International Journal of Embedded Systems (108 papers in press)
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
A software control flow checking technique in multi-core processors
by Mohammad Reza Heidari Iman, Pejman Yaghmaie, Yasser Sedaghat
Abstract: Multi-core processors can benefit performance, power consumption and level of parallelism, which is the primary reason why they are employed in safety-critical embedded systems. Nowadays, the use of safety-critical multi-core embedded systems in different industries is growing significantly. In these systems, an error may result in a severe failure that can lead to disaster. In order to prevent such failure, the fault tolerance of the systems should be improved. Some of the important errors, which can cause failures, are the control flow errors changing the execution flow of a multi-core program and eventually leading to core failure. To detect them, different control flow checking techniques have been proposed, almost all of which have so far aimed to detect errors in single-core processors. In this paper, a software control flow checking technique in multi-core processors, called SCFC-MC, has been proposed wherein, in addition to executing the program, each thread monitors the execution flow of another thread, thereby eliminating every single point of failure. Experimental results show that applying SCFC-MC to a quad-core processor (Intel(R) Core(TM) i7-4710HQ) results in the detection of about 95% of control flow errors with less than 20% performance overhead.
Keywords: fault tolerance; multi-core processors; control flow errors; software control flow checking techniques; SCFC-MC technique; safety-critical embedded systems.
Design and implementation of a wearable human activity recognition system based on an inertial measurement unit
by Wei Zhuang, Suyun Xu, Yue Han, Chunming Gao, Jian Su, Dan Yang
Abstract: In recent years, with the rapid development of the inertial measurement unit (IMU) and wireless body area network, and the maturity of pattern recognition theory, the technology of human activity recognition has gradually attracted the attention of researchers, becoming a research hotspot in this field. On the basis of the existing IMU, this paper describes the design of a wearable human activity recognition system, which consists of microprocessor, a three-axis accelerometer, a three-axis gyroscope, power module and so on. The system can provide real-time, continuous human motion information (acceleration and angular velocity information) to the Android control unit, and it can realise the real-time receiving, dynamic display and storage of human motion information.
Keywords: inertial measurement unit; wearable technology; activity recognition; support vector machine.
Design of a hand gesture recognition system based on forearm surface electromyography feedback
by Wei Zhuang, Yi Zhan, Yue Han, Jian Su, Chunming Gao, Dan Yang
Abstract: This paper presents a study of using surface electromyography (SEMG) for hand gesture recognitions. A SEMG acquisition system is discussed in this paper. The characteristics of surface electromyography are introduced and the transformation characteristics are analysed as well. Then the selected gestures and the location for the SEMG sensor are determined. The process of pattern recognition and the reason of selecting SVM classifier are presented in detail, and the kernel function selection of SVM is discussed. Three optimisation methods of parameters are compared using the cross-validation method. Finally, the parameters obtained by the genetic algorithm are used to test the model and the recognition performance.
Keywords: MYO; wearable technology; gesture recognition; support vector machine.
Release and collection method of residual energy for one-dimensional linear-zone internet of things
by Haibo Luo, Zhiqiang Ruan
Abstract: Energy consumption is one of the most important considerations when designing routing and transmission protocols of sensing networks in the internet of things. In order to achieve low and balanced power consumption, an effective method is to refer to the residual energy of nodes. Some existing energy-efficiency routing algorithms are designed according to the residual energy of nodes, but they do not give the way of residual energy collection. In this paper, we design a strategy for releasing and collecting residual energy for one-dimensional linear topology networks. By listening to the broadcast messages and evaluating the residual energy of forward nodes, nodes can dynamically update the residual energy information of all potential forwarders. The collection error is also analysed theoretically. This method can be applied to opportunistic routing and relay node selection algorithms. The simulation results show that our proposed method has very low collection error, and the error will not accumulate with the operation of the network.
Keywords: internet of things; residual energy; one-dimensional linear zone; collection method.
A new upper bound of the completion time of the background task in a foreground-background system
by Amin Asham
Abstract: A foreground-background scheduling system is a simple real-time preemptive scheduler, which is commonly used in uniprocessor embedded systems. In this system, there is a single background task of the lowest priority and multiple foreground tasks have higher priorities. Foreground tasks may have different levels of priorities. Foreground tasks are allowed to preempt the background task. The background task takes a longer time to complete its execution because of the frequent interruptions caused by the foreground tasks. The completion time of the background task is calculated using the processor for the foreground tasks. In this paper, a new upper bound formula of the completion time of the background task is derived. The proposed formula gives a closer upper bound to the exact completion time compared with the existing bounds in the case of few foreground tasks and it even gives the exact time in certain cases for the heavily used systems. In addition, the proposed upper bound is not a recursive formula like that of the existing response time analysis.
Keywords: upper bound; foreground-background; uniprocessor embedded systems; completion time.
Optimal path selection for logistics transportation based on improved ant colony algorithm
by Xiangqian Wang, Huizong Li, Jie Yang, Chaoyu Yang, Haixia Gui
Abstract: The ant colony algorithm, as a heuristic intelligent optimisation algorithm, has succeeded in solving many real-world problems, such as the vehicle routing. However, the traditional ant colony algorithm has suffered from several shortcomings, including premature stagnation and slow convergence. To address these issues, an improved ant colony algorithm is proposed in this paper. The main contribution is to adaptively adjust key parameters during the evolution. Later, the proposed algorithm is validated by addressing the vehicle routing problem. Two real-world datasets are collected from two logistic enterprises separately (i.e., YUNDA and YTO) based in Huainan City, China. Comprehensive experiments have been performed by applying the proposed algorithm to search for the optimal path. Meanwhile, the comparison between the traditional ant colony algorithm and the improved algorithm has been conducted accordingly. Experimental results show that the proposed algorithm achieves better performance in minimising the routing path and reducing the computational cost.
Keywords: ant colony algorithm; optimal path; logistics and transportation; vehicle routing.
A robust error control coding based watermarking algorithm for FPGA IP protection
by Zhenyu Liu, Dafang Zhang, Jing Long
Abstract: The ownership of Intellectual Property (IP) for an integrated circuit (IC) is difficult to identify when the watermarks are attacked and damaged in previous work. To address this issue, we propose a robust Error Control Coding (ECC) based watermarking algorithm for FPGA IP protection. Firstly, the Blakley threshold scheme is used to share the signature of IP user and generate watermarks. The watermarks are then encrypted and finally embedded into FPGA IP design. The signature sharing makes it unnecessary to ensure that all watermarks are reliable in authentication. The complete signature can be retrieved with several watermarks even if other watermarks are damaged. Secondly, the IP owner's image signature is used for copyright identification, and the image is shared by the threshold multi-secret sharing method to solve the resource overhead problem. The image itself is fault-tolerant. Even if the image has a bit error rate of 16.74%, the copyright content can be successfully identified. The fault tolerance of the image has greatly improved the robustness of the watermark. Experiments show that the algorithm not only has low overhead for watermark embedding, but also achieves good results in terms of robustness.
Keywords: IP watermark; error control; FPGA; secret sharing; robustness.
