling of dynamic cluster is crucial to the improvement of the cluster rendering throughput and the cluster rendering efficiency. However, the dispatching imbalance of the cluster rendering task on the massive number of cluster rendering nodes will prolong the waiting time for the completion of job. Therefore, we propose Dynamic Cluster Job Scheduling Optimization Algorithm Based on Data Irreversibility (DCJS_DI). Firstly, we analyse the job scheduling target. Then, we exploit the frame independence with the clustering rendering and further establish the job scheduling model for the irreversible data dynamic cluster. Next, we elaborate on the job scheduling process of the irreversible data dynamic cluster and the dispatching process of the cluster rendering task. Finally, we investigate via simulation results the impacts of the job hunger and the resource fragmentation issue of the traditional job scheduling strategies on the system performance, the impacts of the multi-progress and multi-threading cluster rendering on the job completion time and the resource efficiency of the irreversible data dynamic cluster. We further study the extension of the cluster computation capability and the reliability issue of the cloud service.
Keywords: sensor cloud; cluster rendering; job scheduling; data irreversibility.
Design and application of real-time network abnormal traffic detection system based on Spark streaming
by Dezhi Han
Abstract: In order to realise the rapid analysis and identification of abnormal traffic in a real-time network, a Distributed Real-time Network Abnormal Traffic Detection System (DRNATDS) was designed, which could effectively analyse abnormal network traffic. DRNATDS provided an effective real-time big data analysis platform and guaranteed network security. The paper proposed K-means algorithm based on relative density and distance ,which integrated with Spark streaming and Kafka. It could effectively detect various network attacks under real-time data stream.The experimental results show that DRNATDS has good high availability and stability. Compared with other algorithms, K-means algorithm based on relative density and distance could more effectively identify abnormal network traffic and improve the recognition rate.
Keywords: Spark streaming; Kafka; network abnormal traffic identification; K-means.
Big data and collective intelligence
by Mirjana Ivanovic, Aleksandra Klasnja Milicevic
Abstract: Nowadays the creation and accumulation of big data is an unavoidable process in a wide range of situations and scenarios. Smart environments and diverse sources of sensors, as well as the content created by humans, contribute to the enormous size and characteristics of big data. To make sense of the data, and to analyse and use these data, more and more efficient algorithms are being developed constantly. Still, the effectiveness of these algorithms depends on the specific nature of the big data: analogue, noisy, implicit, and ambiguous. At the same time, there is the unavoidable scientific area of collective intelligence. It represents the capability of interconnected intelligences to solve concrete problems more collectively and more efficiently than each individual intelligence would be able to do on its own. This paper gives an overview of contemporary achievements in research areas of big data and collective intelligence. At the end, the perspectives and challenges of the common directions of these two areas are discussed.
Keywords: big data; big data generation and processing; cloud computing; collective intelligence; collective intelligence and other artificial intelligence techniques.
DDoS attack detection based on global unbiased search strategy bee colony algorithm and artificial neural network
by Qiuting Tian, Dezhi Han, Zhenxin Du
Abstract: Distributed Denial of Service (DDoS) attacks are one of the common cyber threats today and are difficult to trace and prevent. The DDoS attack detection method for a single artificial neural network has the problems of slow convergence speed and easy to fall into local optimum. A DDoS attack detection method combining global unbiased search strategy bee colony algorithm and artificial neural network is proposed. This method uses the loss function of the artificial neural network as the objective function of the global unbiased search strategy bee colony algorithm. The optimal weights and thresholds are chosen as the initialization parameters of the artificial neural network, in order to avoid the artificial neural network falling into a slow convergence speed and local optimum, thereby realising efficient DDoS attack detection. Experimental results show that the DDoS attack detection method has improved the detection accuracy, convergence speed and has a good generalization ability.
Keywords: artificial neural network; artificial bee colony algorithm; distributed denial of service; DDoS; loss function; convergence speed; attack detection.
