International Journal of Embedded Systems (95 papers in press)
by Rasmus Ulslev Pedersen, Martin Schoeberl
Abstract: More and more embedded systems are emerging based on managed language runtime systems using garbage collected languages such as Java, Python, or the .NET language family. Furthermore, the garbage collection (GC) process is a bottleneck in an embedded system, effectively blocking most other processes including mutator memory access, responding to inputs, or asserting outputs. We demonstrate a valuable new heap memory architecture for garbage collected embedded systems, which works by creating a direct path between memory modules to achieve a two-fold speedup for a memory copy operation as compared to a baseline scenario using multiplexed shared address- and databuses. This direct-path memory setup is generalisable, and memory modules will continue to work as expected when not engaged in garbage collection. The solution space is evaluated by simulating GC activity extracted from the Elephant Track GC tracer. One particular solution is also implemented in hardware to demonstrate the practical realisation of the direct fast copy architecture.
Keywords: garbage collection; managed languages; embedded systems; realtime; memory; SRAM.
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.
Generating verifiable LOTOS specifications from UML models: a graph transformation based approach
by Salim Djaaboub, Elhillali Kerkouche, Allaoua Chaoui
Abstract: The increasing complexity and widespread use of complex and critical systems requires advanced techniques and tools to address their specification, verification and validation. The goal of this paper is to integrate two standard languages (UML and LOTOS) for the specification, verification and validation of dynamic behaviours of critical systems. The main purpose of this integration is to take benefits of the ease-of-use of the graphical notation of UML in system modelling and the formal notation of LOTOS in system verification. In this paper, we propose a graph transformation based approach to generate automatically LOTOS specifications from UML dynamic models. This approach enables developers on the one hand to model the behaviours of systems using graphical UML statechart and communication diagrams, and on the other hand to generate automatically verifiable LOTOS specifications. The proposed approach is automated using AToM3 tool, and it is illustrated through the modelling and verification of two embedded systems.
Keywords: UML; LOTOS; statechart diagram; communication diagram; critical systems; formal methods; verification; graph transformation; AToM3.
Traffic flow detection method based on vertical virtual road induction line
by Jieren Cheng
Abstract: Traffic flow detection is an important part of an intelligent transportation system and it has a wide range of applications. We analyse the existing methods of traffic flow detection and propose a traffic flow detection method based on vertical virtual road induction line (VVRIL). Firstly, according to the direction of vehicle travel, we set a VVRIL in the middle of the driveway. Secondly, the background image is gained from the video image with a Gaussian mixture model. We then make differential operation between the background image and video image to get a binary image, in which we set the values of the foreground pixels as 1 and that of background pixels as 0. Thirdly, we extract the values of the pixels in the VVRIL of the binary image. Besides, we regard the vehicle maximum length obtained by self-learning as the length of the detection zone and get the information of vehicles in the VVRIL. Finally, we get the number of vehicles through the analysis of vehicle centre coordinates in the VVRIL of each video image. Experimental and theoretical analyses show that the method is accurate enough to meet the requirement of real-time performance.
Keywords: road induction line; traffic flow detection; self-learning; Gaussian mixture model.
Router-shared-pair mesh: a reconfigurable fault-tolerant network-on-chip architecture
by YaLi Chen, Kaixin Ren, Naijie Gu
Abstract: This paper proposes a fault-tolerant scheme on a network-on-chip-based system-on-chip (NoC-based SoC), for problems of isolated processing element (PE) and parted regions caused by permanent faults. The scheme is referred to as router-shared-pair mesh (RSPmesh). The topology architecture of the RSPmesh uses the design that a pair of neighbouring PEs share a pair of routers, and uses MUXs to provide diversity for link-connections between routers. A topology reconfiguration algorithm and a routing algorithm corresponding to the RSPmesh are also proposed. Thus, when there are faulty routers or links, RSPmesh-based NoC can be reconfigured to a new 2D mesh NoC with maybe smaller size, but regular and with no faults, and it is able to serve all healthy PEs. The RSPmesh uses no spare routers, and only makes several routers disable according to actual needs in topology reconfiguration. Evaluation and experimental results show that the proposed scheme achieves significant improvements in reliability.