Bio-inspired security analysis for IoT scenarios
by Vincenzo Conti, Andrea Ziggiotto, Mauro Migliardi, Salvatore Vitabile
Abstract: Computer security has recently become more and more important as the dependency of the world's economy on data has kept growing. The complexity of the systems that need to be kept secure calls for new models capable of abstracting the interdependencies among heterogeneous components that cooperate at providing the desired service. A promising approach is attack graph analysis; however, the manual analysis of attack graphs is tedious and error prone. In this paper we propose to apply the metabolic network model to attack graph analysis, using three interacting bio-inspired algorithms: topological analysis, flux balance analysis, and extreme pathway analysis. A developed framework for graph building and simulations as well as an introductory to some IoT scenarios as use cases are also outlined.
Keywords: attack graphs; network security; bio-inspired techniques; IoT.
Towards deterministic FPGA reconfiguration
by João Gabriel Reis, Antônio Augusto Fröhlich
Abstract: Rigid partitioning of components in hardware/software co-design flows can lead to suboptimal choices in embedded systems with dynamic runtime requirements. FPGAs allow systems to cope with such unforeseen conditions by changing portions of hardware dynamically while other parts are still active. Nevertheless, to guarantee a transparent reconfiguration, it is necessary to ensure that it does not disrupt the timing requirements of the running tasks and vice-versa. This work proposes a deterministic FPGA reconfiguration mechanism capable of mitigating the interference generated by I/O operations occurring in parallel. Results show that if the I/O interference is not taken into account and mitigated, the reconfiguration time can grow up to 8800% when peripherals are performing I/O operations through DMA.
Keywords: field-programmable gate array; dynamic reconfiguration; I/O interference.
A sparse system identification algorithm based on fractional order LMS
by Jiaohua Qin
Abstract: System identification is an important research issue for system control, information communication and energy saving in Internet of Things (IoT). Because the system
is usually time-varying, it is challenging to estimate the parameters and trace the system accurately and efficiently. In this paper, we propose an adaptive system identification algorithm based on the fractional order modified least mean square (LMS) algorithm. For the sparse wireless channel, l1-norm of the filter coefficients is considered and zero-attracting correction is introduced into the updating equation. The extension of the algorithm is also proposed for distributed local sensors of information-centric IoT. The convergence speed and MSE performance are investigated with the proposed zero-attracting fractional order LMS algorithm (ZA-FLMS) in the simulations. It is proved that the fractional order, step size and sparsity will take effect to the algorithm performance. The proposed ZA-FLMS algorithm can gain effective improvement for sparse system identification compared with traditional LMS, zero-attracting LMS and fractional order LMS, especially with lower sparsity and smaller fractional order. But the decrement of fractional order and step size will also lead to slower convergence speed, while the bigger fractional order and step size will lead to bigger variation of MSE. Therefore, optimised ZA-FLMS is introduced to further increase the convergence speed, which uses l0-norm in the initial stage of the algorithm and shows faster convergence speed with fractional order as 0.5. The compromise of MSE performance and time cost needs be considered in real systems.
Keywords: fractional order; sparse system identification; LMS.
A genetic algorithm-based tasks scheduling in multicore processors considering energy consumption
by Hassun Vakilian Zand, Mohsen Raji, Hossein Pedram, Hossein Heidari SharifAbadi
Abstract: Energy consumption has been always an important issue in multicore processors, which are getting more and more popular in embedded systems. In this paper, we propose an energy-aware task scheduling approach taking advantage of heuristic algorithms based on a genetic algorithm. The proposed approach includes both static and dynamic scheduling schemes. The task scheduling is modelled as a genetic algorithm problem, which is mainly used when the tasks are ready before run-time; i.e. static task scheduling. The tasks that arrive after beginning task execution are dynamically scheduled using a proposed heuristic algorithm in combination with the genetic algorithm. The experimental results show that the proposed algorithm achieves to more energy efficiency in both static and dynamic task scheduling for multicore processors as compared with similar energy-aware scheduling methods.
Keywords: multicore processor; task scheduling; energy consumption; genetic algorithm.
A new software-defined network architecture to solve energy balance problems in wireless sensor networks
by Yinxiang Qu
Abstract: Wireless sensor networks (WSNs) are an important part of the Internet of Things. WSNs have some of their own characteristics. For example, the sensor battery is limited, and the entire network has a data aggregation node called a sink. When the sensor transmits data to the sink in a multi-hop manner, the sensors around the sink, in addition to transmitting the data information collected by themselves, also help other sensors to forward the data to the sink. This causes the sensors around the sink to die faster than other sensors. This problem is called the energy hole problem. Previous solutions such as sensor uneven distribution schemes and clustering schemes are not only difficult to implement but also increase the design cost of the system. In particular, traditional WSNs cannot be updated once deployed. In order to solve the above problem, we propose a method of combining soft defined networks (SDN) and WSNs. First of all, this paper introduces the relationship between the optimal receiving power and the sensor coverage radius at a given power, and then uses the macro-control function of the controller to dynamically adjust the transmit power and coverage radius of each sensor to extend the lifetime of the network. The simulation results show that the proposed method has a good performance in terms of the number of surviving sensors, network capacity and residual energy.
Keywords: soft defined network; wireless sensor networks; energy hole.
Localisation algorithm based on weighted semi-definite programming
by Jianfeng Lu, Xuanyuan Yang
Abstract: In order to improve the performance of reduced complexity positive semi-definite programming (RCSDP) algorithm based on time difference of arrival (TDOA), a weighted semi-definite programming (WSDP) scheme is proposed in this paper. Based on the squared distance differences between the target node to one anchor node and to the other anchor node, the location of the target node is described as the optimal solution of a non-convex optimisation problem. The semi-definite relaxation technique is used to transform the original non-convex problem into a weighted convex problem, which takes the measurement noise into consideration, and then the estimated location of the target node is obtained. The simulation results show that the localisation performance of the WSDP algorithm is better than that of the RCSDP algorithm, regardless of whether the target node is located inside or outside the area surrounded by anchor nodes.
Keywords: localisation; semi-definite programming; time difference of arrival; weight factor.
An improved method for indoor positioning based on ZigBee technique
by Jiaqi Zhen, Boshen Liu, Yanwei Wang
Abstract: With the popular use of smart mobile terminals, it is possible to locate the mobile terminals. The traditional indoor positioning methods often bring about large errors under the circumstance of interference. In this paper, an improved K-nearest neighbourhood method based on ZigBee wireless sensor network is proposed, it selects K nearest database vectors from the minimum distance, and calculates the average of these coordinates as the final location result. The accuracy of indoor positioning is effectively improved, meanwhile, there is no significant increase of the computing time. The effectiveness of the method has been verified.
Keywords: indoor positioning; ZigBee; K-nearest neighbourhood method; wireless communication.