Research on profit abilities of order placement strategies in pairs trading
by Luo Suyuan
Abstract: This paper discusses profit abilities of three pairs-trading strategies. When spreads of one pair reach an entry threshold, traders submit limit orders for one lowly liquid stock and market orders for the other highly liquid stock. In order to research on profit abilities, this paper models spreads of that pair of stocks as an OU (Ornstein Uhlenbeck) process and supposes execution time of limit orders as a random variable independent of spreads of the pair. Strategy I is a traditional pairs-trading strategy with market orders. Inversely, Strategy II and III are pairs-trading strategy related with limit orders. Finally, this research finds out that pairs-trading strategies with limit orders can beat pairs-trading strategies with market orders through an empirical experiment with real-world data. The contribution of this paper is to analyse three strategies and verify that strategy III has the best performance when the investment threshold is low. The results of this paper can help investors to make rational investment.
Keywords: pairs-trading; market order; limit order; Ornstein Uhlenbeck process.
Differentially private geospatial data publication based on grid clustering
by Dongni Yang, Songyan Li, Zhaobin Liu, Xinfeng Ye
Abstract: Collecting geospatial data from location-based services can provide location evidence while analysing traffic and other spatial information. However, releasing location data may result in the disclosure of sensitive personal information. The Adaptive Grid (AG) method uses differential privacy to protect information. In AG, the algorithm uses two levels of grids over data domain. Moreover, the granularity of the level-2 grid relates to the noisy count obtained at the level-1 grid. However, it does not take into account of the data distribution. Usually, the number of grids actually divided by the dataset will be very large. It causes noise accumulation and the accuracy will be reduced in response to long- range counting queries. In this paper, the adjacent grid cells with similar data density are clustered together. The proposed method was called Differentially Private geospatial data publication based on Grid Clustering (DPGC). Laplace noise is added to the clusters created by the clustering of the grid cells. The noisy count obtained from the grid cells that form each cluster is evenly redistributed to the grid cells in the cluster. The proposed method adds noise to each cluster instead of each grid cell. As a result, it reduces the error caused by adjacent grid cells with similar data density. Extensive experiments on real-world datasets were carried out. They showed that the query accuracy of the proposed method is higher than the existing methods.
Keywords: differential privacy; grid clustering; big data privacy.
HighPU: a high privacy-utility approach to mining frequent itemsets with differential privacy
by Yabin Wang, Yi Qiao, Zhaobin Liu, Zhiyi Huang
Abstract: In the field of data mining, Frequent Itemset Mining (FIM) is a popular technique for analysing transaction datasets and establishing the foundation of association rules. Publishing frequent itemsets, however, may present privacy challenges. Differential privacy provides strong privacy assurance to users. In this paper, we study the problem of mining frequent itemsets under the rigorous differential privacy model. We propose an approach, called HighPU, which achieves both high data utility and high degree of privacy in FIM. HighPU begins by truncating transactions over the original dataset. Then HighPU directly searches for maximal frequent itemsets. We use a consistent approach to improve the accuracy of the results. Extensive experiments using several real datasets illustrate that HighPU significantly outperforms the current state of the art.
Keywords: differential privacy; frequent itemset mining; top-k itemsets; privacy protection.
Cryptanalysis of the existing integrated PKE and PEKS schemes
by Yang Lu
Abstract: Public key encryption with keyword search (PEKS) is a useful cryptographic primitive that allows one to delegate to an untrusted storage server the capability of searching on publicly encrypted data without impacting the security and privacy of original data. However, owing to lack of data encryption/decryption function, a PEKS scheme cannot be used alone but has to be coupled with a standard public key encryption (PKE) scheme. For this reason, a new cryptographic primitive called integrated PKE and PEKS (PKE/PEKS) was introduced by Baek et al. in 2006, which provides the functions of both PKE and PEKS. So far, several PKE/PEKS schemes have been proposed in the literature. However, none of them considers the keyword guessing attack. The first PKE/PEKS scheme proposed by Baek et al. was shown to be insecure under this attack. In this paper, we analyse the security of other PKE/PEKS schemes. We demonstrate that none of these schemes can resist the keyword guessing attack. The presented attacks show that a malicious storage server can successfully guess the keyword encoded in any keyword trapdoor produced by these schemes. Therefore, it is still an unsolved problem to devise a PKE/PEKS scheme withstanding the keyword guessing attack.
Keywords: public key encryption; keyword search; PKE/PEKS; keyword guessing attack.