Keywords: network-on-chip; fault tolerant; isolated PE problem; parted regions problem; topology reconfiguration; 2D mesh; router-shared-pair mesh.
Application source code modification for processor architecture lifetime improvement
by Montassar Ben Saad, Ahmed Jedidi, Smail Niar, Mohammed Abid
Abstract: In the optimal functioning of SoCs, two significant metrics of quality are the most important: lifetime and reliability. This paper focuses on methods to increase the lifetime of a processor. Two methods are presented: Relax Point Injection (RPI) and Code Structure Adaptation (CSA). In RPI, a specific treatment is incorporated into the application code to prevent a harmful rise in the temperature of the chip. The MTTF of the processor is increased by 33.88% through means of an RPI method. However, the execution time of the application is sometimes increased by the RPI by more than 12%. In the CSA method, the arrangement of the application code is regulated to improve the lifetime of the processor. The MTTF of the processor is increased up to 28% by the CSA technique and the implementation time is maintained.
Keywords: mean time to failure; thermal dissipation; relax point injection; code structure adaptation.
Iterative algorithms for impulsive noise reduction in OFDM-based power line communications
by Samir Laksir, Abdelaali Chaoub, Ahmed Tamtaoui
Abstract: Power Line Communications (PLC) is a technology that permits data transmission using electrical networks. In the last few years, multimedia transmission (e.g. audio, image and video) over electrical signals has received a huge amount of research
interest thanks to the already-existing indoor networks. However, impulsive noise presents the most impairments in PLC systems caused by switching transients in the in-home networking. In this paper, we propose some iterative suppression algorithms based on accurate impulsive noise estimation, using an adaptive threshold under a target false alarm probability. The proposed algorithms detect the amplitudes of impulsive noise, and then cancel them iteratively from the received contaminated signal. The results show that the proposed algorithms provide noticeable improvement in terms of bit error rate, peak signal-to-noise ratio, and visual reconstructed image quality, when compared to that of the conventional methods.
Keywords: power line communications; orthogonal frequency division multiplexing;
multimedia transmission; impulsive noise; iterative suppression algorithms; false alarm probability; impulsive noise estimation; bit error rate; peak signal-to-noise ratio; visual image quality.
A new scheme for improving the utilisation of nested-cloud resources
by Yuan Ni, Zhiwei Zhang
Abstract: In recent years, cloud computing has played an important role in IT fields. It brings benefits such as high computing efficiency, cost saving, provisioning on-demand of computing resources and high utilisation of the hardware. However, with more fields and business cloud computing involved, the inherent problems in its structure are restricting the further development of cloud computing. The most serious problem is how to achieve the highest usage of the cloud resources. In this paper, we present a scheme based on multi-layer nested virtualisation. It can make full use of the allocations of a virtual machine provided by an Infrastructure-as-a-Service (IaaS) provider. Furthermore, we also propose a corresponding task scheduling algorithm for the nested cloud environment. The task scheduling considers both the computing efficiency and the monetary cost paid for the cloud service. In order to justify our proposal, we use KVM as unmodified multiple hypervisors to run multi-layer VMs on x86 platform. We also do simulations to test our task scheduling algorithm. The result shows encouraging support for our proposal.
Keywords: cloud architecture; multi-layer nested virtualisation; task scheduling; KVM; Intel-VT.