DOA estimation of wideband sources by sparse recovery based on uniform circle array
by Jiaqi Zhen, Yanwei Wang
Abstract: The conventional direction of arrival (DOA) estimation by compressed sensing is based on wideband focusing and sparse recovery in discrete domain, the former needs high signal to noise ratio (SNR), and the latter requires the space division, or their performance will degrade seriously. Therefore, a new algorithm of wideband DOA caculation in frequency domain based on uniform circle array (UCA) is proposed, the optimisation problems are respectively disposed at each frequency, then the sparse support set is acquired by the corresponding semidefinite programming, after that DOA is calculated through fusing the data of every frequency, simultaneously the original sources can also be reconstructed. This algorithm averts the grid partition in discrete domain, and it also performs well in condition of small samples and low SNR.
Keywords: direction of arrival; wideband sources; sparse recovery; uniform circle array; array signal processing.
Improved Faster R-CNN identification method for containers
by Ning Chen, Xiaohu Ding, Hongyi Zhang
Abstract: In a complex port environment, the fast and effective automatic visual recognition of containers is an important part of the intelligent operation and management of ports. Owing to the large amount of container image data of complex scale and shape, the traditional target detection and recognition algorithm is limited by the illumination, weather and scenes of the port; this has created challenges and difficulties in port container recognition and identification. This paper proposes a deep learning method for container target recognition detection based on the Faster R-CNN framework, the deep separable network structure is introduced into the VGG network, and the DS-VGG network is designed to improve the accuracy while reducing the network parameters to improve the recognition speed. By introducing the Adversarial Spatial Transformer Network (ASTN) to the Faster R-CNN network training to enhance the diversity of data features and improve recognition performance. In order to enhance the convolution feature extraction of container targets, a strategy training network that enhances sample target foreground features, multi-scale training learning and data amplification are used. Finally, the performance test and comparison test of the improved model proposed in this paper are carried out. The test results show that the target recognition speed is 50 frames/s on the container test set, the average accuracy rate is 97.7%, and the recall rate is 94.45%. Compared with Faster R-CNN, the recognition performance is significantly improved in complex scenes such as fog, rain and night.
Keywords: container; port intelligence; deep learning; target recognition; Faster R-CNN network; ASTN.
Design of network intrusion detection system based on parallel DPC clustering algorithm
by Jing Wang, Dezhi Han
Abstract: With the advent of the era of big data, network intrusion detection systems based on K-means algorithm cannot meet the detection efficiency and detection speed requirements in the big data environment. The DPC algorithm can be applied to high-dimensional network traffic and large-scale data application environments, but there are problems of large calculated amounts and limited serial processing capability. Aiming at the problems of the DPC algorithm, the DPC algorithm is ameliorated firstly to improve the clustering accuracy of the algorithm. Then, the DPC algorithm is parallelised on the Spark platform, processing ability and running speed of the DPC algorithm is greatly improved by running in parallel in the memory of multiple virtual machines. The experimental results show that the network intrusion detection system based on the parallel DPC clustering algorithm has higher detection rate and lower false rate. The parallelisation clustering efficiency is much higher than the single-computer clustering efficiency.
Keywords: DPC; clustering; network intrusion detection; Spark platform; parallel.
Selection gate-based networks for semantic relation extraction
by Jun Sun, Yan Li, Yatian Shen
Abstract: Semantic relatedness between context information and entities, which is one of the most easily accessible features, has been proven to be very useful for detecting the semantic relation held in the text segment. However, some methods fail to take into account important information between entities and contexts. How to effectively choose the closest and the most relevant information to the entity in context words in a sentence is an important task. In this paper, we propose selection gate-based networks (SGate-NN) to model the relatedness of an entity word with its context words, and select the relevant parts of contexts to infer the semantic relation toward the entity. We conduct experiments using the SemEval-2010 Task 8 dataset. Extensive experiments and the results demonstrate that the proposed method is effective for relation classification, which can obtain state-of-the-art classification accuracy.
Keywords: relation extraction; selection gate networks; neural networks.
A channel matching scheme for cross-chain
by Wei She, Zhihao Gu, Wei Liu, Jian-sen Chen, Bo Wang, Zhao Tian
Abstract: At present, the cross-chain technology works on becoming a bridge to build trust and transmit the information among chains. However, most of the existing schemes have poor universality, and they also have insufficient protection for the process of information cross-chain transmission. Contraposing the deficiencies above, we propose a channel matching scheme for cross-chain (CMSCC), CMSCC combines the ideas of the relay chain scheme and the channel in Fabric. The relay chain named chain-anchor and relay-chain (CARC) can be connected to other blockchains by multi-blockchain communication and consociation protocol (MBCCP). In CARC, the order peers will match the peers which on different chains and will create the peer matching channel (PMC) among matched peers. So, the information can be transmitted among matched peers. Finally, the experiment verify that CMSCC enables information to be transmitted among chains through PMC, and CMSCC can also protect the process of information cross-chain transmission by PMC simultaneously.
Keywords: blockchain; cross-chain; relay chain; channel; transmission.
Research on electric vehicle charging scheduling algorithms based on a 'fractional knapsack'
by Zhenzhou Wang, Xinyuan Li, Pingping Yu, Ning Cao, Russell Higgs
Abstract: The large-scale disorderly charging of electric vehicles creates challenges for the security of power systems, especially power distribution systems. To avoid peak power consumption during the day and improve the use rate of the power grid at night, a charging scheduling algorithm for electric vehicles based on a 'fractional knapsack' is proposed. Considering the constraints of the users' charging demand and charging system capacity, a charging model based on a fractional knapsack is established to optimise the peak-valley load difference and reduce load fluctuation and charging cost, which is the objective function. To verify the effectiveness of the proposed algorithm, the Monte Carlo method is used to simulate the charging demand of electric vehicles, and the disorderly charging and orderly charging scheduling are simulated and compared under a time-sharing tariff mode. The results show that the proposed scheduling algorithm improves the peak-valley difference of the power grid, reduces fluctuation in the power grid load, and improves the use rate of the power grid.
Keywords: fractional knapsack; electric vehicle; charging scheduling; peak-valley load difference.
Zero-error channel capacity of quantum 5-symbol obfuscation model
by Wenbin Yu
Abstract: The existing noise channel coding methods need to be optimised in terms of channel capacity and algorithm complexity. Based on the characteristics of the quantum 5-symbol confusion-channel model and the theory of matrices, a coding method is proposed that combines the 5-symbol confusion channel with the quantum-superposition-state coding. This coding method uses two isomorphism steps to obtain the zero-error code words, which are the isomorphism between quantum superposition states and vectors and the isomorphism between channels and matrices. The theoretical derivation proves that the channel capacity is able to increase by employing quantum zero-error coding other than its classical counterpart.
Keywords: quantum coding; zero-error channel; channel capacity.
Urban waterlogging monitoring and early warning based on video images
by Fengchang Xue, Juan Tian, Xiaoyi Song, Yan Yan
Abstract: Urban flood disaster causes serious losses to urban residents. Timely access to urban waterlogging conditions has great significance for disaster prevention and disaster relief. Owing to the time resolution limitation of data, the traditional monitoring urban flood disasters using remote sensing imagery cannot realise real-time automatic monitoring and continuous monitoring of key disaster areas. This paper selects road monitoring video, uses image difference operation and SVM (Support Vector Machine) algorithm to identify the waterlogging area, and uses the region growing method to extract the waterlogging area range. The research results show that this method can be used for continuous monitoring and early warning of urban waterlogging in real time.