A novel HHT and KS statistical approach to detect RoQ attack in wireless sensor network
by Hongsong Chen, Zhongchuan Fu
Abstract: Reduction of Quality (RoQ) attack is a special Denial of Service (DoS) attack. It is a serious threat to the security of Wireless Sensor Networks (WSN). The RoQ attack combines the attack effectiveness and the similarity to normal traffic, so it is difficult to detect by traditional methods. Hilbert-Huang Transform (HHT) time-frequency analysis method can be used to analyse the nonlinear small signal produced by RoQ attack. However, false IMF components are the challenging problems to detect the RoQ attack. The Kolmogorov-Smirnov (KS) test approach is proposed to recognise the false intrinsic mode function (IMF) components. CC2530 System-on-Chip is selected to build the WSN experimental node. Ad Hoc On-demand Distance Vector (AODV) routing protocol and random Routing REQuest (RREQ) flooding attack are simulated to implement RoQ attack in a Zigbee wireless sensor network. Experiment results show that the novel method is effective to detect RoQ attack in Zigbee WSN.
Keywords: HHT; KS test; RoQ attack; wireless sensor network; intrusion detection.
Microcontroller design for security systems: implementation of a microcontroller based on STM32F103 microchip
by Bernard Marie Tabi Fouda, Dezhi Han, Bowen An
Abstract: To discover a concept of solving the security and the stability of the server used in monitoring systems, too much time and effort was spent to design and implement a low-cost performance microcontroller. In the case of the monitoring system, knowing that the server can crash at any time, a microcontroller is required to enable the server to reset after crashing while protecting its data core, and then be able to display in real time the information saved during the crash. This paper encompasses a complete successful designed and tested microcontroller based on STM32F103C8T6 microchip. To design the microcontroller, a Printed Circuit Board (PCB) prototype, peripherals and chips were assembled to reset the server from the relay chip. The connection and data communication between the microcontroller and the server are via Universal Serial Bus (USB) and J-Link connection. The design is also using the AES256 encryption algorithm to protect the data of the system. The simulation results show that after the server crashes, it can easily restart and run, avoiding hassles with insurance of protecting the data core of the system. This approach facilitates and increases the security and the stability effectiveness of the monitoring system.
Keywords: STM32F103C8T6; microcontroller; J-link; universal serial bus; printed circuit board; AES256 encryption algorithm.
Special Issue on: Recent Advances in Information Security’
Research on borrower's credit classification of P2P network loan based on LightGBM algorithm
by Sen Zhang, Yuping Hu, Zhuoyi Tan
Abstract: The credit classification of a borrower is the main method to effectively reduce the credit risk of P2P online loans. In this paper, LightGBM algorithm has the advantage in the high accuracy of data classification. Feature extraction, selection and reconstruction of the original data are performed by feature engineering. The One Hot Encoding technology is used to re-encode the discretised feature indicators. Z-score data normalisation normalises the characteristics of continuous variables. Re-sort all feature indicators by contribution and perform PCA dimensionality reduction, and filter out effective feature indicators for training and testing. Finally, the problem of imbalance of samples and optimisation of model parameters is solved by 10-fold cross-validation. Results of simulation experiments show that the LightGBM model has good stability, good fitting ability and high classification prediction accuracy.
Keywords: P2P network loan; LightGBM; feature engineering; cross-validation; credit classification.
Special Issue on: Security of Mobile and Embedded Corporate Infrastructures in the BYOD Era
Privacy-preserved traffic forecast scheme for intelligent transportation system
by Shuzhen Pan, Yan Kong, Qi Liu
Abstract: Traffic forecast in intelligent transportation system (ITS) takes the responsibility of traffic conductor, traffic control and so on, which is an important part in our daily life. Nowadays, traffic forecast schemes in ITS have been widely studied by researchers in different countries. However, these traffic forecast schemes don't take users' privacy into consideration. Users' privacy information is always leaked to the infrastructures and other users in the same system, and this can cause great damage to the information owner. In this paper, a privacy-preserved traffic forecast scheme is proposed, for ITS, to solve the problem of traffic forecast and privacy leakage together. The proposed scheme is based on a recurrent neural network that is operated by the infrastructures. In addition, the infrastructures or other vehicles can get nothing about the sender's privacy from the data package sent by a target vehicle. Our simulation can prove the advantages of our scheme in terms of the forecast accuracy. The security analysis can prove the privacy-preserved property. At the end of simulation part, we give the detailed analysis on forecast accuracy and the fault tolerance of our traffic forecast scheme.
Keywords: intelligent transportation systems; traffic forecast; privacy preservation.