A provably secure delegated authentication scheme and its applications
by Qun Lin, Jianzhong Li, Xuechang Ren
Abstract: In a proxy signature scheme, an original signer can delegate his/her signing capability to a proxy signer, so that the proxy signer can sign on behalf of the original signer. Proxy signature is an important technology in delegated authentication scheme, so it is important to design provably secure proxy signature schemes. In 2012, Boldyreva et a1. gave the definition of proxy signature and formalised a model of security for proxy signature schemes. They not only formalised a model of security for proxy signature schemes, but also specified the adversary's capabilities and goals. In this paper, we propose a new provably secure proxy signature scheme under this model, which is more efficient than that of the existing ones. Furthermore, we use this signature scheme to construct a proxy blind signature scheme, and give the security analysis of the new scheme. Proxy blind signature can be applied in cloud computing.
Keywords: proxy signature; provable security; proxy blind signature; cloud computing.
Region-based trilateral filter for depth video coding
by Chunyue Hu
Abstract: 3D video systems using depth-image-based rendering (DIBR) are attracting significant interest because of their low processing cost. In order to enable these systems, the depth video must be coded in addition to the texture video. During the coding process, coding artifacts in the depth map could cause video quality degradation of virtual view. In this paper, a region-based trilateral filter for depth video coding is proposed to improve the coding quality. First, the depth map is split into multiple objects with the label of segmentation. Then, in order to remove the artifacts from different objects and preserve the sharp edge, each pixel is filtered by using neighboring pixels belong to the same object. The proposed method has been evaluated with several 3D video sequences in HEVC platform. Experimental results demonstrate that the rendering quality can achieve a considerable gain under the same bitrate.
Keywords: 3D video; HEVC; depth video coding; trilateral filter.
Can finger knuckle patterns help to strengthen e-banking security?
by Abdallah Meraoumia, Djamel Samai, Salim Chitroub
Abstract: Communication via the internet has become vital for any kind of information exchange, private, public, commercial, or military. Banks are the first that have used the internet for financial transactions (e-banking). However, the safe use of e-banking implies that all precautions have been considered to identify legitimate users and thus avoid economic and social damage that may be caused by any possible fraud. In this context, we propose in this paper a secure biometric system dedicated to e-banking for reducing the fraud risk and strengthening the customer confidence. The fuzzy commitment concept associated with the Finger-Knuckle-Print (FKP) is the core of our proposed system. However, such a system will only be efficient if the FKP features are accurately extracted. For this, we have developed a new method of feature extraction called Adaptive Extended Binary Pattern (AELBP). The obtained experimental results have been judged promising for a high security of e-banking with guaranteed trust from costumers.
Keywords: information security; cryptography; fuzzy commitment; biometrics; finger-knuckle-print; feature extraction; local binary pattern; data fusion.
Interactive map matching and its visualisation: techniques and system
by Li Cai, Bingyu Zhu, Yifeng Luo, Shuigeng Zhou
Abstract: The trajectory data of taxis is an important kind of traffic data. Many traffic applications need to perform processing and analysis on trajectory data. Visualising trajectory data of vehicles on road maps is an important measure of reflecting and demonstrating the trend of traffic variation, where map matching from trajectory data to the road network plays the most crucial role in such a visualisation process. We design and implement a novel interactive visualised map matching system in this paper, namely MMatchingVis, which provides multiple front-end functions including road selection, data extraction, map matching algorithm selection, and result display, based on web techniques and Baidu Map. MMatchingVis employs the JStorm platform for trajectory data processing. We evaluate MMatchingVis' map matching results with the trajectory dataset collected from 6599 taxis in Kunming, and evaluation results show that MMatchingVis could efficiently process and analyse trajectory data, support multiple user interaction models, and provide fine-grained visualisation presentation.
Keywords: visualisation; map matching; GPS trajectories data; user interaction; cloud computing.