Keywords: waterlogging monitoring; road monitoring video; SVM; region growing method.
Enterprise internationalisation performance evaluation model based on artificial neural network
by Guojun Yang, Xiaohu Zhou, Zhiyao Laing
Abstract: The internationalisation performance of enterprises is affected by many factors. Designing scientific and effective evaluation index system and conducting comprehensive evaluation are important means to help international enterprises to evaluate their internationalisation performance reasonably. This paper comprehensively reviews the research findings of factors influencing internationalisation performance and indexes in China and abroad, focusing on two quantifiable factors of financial performance and structural proportion, and constructs a hierarchy of internationalisation performance evaluation index system. Secondly, an artificial neural network (ANN) is used for adaptive training to obtain the optimised connection weights. Finally, the data of some enterprises internationalisation performance indexes are collected for empirical research to illustrate the rationality and effectiveness of the method.
Keywords: artificial neural network; internationalisation performance; index system; evaluation; enterprise internationalisation; financial performance; structural proportion.
Attacking the Niederreiter-type cryptosystem based on rank metric
by Chungen Xu, Yingying Zhang, Lin Mei, Lei Xu, Cong Zuo
Abstract: This paper deals with the Niederreiter cryptosystem based on Gabbidulin codes which were solidly broken by Overbeck within polynomial time. In this paper, we first review the conditions under Overbeck's attack applications and then adjust corresponding parameters to target a high security level. Since permutation matrix and the scrambling matrix are used in Gabidulin codes, then the Frobenius matrices have too much structure to be hidden. By analysing the rank of the system matrix, we can find that choosing the matrix such that the dimension of the kernel of the public parity-check matrix is greater than one will achieve a good result. In addition, we also show that bounding the rank of the distortion matrix is to enhance the security of the system. Finally, we give the security analysis of the modified Niederreiter type cryptosystem and demonstrate that it can resist structural and decoding attacks.
Keywords: code; cryptosystem; rank metric; matrix; Niederreiter.
Rainfall runoff prediction via a hybrid model of neighborhood rough set with LSTM
by Xiaoli Li, Guomei Song, Shuailing Zhou, Yujia Yan, Zhenlong Du
Abstract: Accurate rainfall runoff prediction is crucial for flood forecasting and water resources management, and it remains a challenging issue in hydrological information processing. The most challenging problem is that the processing of hydrological information holds the strong locality and nonlinearity, which leads to poor prediction accuracy. Neighborhood rough sets have strong capability on data classification and reduction, which can reduce those redundant rainfall runoff data. Long and short term memory network (LSTM) is a special recurrent neural network (RNN) that is an excellent variant of RNN, it is good at handling the time series data. In the paper, a hybrid model of neighborhood rough sets with LSTM is proposed, which is used for the rainfall runoff prediction. The experimental results show that the presented model could improve the training speed of LSTM and achieve much higher prediction accuracy than the conventional rainfall runoff prediction methods.
Keywords: neighborhood rough sets; attribute reduction; rainfall runoff prediction; LSTM.
Revocable ciphertext-policy attribute-based encryption in data outsourcing systems from lattices
by Xixi Yan, Chaochao Yang, Qichao Zhang, Jinxia Yu
Abstract: Attribute-based encryption mechanism is widely used in outsourcing environment because of its characteristics of 'one-to-many' communication. However, users attributes often change dynamically. In order to solve the problem of attribute revocation in the attribute-based encryption scheme in outsourcing systems, a revocable ciphertext-policy attribute-based encryption in data outsourcing systems from lattices is proposed. The scheme uses the LWE problem to construct the encryption and decryption algorithm, which can resist the quantum attack. Tree-access structure is adopted to realise flexible fine-grained access strategy. In addition, with the help of the data outsourcing management server, the attribute key and cipher-text are updated to achieve immediate attribute revocation. The scheme is proved to be secure under the selective attribute and selective plaintext attack. The comparative analysis shows that the scheme has a significant improvement in performance, and it supports immediate attribute revocation, which is more suitable for the dynamic change of users in the outsourcing systems, such as social network platform.
Keywords: lattices; attribute-based encryption; attribute revocation; data outsourcing system.
A decision tree algorithm for forest fire prediction based on wireless sensor networks
by Demin Gao, Jie Xin
Abstract: Forest fire poses a significant threat not only to the natural environment and ecological systems but also to the safety of human life and property. Combined with new technologies, a decision tree algorithm is proposed for forest fire prediction, in which wireless sensor networks technology is used to transmit data and predict the ignition of the forest. There are four meteorological parameters as part of training data, containing temperature, relative humidity, wind speed, and daily precipitation, while the other part is prediction results of forest weather index system. The decision tree generated by our system could classify these parameters from the most significant to the least significant and so can better foretell fire occurrence. The analysis of prediction results shows that our system is effective.
Keywords: decision tree algorithm; forest fire prediction; wireless sensor networks.
Based on the GF1 and GF4 radiation calibration analysis
by Shao Wen, Zui Tao, Xie Yong, John J.Qucurrently, Huan Hai, ChuanYang Tian
Abstract: In order to test the in-orbit radiation characteristics of GF-1 and GF-4, and obtain accurate radiometric calibration coefficients, this paper proposes a MODIS and LandSat8 cross-calibration method based on Dunhuang radiation correction field and a method for comparing the radiance error of GF-1 and GF-4 satellite images based on uniform regions. First, this article introduces the cross-calibration principle and the overview of the uniform target sample area, and compares the band matching and spectral response of the four remote sensors. Then, it compares cross-calibration based on the scenes of the same area at the same time of GF-1, GF-4, MODIS, and LandSat8 on July 28, 2017. The results show that the calibration coefficient error obtained by this method is within 6%. The error of the calibration coefficient obtained by cross-calibration with LandSat8 satellite is smaller, which satisfies the basic remote sensing quantitative demand. The GF-1 satellite has high calibration accuracy and is suitable for cross-calibration research of China's independent satellite remote sensors. It is of great significance for the establishment of China's on-orbit calibration system.
Keywords: cross calibration; Dunhuang radiation correction field; GF-1; MODIS; GF-4; LandSat8; visible and near infrared.
Enhanced parallel CFAR architecture with sharing resources using FPGA
by Sadok Msadaa, Youness Lahbib, Ridha Djemal, Abdelkader Mami
Abstract: The real time CFAR processor needs a very high computational performance. To meet the real-time requirements, this paper presents an implementation of a new hardware parallel design using ACOSD-CFAR detector. The aim of this work is to increase the architecture throughput and decrease the power consumption while maintaining a high resolution target detection. Our proposed implementation exploits the properties of the ACOSD-CFAR detector to enhance it with a parallel architecture, including some sharing resources. Compared with conventional implementation of CFAR, the proposed architecture increases the throughput from 2576 Mbit/s to 4736 Mbit/s by 184% and reduces the power consumption by 15%. The design is implemented on a Zync 7000 FPGA board, which is considered as a common validation platform.