Sign prediction and community detection in directed signed networks based on random walk theory
by Baofang Hu, Hong Wang, Yuanjie Zheng
Abstract: Previous studies on social networks often focused on networks with only positive edges between node pairs. As a significant extension, we applied the random walk theory based on graphs with both positive and negative edges. In particular, we derived the commute time similarity between node pairs in directed signed networks and proved that its corresponding Laplace spectral was a legal kernel to compute the similarities between node pairs. We used the similarity distance to predict the sign and direction of the edges on two real social networks based on the idea of collaborative filtering, and the experimental results showed that the method provided good performance. We also used the defined Laplacian spectrum of the directed signed networks to detect the community structure in two real-world networks and three synthetic networks, and the algorithm achieved good performance.
Keywords: directed signed network; random walk; community detection; sign prediction; collaborative filtering.
Handling startling circumstances with IRM scheduler of real-time systems
by Mahmoud Naghibzadeh
Abstract: There are many vital real-time systems, such as avionics, mission-critical, industrial control, and space missions, which cannot tolerate any request overruns. Safeness analysis of such systems is directly dependent on the scheduling strategy being used. Rate-monotonic and earliest deadline first are the leading scheduling strategies for these systems. Before deployment, it is essential to make sure no task misses occur for such hard real-time systems. This is usually done by formally proving the safeness of the system for the given set of tasks. Besides, a scheduler is preferred which performs better in unexpected situations, such as when a transient fault occurs. The purpose of this paper is to first reveal some new behaviours of the intelligent rate-monotonic scheduler and then evaluate its performance in unexpected situations. Simulation results show that it performs better than both rate-monotonic and earliest deadline first in such situations.
Keywords: scheduling; intelligent rate-monotonic; pre-emption; deadline miss.
A practical approach for estimating human daily water intake
by Bin Dai, Rung-Ching Chen, Yuan-Yu Ding
Abstract: The proportion of water in the human body must keep in a proper level to maintain normal physiological action. Daily water consumption needs to be properly supplemented in time to protect the health of human beings. Water consumption is related to age, body weight, temperature, physical activity, etc. These factors are generally difficult to express with crisp data, and it is not easy to get accurate values of recommended daily water intake. The main objective of this paper is to build a recommended daily water intake system and a practical application using fuzzy reasoning methods. We also compared the actual recommended values with our system output values to find the differences. We have designed an application on the Arduino platform and use Bluetooth electronic scale to connect to smart phones to help people to control their daily water intake. The experiment results proved that the recommendation system is effective in actual applications.
Keywords: recommendation system; daily water intake; fuzzy system; Arduino; electronic scale.
Efficient authentication scheme for vehicular ad-hoc networks with batch verification using bilinear pairings
by Hang Tu, Cui Jingsong
Abstract: The potential for vehicular ad hoc networks (VANETs) in improving traffic, enhancing road safety and reducing traffic accidents has attracted attention from academia, industry, and governments. To ensure secure communication in VANETs, a number of authentication schemes, including those with batch verification, were proposed in recent years. However, studies have demonstrated that most of the existing schemes suffer from bad performance or weak security. To address those problems, we construct a new identity-based digital signature (IBDS) scheme using bilinear pairings. The IBDS scheme is then used to construct a new identity-based conditional privacy-preserving authentication (IBCPPA) scheme for VANETs without the need for a map-to-point hash function or double secret keys. Using simulations, we demonstrate that our provably-secure IBCPPA scheme not only achieves better performance than related schemes, but also overcomes the inefficiency problem of the double secret keys in related schemes (i.e. the system does have to manage two secret keys to provide security).
Keywords: authentication; vehicular ad-hoc networks; security; bilinear pairing.
A new efficient privacy-preserving data publish-subscribe scheme
by Ping Chen, Zhiying Wang, Xiaoling Tao
Abstract: Data publish-subscribe is an efficient service for users to share and receive data selectively. Owing to the powerful computing resources and storage capacity, the cloud platform is considered as the most appropriate choice to publish and subscribe large-scale data generated in real-world life. However, the cloud platform may be curious about the content of published data and subscribers' interests. In this paper, we aimed at realising a secure and efficient privacy-preserving data publish-subscribe scheme on cloud platforms. On one hand, we adopt ciphertext-policy attribute-based encryption (CP-ABE) to encrypt the data based on its access policy. Moreover, part of the decryption computation is shifted to the cloud platform to reduce subscribers' computation overhead. On the other hand, we use an efficient searchable encryption scheme based on bloom filter tree (BFtree) to protect subscribers' privacy and match their interests with encrypted data. Not only that, publishers and subscribers can also exchange their roles in our scheme. The security analysis and experimental results prove that our scheme is efficient and secure in privacy-preserving data publish-subscribe service.
Keywords: privacy-preserving; data publish-subscribe; CP-ABE; BFtree; cloud platform.
A new identity-based public auditing against malicious auditor in the cloud
by Kun Qian, Hui Huang
Abstract: With the development of cloud computing, the integrity of data is becoming increasingly important. The auditing schemes for data integrity allow data owners to verify the integrity of the data stored in an untrusted server. Most of public auditing schemes are based on the public key infrastructure (PKI), which may lead to certificate management problems. Recently, an identity-based public auditing scheme was proposed and it could effectively reduce computation cost of auditors and solve certificate management problems. However, the scheme was proved to be insecure. In this paper, we consider the malicious auditor and propose a new identity-based public auditing against malicious auditors in cloud computing. The new construction is proved to be secure by assuming the hardness of the computation Diffie-Hellman problem (CDHP). Finally, compared with the existing identity-based auditing scheme, our scheme is efficient and reduces the computation overhead of the auditor.
Keywords: data storage; cloud computing; public auditability.
EEG control variable algorithm and motion control strategy for toy rail car
by Hongguang Pan, Mei Wang, Xiaokang Wang, Jzau-Sheng Lin
Abstract: For training the thought concentration ability of hyperactivity sufferers, this paper proposed a kind of electroencephalogram (EEG) control variable extraction method and the motion control strategy. Firstly, the technologies of EEG acquisition from frontal lobe, and the wireless data transmission from the acquisition card to the Pad Phone were realised. Then, through the wavelet Mallat algorithm and FFT to extract the EEG control variable, the accurate controls of start, stop, and running velocity of the toy rail car were implemented. Thirdly, the mathematical model between the velocity and the EEG control variable was established, and then the start threshold, the swerve control and the speed adjust method were accurately designed using this model, so that the delay start and the swerve speed were solved. In addition, the wireless data transmissions among the Pad Phone, the EEG acquisition module and the rail-electric potential controller were realised. Finally, it was proved that the proposed EEG control variables and the control strategy effectively fulfill the accurate speed control and the stable motion control.
Keywords: EEG; wavelet transform; Mallat algorithm; FFT; motion control.
DPVFS: a dynamic procrastination cum DVFS scheduler for multicore hard real time systems
by Shubhangi Gawali, Biju Raveendran
Abstract: Optimising energy consumption has become primary focus of research in recent years. Static and dynamic energy optimisation during task scheduling is one of the most prominent measures available. This is achieved mainly by shutdown and slowdown techniques. In uniprocessor real-time systems, the most widely used shutdown and slowdown techniques are Dynamic Procrastination (DP) and Dynamic Voltage and Frequency Scaling (DVFS). This paper proposes DPVFS a hard real-time task scheduler for multicore system to optimise overall energy consumption without deadline misses. DPVFS combines DP and DVFS for multicore systems to save overall energy consumption. DPVFS shut the processor down whenever possible with the help of procrastination. If shutdown is not possible, it adjusts the voltage and frequency to reduce dynamic energy consumption. The experimental evaluation of DPVFS with synthetically generated benchmark program suites shows savings of 18.8% and 33.2% of overall energy over DP-based schedulers and DVFS-based schedulers respectively.
Keywords: procrastination; dynamic voltage and frequency scaling; multi-core real-time scheduling.