Keywords: CFAR; VHDL; radar; parallel; FPGA; ACOSD; radar detector; radar implantation; enhanced; radar architecture; sharing resource.
Low power transistor level synthesis of finite state machines using a novel dual gating technique
by Abhishek Nag, Subhajit Das, Sambhu Nath Pradhan
Abstract: In this work, an efficient technique of clock and power gating is concurrently introduced in Finite State Machines (FSM) with a view to minimising the overall power dissipation. The proposed power gating concept works on the principle of shutting down the power supply to the FSM during periods of inactivity. The extraction of the inactivity criteria is based on the occurrence of self-loops within the FSM or an unchanged FSM output between successive clock pulses. Clock gating, on the other hand, disables the clock signal to the sequential blocks of the FSM during this inactive/idle periods. The idea of implementing the gating in both the state logic as well as output logic is introduced in this work. The proposed approach has been introduced in three benchmark FSM circuits, simulated and synthesised in CADENCE digital design tool. The results indicate a maximum of 73% total power savings (dynamic and static) with an average penalty of 27% area (approx.).
Keywords: clock gating; power gating; finite state machine; self-loops.
A combinational convolutional neural network of double subnets for food-ingredient recognition
by Lili Pan, Cong Li
Abstract: Deep Convolutional Neural Networks (DCNNs) have become the dominant machine learning technique for visual object recognition. They have been widely used in food image recognition and have achieved excellent performance. However, not only are the food-ingredient datasets not easy to obtain, but also the scale is not big enough to learn a deep learning model. For small-scale datasets, this paper proposes a novel DCNN architecture, which constructs an up-to-date Combinational Convolutional Neural Network of Double Subnets (CBDNet) for automatic classification of food-ingredients using feature fusion. The feature fusion is a component which aggregates subnets for more abundant and precise deep feature extraction. In order to improve classification accuracy, some useful strategies are adopted, including Batch Normalisation (BN) operation and hyperparameters setting. Finally, experimental results show that the CBDNet integrating double subnets, feature fusion and BN operation extracts better image features and effectively improves the performance of food-ingredient recognition.
Keywords: food-ingredient recognition; deep feature; deep learning; deep convolutional neural network.
Comparative analysis on detection performance with ground-based microwave radiometer and radiosonde
by Lina Wang
Abstract: In order to investigate the reliability of detection data, this article compares and analyses the meteorological elements retrieved from the microwave radiometer (MWR) and observational data from radiosonde. The results show that temperature profile and water vapour density profile from MWR have better positive correlation with those from radiosonde. The temperature retrieved from MWR is lower than that from radiosonde in non-precipitation conditions, but higher in precipitation conditions. The bias may be related to sampling methods, MWR retrieval methods, precipitation and so on.
Keywords: comparative analysis; microwave radiometer; radiosonde; temperature; water vapor density ; correlation.
Using improved RFM model to classify consumers in big data environment
by Guang Sun, Xiaofeng Xie, Jiayibei Zeng, Wangdong Jiang, Yuxuan Huang, Meisi Lin
Abstract: Big data makes the marketing focus of enterprises change from products to consumers, so customer relationship management (CRM) becomes a central issue for business operation. Because customer classification is the key question for CRM, this paper starts with RFM model, combines analysis of K-means clustering, and studies the method for distinguishing between valueless customers and high-value customers. Based on this method, specific management strategies are proposed to help enterprises find core consumers. Also, quantitative analysis of the validity of the cluster is done by using the elbow method. Results of the experiment show that establishing RFM index and using K-means clustering can start from the structure of dataset of consumers of enterprises and finally compare the difference among customer classification by using the clustered scatter plot to provide an effective way of classifying consumers.
Keywords: RFM model; customer segmentation; big data; cluster analysis.
Time-aware parallel collaborative filtering movie recommendation based on Spark
by Dan Yang
Abstract: Most of the traditional collaborative filtering (CF) recommendation models mainly consider the users' ratings on items, often ignoring the time context information of users. However, this information is non-trivial to improve the effectiveness of the recommender system. A time-aware parallel CF movie recommendation based on Spark is proposed in this paper. The CF algorithm based on matrix factorisation can associate users' interests with items through implicit features and solve the sparse matrix problem. The time-aware CF algorithm considers the dynamic features associated with the items and users, which improves the recommendation accuracy by introducing discrete time parameter into the matrix factorisation model. To solve the problem of the slow processing speed of high volume data, distributed computing based on Spark is used to achieve the parallelisation of the algorithm. The experimental results on the real dataset MovieLens show that the proposed method performs significantly better than traditional CF recommendation, which can alleviate the problem of data sparsity and significantly improve the processing speed and recommendation accuracy.
Keywords: time-aware; collaborative filtering recommendation algorithm; matrix factorisation; Spark.
Factor analysis and evaluation of Chinas higher education development in big data era
by Hangjun Zhou, Xingxing Zhou, Jing Liu, Kezhuo Chen
Abstract: Big data has become an indispensable part of higher education development, and most of the related data in higher education of China can be applied to evaluation and analysis. However, currently there is a lack of research on the development of higher education using the latest China Statistical Yearbook data. In this paper, based on the latest data, by using SPSS to analyse and evaluate the development of higher education, the new issues can be discovered and initial reliability recommendations can be proposed. Therefore, we will have more profound and unique insights and applications in the practice of the big data era, and will further provide evidence and clues to better support and develop China's higher education.
Keywords: Higher Education; Data Analysis; SPSS; Big Data.
Efficiency and safety assessment of suburban highway access management
by KeJun Long, Nuo Xu, Ling-yun Xiang, Xi Duan
Abstract: Congestion and traffic accidents often take place at the entrances and exits of highway. However, reasonable access management and design can greatly mitigate congestion and accident. This paper discusses the field of suburban highway access management by introducing three typical suburban highways access management models, including the two-way stop-controlled intersection, simplified Restricted Crossing U-Turn (RCUT), and interchange with right-in right-out. To ensure better efficiency and safety, the simulations were conducted in VISSIM and SSAM (Surrogate Safety Assessment Model) software. The travel time, delay, and throughput are used as the efficiency indexes, and the traffic conflicts are used as safety indexes to quantify the access management performance. The results show that the total delay at an interchange with right-in right-out decreased by 40.5%, and the conflict decreased by 50.0% than a two-way stop-controlled intersection design, as well as the total delay decreased by 30.7% and the conflict decreased by 67.4% than a simplified RCUT design.
Keywords: access management; traffic simulation; surrogate safety assessment model.
A software-based calibration approach to increase the robustness of embedded systems
by Md Al Maruf, Akramul Azim
Abstract: Embedded systems often interact with dynamic environments requiring not only to meet deadlines but also to achieve a certain level of accuracy. Since the inaccuracy of a task output produces a similar adverse effect such as timing violation, we propose a software-based calibration approach to increase the robustness of embedded systems by monitoring and comparing system component's output accuracy with a calibration standard to take actions for addressing any inaccuracy. The calibration standard is derived from a representative component's output with known high accuracy. As an example, we analyse the accuracy of a component that performs dynamic voltage and frequency scaling (DVFS) and explains the associated timing effects in terms of task schedulability. We also perform experiments on LITMUS-RT kernel to demonstrate the need and applicability of our calibration approach in the domain of embedded systems.