Energy oriented EDF for real-time systems
by Gil Kedar, Avi Mendelson, Israel Cidon
Abstract: Energy is a major concern when designing real-time systems. A common method for saving energy while still guaranteeing the real time constraints is to embed dynamic voltage and frequency scaling (DVFS) mechanisms and dynamic power management (DPM) mechanisms within a real-time scheduling algorithm, such as EDF. This paper proposes a new extension to the EDF scheduler, termed energy oriented EDF (EO-EDF). The new scheduler makes it possible to change the original EDF task execution order to better use the slack time and thus decrease the energy consumption, while still meeting the task deadlines. The new task order is defined according to a novel criterion we invented, termed task prediction order (TPO). The paper introduces two new versions of the EO-EDF scheduler, termed TPO-EDF and STPO-EDF. While STPO-EDF applies the TPO criterion in a static manner, TPO-EDF allows it to be used dynamically. We simulate the new proposed algorithms using both synthetic workloads and real time benchmarks. The evaluations show that integrating both the TPO-EDF and STPO-EDF scheduling algorithms with DVFS and DPM mechanisms achieves an energy saving of 30% on average, in comparison with current known EDF-based using DVFS and DPM mechanisms.
Keywords: low energy; real-time; scheduling; EDF; EO-EDF; TPO.
Model construction and application of coal mine CPS perception and control layer
by Ping Ren, Jingzhao Li, Dayu Yang
Abstract: Coal mine cyber-physical system (CPS) is a complex system that combines the information resources and the physical resources, which are established above and below ground. The system realises the integrated design of the information world and the physical world, so that the integrated system is more reliable and efficient. The key technology of coal mine CPS is the construction of a physical system model and information processing. Aiming at the situation of the coal mine industrial system consisting of several different physical systems, in this paper we first analyse the parametric model of infection control layer and construct the continuous-time system model, including the non-memory continuous time system model, the memory system model, the feedback control system, and the discrete-time system model. During the actual production process, considering that some systems of coal mine CPS are continuous time and discrete time hybrid systems, by analysing the parameters of the inclined lane signal subsystem, the anti-wrong turnout subsystem, the anti-slip car subsystem, the frequency control system, such as system function, input signal, state transition, and control output, a hybrid system state model with n inputs and m outputs is proposed, and the parameters application model of perception and control layer is obtained. In practical application, the inclined lane transportation architecture has achieved good results.
Keywords: cyber-physical system; perception and control layer; coal-mining industry; inclined tunnel transportation; model; status.
MOESIF: an MC/MP cache coherence protocol with improved bandwidth usage
by Geeta Patil, Biju Raveendran, Neethu Bal Mallaya
Abstract: This paper proposes a novel cache coherence protocol MOESIF - to improve the off-chip and on-chip bandwidth usage. This is achieved by reducing the number of write backs to next level memory and by reducing the number of responders to a cache miss when multiple copies of data exists in private caches. Experimental evaluation of various SPLASH-2 benchmark programs on CACTI 5.3 and CACOSIM simulators reveals that the MOESIF protocol outperforms all other hardware-based coherence protocols in terms of energy consumption and access time. MOESIF protocol on average offers 94.62%, 88.94%, 88.88% and 4.47% energy saving, and 96.37%, 92.83%, 92.77% and 9.21% access time saving over MI, MESI, MESIF and MOESI protocols, respectively, for different numbers of cores/processors.
Keywords: cache coherence; MC/MP cache; energy-efficient coherence protocols;.