Keywords: embedded systems; task scheduling; monitoring; calibration; accuracy.
Energy-aware automatic tuning on many-core platform via adaptive evolution
by Chen Liu, Zhiliu Yang, Yijun Jiang
Abstract: Even though attaining high performance has been the users pursuit traditionally, in thernmany-core era the emphasis has shifted towards controlling the power and energy consumption so as to maintain a satisfying performance while consuming an acceptable amount of energy. This applies to both high performance and mobile computing platforms. To achieve this goal,We propose evolution algorithm based automatic tuning as one feasible solution for energy-aware computing on many-core microprocessors. In this paper, we presented several auto-tuning approaches employing Differential Evolution (DE) algorithms and Genetic Algorithm (GA). Our target is to approach the optimal setting of different power islands on a many-core platform as fast as possible when running multiple programs. Comparing with brutal-force approaches, our solution has the advantage of fast converging speed without the need to traverse the entire search space, and runtime tuning without a priori knowledge of the software workload. Our experimental results show that, AdaptivernDifferential Evolution algorithm is able to achieve reduced energy consumption as well as better energy delay product (EDP) than other representative algorithms that we examined. Based on the results we obtained, we believe adaptive evolution based auto-tuning approach is an effective method towards energy-efficient computing on many-core platforms.
Keywords: differential evolution algorithm; genetic algorithm; energy-aware computing; dynamic voltage and frequency scaling; many-core processors.
A review of regional distributed energy system planning and design
by Junjie Wang
Abstract: With the development of urbanisation in China, regional distributed energy plays a vital role in modern urban power systems, which can reduce emissions, reduce power consumption, and increase the safety and reliability of power grids. This paper focuses on three main aspects of regional distributed energy system planning and design: load forecasting, system optimisation and system evaluation. It summarises the types and research methods of each aspect and proposes the future development direction. Finally, the research framework of system planning and design is proposed.
Keywords: regional distributed energy system; load forecasting; system optimisation; system evaluation.
Real-time anomaly detection in gas sensor streaming data
by Haibo Wu, Shiliang Shi
Abstract: In order to improve the timeliness and accuracy of coal mine gas disaster risk assessments, it is important to detect anomalies in the gas sensor streaming data in real-time. In this paper, the support vector regression (SVR) algorithm combined with the normal statistical distribution technique is used to establish a real-time anomaly detection model for gas sensor streaming data. The model uses SVR to fit the nonlinear mapping relationship between the multi-dimensional monitoring data of the coal mine working face and the gas sensor streaming data to predict the gas sensor value. For the distance between the predicted and the measured gas sensor values, the normal distribution method is used to determine gas sensor anomalies in real-time. Furthermore, this paper presents a prototype system for the real-time anomaly detection in gas sensor streaming data that is built using the memory-based distributed stream processing framework Spark Streaming. Experiments show that the real-time anomaly detection system combined with the SVR algorithm and the normal statistical distribution can periodically update the anomaly detection model and determine anomalies in the sensor streaming data in real-time. For a window size of 9, an update cycle of 1 and an anomaly threshold of 0.95, the anomaly detection model is better than the boxplot, the statistical analysis and the clustering algorithm regarding the prediction precision and accuracy. Moreover, the presented approach improves the detection efficiency.
Keywords: gas sensor; anomaly detection; streaming data; SVR; Spark Streaming.
Research on urban transport network topology vulnerability identification under rainfall conditions
by Weiwei Liu, Yang Tang, Fei Yang, Yi Dou, Jin Wang
Abstract: Under the impacts of global climate change, meteorological disasters such as rainstorms occur frequently in recent years and have led to several severe disruptions of transport networks in urban areas. In order to minimise the risk of potential losses of life and property, this paper develops a method to identify topology vulnerability of a road network for a range of rainfall scenarios. The paper reviews the theories and methods of vulnerability analysis and defines the concept of vulnerability of a road network by considering the intensity of rainfall. The paper establishes a comprehensive assessment index importance of nodes and edges by combining the betweenness and traffic flow to find out the source of the fragility of a road network by considering the intensity of rainfall. Then, the assessment index mincuts frequency vector is introduced into the assessment of topology vulnerability. This paper provides the scientific basis for disaster control and reduction of the risk to urban road networks.
Keywords: urban traffic; topology vulnerability; road network; rainfall; mincuts frequency vector.
RFID aided SINS integrated navigation system for lane applications
by Qi Wang, Changsong Yang, Shaoen Wu
Abstract: To improve the lane vehicle position accuracy, RFID technology is applied to correct the position of the SINS irregularly with label positioning. The acceleration data of the vehicle in three directions is measured by the accelerometers of the inertial measurement unit; the attitude matrix is updated in real time using the angular velocity of the gyroscope output space, and the acceleration component is transformed into the geographic coordinate system, and the acceleration of the inertial measurement unit. The data is subjected to an integral operation process to obtain a spatial displacement value of the vehicle. The real-time updating algorithm of the attitude matrix and the processing of the inertial measurement unit signal are presented. The quaternion-based algorithm is used to solve the attitude matrix as well as updating the coordinate system of the inertial navigation attitude matrix in real time. The Hilbert-Huang transform is used to filter the acceleration signal to solve the integrator saturation problem caused by the low-frequency component of the acceleration signal. The EMD algorithm based on the continuous root mean square error is applied in rejecting the low-frequency components in the signal. The simulation experiments show that the system is reliable and has high precision.
Keywords: RFID; SINS; attitude matrix; simulation experiments.
Application of vision-aided strapdown integrated navigation in lane vehicles
by Qi Wang, Changsong Yang, Shaoen Wu
Abstract: The application of GNSS is very extensive in lane applications with the development of science and technology. Vision-aided strapdown integrated navigation is an effective aided-navigation method in the case of GNSS failure in a lane vehicle, which plays an important role in realising high-precision navigation of the lane navigation system. A vision-aided navigation system based on GNSS positioning is constructed using the electrical powered platform as the research object. The design idea and hardware platform of vision navigation system is presented in the paper. Digital image processing technology is used to segment the collected lane image. The image pre-processing operation, including de-noising filtering and grayscale processing, is carried out to complete the segmentation and get the effective navigation area. According to the effective area of navigation, a navigation datum line is extracted by the least squares linear fitting and Hough transform. According to the camera imaging model and the camera's internal and external parameters, the navigation datum line in the image coordinates is transformed into the world coordinates, and the heading angle is calculated. The Kalman filter algorithm is used to fuse the navigation parameters of the vision navigation module and the GNSS positioning module, and the integrated navigation model is established.
Keywords: lane vehicle; vision-aided strapdown integrated navigation; image segmentation; navigation line detection; Kalman filter.
Research on fuzzy clustering method for working status of mineral flotation process
by Yanpeng Wu, Xiaoqi Peng, Nur Mohammad, Hengfu Yang
Abstract: A fuzzy clustering method based on FCM algorithm for the working status of the mineral flotation process is proposed to help workers control the mineral flotation process better. The working status of the mineral floatation process can be determined by judging dosing records, in terms of copper sulphate, lead nitrate, xanthate, 2# oil, black medicine, etc., or by observing the visual features of the foam layer including the average grayscale, R-means, G-means, B-means, the average bubble size, the skewness of bubble size, the standard deviation of bubble size and bubble stability. Nearly 6000 continuous dosing data were collected from an automatic dosing system installed in a gold mine flotation workshop and normalised to the range of [0, 1]. Those dosing data were first pre-classified into steady condition and unsteady condition according to the degree of change in neighbouring data and then clustered respectively. Ninety-six categories of steady condition and 24 categories of unsteady condition were obtained by an FCM program using Euclidean distance. Similarity coefficient analysis on mean, standard deviation, and variation coefficient indicate that the dosing clusters in steady condition are more trustworthy to dosing operators than those in unsteady condition. Meanwhile, FCM algorithm is suitable for dosing data clustering with high consistency.
Keywords: FCM; fuzzy clustering; dosing records; visual characteristic; mineral flotation.
Matrix completion based prediction analysis of carbon emissions
by Wei Huang, Danqing Wei, Cheng Wang, Chongze Lin
Abstract: China's carbon emissions data at this stage are mainly concentrated at the provincial and national levels. As a major area for the implementation of carbon emission reduction measures, cities have not had a complete carbon inventory for a long time owing to the lack of basic data. In order to solve this problem, this paper constructs a set of prefecture-level CO2 emission forecasting methods to study the carbon emissions of 11 urban areas in Zhejiang Province. The two-dimensional matrix is formed by one-to-one correspondence between city and time. Through the analysis of the historical data of carbon emissions, the intrinsic relationship is found, and the missing data is predicted by the method of matrix completion. Experiments show that compared with Zhejiang's actual carbon emissions statistics data, the difference is found to be within 1%, and can achieve 69.3% higher than the latest method.
Keywords: carbon emissions; carbon inventory; matrix completion.
Effect of the crowdfunding description on investment decisions from the perspective of prosocial behaviour
by Yonghui Dai, Tao Wang, Ziyi Wang, Bo Xu
Abstract: Alleviating poverty is a global challenge and requires extensive mobilisation of the participation from every social force. A feasible way for alleviating poverty is crowdfunding through the internet, which could receive financial aid from a number of people all over the world to some reasonable projects that were released online officially. This work argues that prosocial behaviour can increase the possibilities of achieving the fundraising goals of projects. Based on the prosocial behaviour theory, this work analyses the prosocial factors from the poverty alleviation projects textual data published on an international crowdfunding platform, Kiva, and establishes the logit model to test and verify the feasibility of these factors. The results show that the fundraiser identity and fundraising project orientation in the crowdfunding description both play critical roles in the financial funding process.
Keywords: poverty alleviation; crowdfunding; prosocial behaviour; investment decisions.
Turtle-shell data embedding method with high image fidelity
by Guo-Hua Qiu, Chin-Feng Lee, Chin-Chen Chang
Abstract: In this paper, a modified version of the turtle-shell data-hiding (TDH) scheme is proposed, aiming at improving the quality of stego-image. TDH scheme was first proposed by Chang et al. in 2014. In comparison to other schemes at that time, the scheme exhibited a superior visual quality in the premise of ensuring high embedding capacity (EC). However, in the TDH scheme proposed by Chang et al., the rules to seek for the search space of the hiding message suffer the drawback of omitting the better alternative pixel pair. The scheme proposed in this paper modifies the embedding rule by allowing the selection of a proper region other than the search space proposed by Chang et al., so as to better hide data and minimise the image distortion. According to the experimental results with the same EC, the visual quality of the stego-image in the proposed method is 0.41 dB higher than that of stego-image in Chang et al.s method.
Keywords: data hiding; steganography; turtle-shell; visual quality; embedding capacity.
Scalable and efficient routing protocol for internet of things by clustering cache and diverse paths
by Zhiqiang Ruan, Haibo Luo
Abstract: This paper proposes SENR, a Scalable and Efficient Named-based Routing protocol for the Internet of Things (IoT). Unlike traditional IoT data collection mechanisms, SENR is a consumer-driven data retrieval scheme, which is motivated by the different architecture designs in Named Data Networking (NDN). SENR explores semantic notification and opportunistic caching strategy to enhance the data retrieval efficiency for smart systems. It includes three aspects: 1) for data and Interest aggregation, SENR performs data aggregation according to the hierarchy of namespace, and stores data over in-network storage node; in the data retrieval process, it marks each route notification at the edge nodes, and allows the Interest with the same name prefix to choose the same edge routing node towards to the storage node, which improves the cache hit ratio and reduces the network traffic; 2) for adjustable path selection, SENR adopts link-state routing protocols to collect network topology and compute the shortest path to the content source nodes, moreover, it uses distance-vector routing protocols to select multiple alternative paths to cope with node failures; 3) for partial information updates, SENR employs partial routing update to explicit support of dynamic paths and caching capabilities, so that each node simply informs its neighbours by the available routing updates, and allows the neighbours to fetch such part of updates. Simulation results demonstrate that SENR can significantly reduce the content retrieval delay and routing overheads.
Keywords: IoT; semantic query; opportunistic caching; future network.
MOGATS: a multi-objective genetic algorithm-based task scheduling for heterogeneous embedded systems
by Mohaddeseh Nikseresht, Mohsen Raji
Abstract: Multi-objective optimisation is an unavoidable requirement in different steps of embedded systems design, including task mapping and scheduling. In this paper, a new Multi-Objective Genetic Algorithm-based Task mapping and Scheduling (abbreviated as MOGATS) is presented for heterogeneous embedded system design. In MOGATS, the architecture of the hardware platform and the set of tasks in the form of a task graph are assumed to be given as the inputs. The task mapping and scheduling problem is modelled as a genetic algorithm-based optimisation approach in which the execution time, energy consumption, and reliability of the scheduling are considered as the objectives of the optimisation method. In MOGATS, we are interested in finding the Pareto frontier of the solutions (scheduled tasks) in order to help the designer to pick out the best outcome according to different design considerations. The experimental results on real application task graphs show that MOGATS provides a better solution than the greedy algorithm if it is applied as a single-objective task scheduling method. Moreover, the superiority of MOGATS in comparison with the state-of-the-art single- and multi-objective task scheduling algorithms is shown in terms of standard performances factors, such as scheduling length ratio and speed-up parameters.
Keywords: heterogenous embedded systems; task scheduling; multi-objective optimisation; genetic algorithm.
Research on teaching practice of app inventor course with embedded in computational thinking
by Hexiao Huang, Leilei Chen, Siwei Jin, Ying Wang, Hongzhi Hu
Abstract: At present, the concept of computational thinking is spreading in education around the world. From the point of view of current research and application in China, the research on the cultivation of computational thinking in K-12 education is still in the early stage. Based on the graphic programming design course of app inventor in primary and secondary schools, this study embeds computing thinking into teaching objectives and contents, constructs the teaching model of app inventor course with embedded computing thinking, and discusses how to cultivate students' computing thinking ability in teaching practice. The method provided in this paper is helpful to analyse the changes of students' computational thinking during the teaching process, and it can effectively identify teaching effects.
Keywords: computational thinking; app inventor; abstract thinking; teaching practice.
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: 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.
An experimental estimate of the impact produced on PNU by new generation video codecs
by Andrea Bruno, Giuseppe Cattaneo
Abstract: The resolution of video cameras has increased considerably in recent years going from from 320 x 240 pixels to the current 4K 3840 x 2160 pixels per frame. This trend, along with the spread of online streaming, has forced the main companies to create a new generation of codecs with a greater efficiency and higher compression ratio to fully exploit the features of these devices even on the internet. This has led to a new generation video formats, such as H.265, VP9 and AV1, with new compression methods and more sophisticated algorithms. However, compression can heavily affect the noise present in each frame. Other encoder specific features, such as the intra-frame prediction for H.265 and AV1, can flatten the residual noise in the frame. A series of experiments were designed to verify whether the well-documented techniques for source camera identification based on PNU can still be applied to videos in these formats. Surprisingly, the experiments proved that the results are far from what could be expected for videos encoded with traditional codecs, and they can become even worse for higher resolution videos depending on the encoding scheme. In some experiments, the PNU extracted from the video frames could not be used for source camera identification. Nevertheless, the most original result achieved was the methodology designed for the experiments. In order to avoid any hidden, and therefore misleading artifact, it was decided to discard source videos coming from commercial cameras encoded in the target formats. The source videos were initially acquired as a raw data stream from a dedicated embedded system and then recorded with an open source YUV encoder. Successively, the same video was encoded in each of the formats under study using an open-source and well documented code (in some cases proposed as reference implementations). This allowed to build a fair input dataset without any hidden side effects produced by the codecs and the post-processing tools installed on the device by the manufacturers.
Keywords: PNU; video forensics; video codecs; video compression; intra-frame prediction.
Lightweight and efficient approach for multi-secret steganography
by Katarzyna Koptyra, Marek R. Ogiela
Abstract: This paper compares the efficiencies of two approaches of multi-secret steganography in lightweight systems: interlacing and multi-level. The study was conducted for two and three secrets with use of F5 algorithm for both approaches. The embedding times were measured with and without I/O operations. The application of these techniques in lightweight solutions is discussed.
Keywords: steganography; internet of things; efficiency; interlacing; multi-level steganography; image steganography.
A survey on screenlogger attacks as well as countermeasures
by Hugo Sbai, Jassim Happa, Michael Goldsmith
Abstract: Keyloggers and screenloggers are one of the active growing threats to users' confidentiality as they can run in user-space, easily be distributed and upload information to remote servers. They use a wide number of different technologies and may be implemented in many ways. Keyloggers and screenloggers are very largely diverted from their primary and legitimate function to be exploited for malicious purposes compromising the privacy of users, and bank customers notably. Owing to the recent multiplication of mobile devices with a touchscreen, the screenlogger threat has become even more dangerous. This threat is even harder to fight given the limited resources of the affected devices. This paper is the first step of a project aiming at proposing efficient countermeasures against screenloggers. It provides a complete overview of the different techniques used by this malware and discusses an extensive set of plausible countermeasures.
Keywords: screenlogger; virtual keyboard; noise; screenshot; malware detection; OCR; shoulder surfing.
Secure RGB image steganography based on modified LSB substitution
by Laiali Almazaydeh
Abstract: Different steganography techniques have been presented based on RGB image as the image considered to be secure cover for hidden data. Specifically,this paper presents edge-based image steganographic method using parameterised canny edge detector, that relies on embedding the secret message bits into variable LSB length of the blue colour channel of the cover image. The blue colour channel is selected as the steganography-based research showed that the visual perception of blue colour intensity is less distinct than the red and green colours. Secret message bits are embedded up to four bits of LSB which are selected by a random number generator. Additionally, as the less significant information is carried out by the LSB of each pixel so slight changes of LSB will not affect the visual quality of the cover image. The proposed algorithm was tested on a set of RGB colour images, and satisfactory results were demonstrated regarding minimum distortion in the blue colour of a pixel and visually identical original and stego images.
Keywords: security; cryptography; steganography; LSB; canny edge detector; RGB image.
Modelling performances of an autonomic router running under attack
by Lelio Campanile, Marco Gribaudo, Mauro Iacono, Michele Mastroianni
Abstract: Modern warehouse-scale computing facilities, providing computing services
to very large number of users by means of a massive sharing of resources, seamlessly enabled by virtualisation technologies, are based on thousands of independent computing nodes that are administered according to efficiency criteria that depend on workload. Networks play a pivotal role in these systems, as they are likely to be the performance bottleneck, and because of the high variability of data and management traffic. In this paper, we focus on performance modelling of autonomic routers, to provide a simple, yet representative, elementary performance model to provide a starting point for a comprehensive autonomic network modelling approach. The proposed model is used to evaluate the behaviour of a router under attack under realistic workload and parameter assumptions.
Keywords: performance evaluation; network security; autonomic networks; Petri nets.
LEG-PER - LiDAR-enhanced GNSS positioning error reduction
by Walter Balzano, Fabio Vitale
Abstract: Internet of Things is a promising research area, with many applications in smart home devices but also in vehicular communication and self-driving vehicles. Using this technology, vehicles are able to establish P2P connections in order to share information, improving road security and reliability against accidents due to road and traffic conditions. IoT and vehicle technologies are strongly dependent on positioning, which in outdoor environments is normally determined using satellite systems like GPS and GLONASS. However, the precision is not very reliable due to multipath issues and when the sky visibility is limited, like in urban canyons. In this paper we present LEG-PER - LiDAR-Enhanced GNSS Positioning Error Reduction, a new methodology that improves the accuracy of satellite-based systems using a combination of V2V and an elastic graph generated by the vehicles in the area using LiDAR-determined distances.
Keywords: vehicle-2-vehicle; LiDAR; global navigation satellite system.
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.
Maintenance cycle optimisation of multi-component systems under constraints of overall cost and reliability
by Hong Xiao, Rongyue Zhang, Zhigang Chen, Yingshuang Liu, Yubin Zhou
Abstract: Periodic maintenance inevitably leads to "over-maintenance" or "non-timely maintenance" of equipment or systems. In order to improve reliability and reduce maintenance cost, this paper presents a dynamic maintenance cycle optimisation model for multi-component systems. In this paper, the influence of different maintenance intervals on system reliability and total maintenance cost is deduced under the condition that the system is in limited service time and the components are not fully maintained. The model takes the optimisation of system maintenance cost as the objective function to solve the optimal preventive maintenance times and maintenance intervals under the constraint of the lowest reliability of the system. The simulation results of the last example verify that the proposed method can significantly improve the maintenance cost, system reliability and reliability fluctuation stability, and can be used to guide the subway maintenance cycle optimisation practice.
Keywords: subway vehicles; life-time maintenance cost; imperfect maintenance; multi-component; optimisation.