A detection model of malicious Android applications based on Naive Bayes
by ChunDong Wang, Yi Zhao, XiuLiang Mo
Abstract: With the popularity of mobile devices, thousands of malicious applications targeting mobile devices, including the popular Android platform, are created on a daily basis, which cause substantial losses for their users. How to detect malicious applications efficiently has become a new and ever-growing challenge. However, previous studies overlooked malicious potential permission combinations as a feature in detection. In this paper, according to the Android permission mechanism, we propose and implement a detection model based on Naive Bayes. The model uses the Apriori algorithm to effectively mine the potential correlation in permissions among the various malicious applications. Then, in order to improve the performance of the Android malware detection system, the additional feature methodology proposed in this paper is used to deal with samples which have dangerous permission combinations. Combined with the improved Naive Bayes classifier, samples are classified into two categories. The experimental result reveals that the optimal detection rate in our detection model is 95.63%. Thus, it significantly improves the accuracy of the Naive Bayes in the detection of malicious Android applications.
Keywords: Android permission; malware detection; machine learning.
An efficient privacy-preserving friendship-based recommendation system
by Bingpeng Ou, Jingjing Guo, Xiaoling Tao
Abstract: With the development of Internet, recommendation system plays a significant role for providing personalised services in our life. However, it also raises serious concerns about privacy since the system collects a lot of personal information. Thus, plenty of schemes have been proposed to address the privacy issues by using cryptographic techniques. However, with the rapidly increasing of users and items, most of existing cryptography-based schemes become inefficient because of the huge computation cost. In this paper, we propose an efficient privacypreserving scheme for recommendation system. Compared with existing schemes, our scheme does not require that friends of user are online during computing predicted rating. Finally, we evaluate the performance of our scheme with the MovieLens 20 m dataset and it shows that our scheme can reduce the overhead of computation and communication.
Keywords: recommendation system; privacy-preserving; homomorphic encryption; proxy re-encryption.
A result correctness verification mechanism for cloud computing based on MapReduce
by Ziao Liu, Tao Jiang, Xiaoling Tao
Abstract: MapReduce is widely applied as a parallel programming model to process massive amounts of data in cloud computing environment. However, in open systems, the workers of MapReduce framework are provided by various administration domains and may be unreliable or malicious. The existing schemes of MapReduce processing model based on multiply duplicate tasks can effectively detect the lazy and non-collusive workers. However, they cannot cope with the vulnerability that malicious workers collude to return incorrect results and thereby undermine the final computation results of users outsourced tasks. In this paper, we present an effective result correctness verification mechanism for MapReduce in public cloud computing environment. By using task duplication and weighted correctness attestation graph, our mechanism can effectively detect both non-collusive and collusive malicious workers in public cloud environments. In order to further improve the detection speed, we introduce a worker selection method based on trust values and consistency relationship. Finally, analysis and experimental results indicate that our mechanism can guarantee higher detection rate with proper additional computation overhead.
Keywords: cloud computing; result correctness; MapReduce; attestation graph.
A new approximate image verification mechanism in cloud computing
by Mengping Yin
Abstract: With the growing prevalence of cloud computing, more and more data especially images and videos are stored in cloud servers. To ensure the security of private data, data owners usually encrypt their private data before outsourcing the data to cloud servers. It is discovered that highly correlated data exist in storage outsourcing and much useful information can be extracted from these correlated data and used for cloud-based services. In the paper, we propose a scheme of encrypted image verification in cloud computing for mobile devices. Many existing schemes focus on verification of query results for outsourced text data or identical images. Different from that, the proposed scheme aims to verify the correctness of query results for similar images. Through the successful query of similar images, power and memory resources of mobile devices can be saved. The security of our scheme is analysed in the random oracle model, and analysis shows that the scheme is secure against adaptive chosen-keyword attack. And what's more, the experimental results demonstrate that our scheme is an efficient one.
Keywords: correctness verification; encrypted image; cloud computing; local sensitive hashing.
Local connectedness over soft rough topological space
by Li Fu, Hua Fu, Zhen Liu, Fei You
Abstract: In this paper, the connectedness of the soft topological space is further discussed, based on the soft connected rough topological space. Continuous mapping is defined in the soft rough topological space, and the property of soft connectedness under continuous mapping is discussed. The connectedness of soft points and local connectedness are defined in the soft connected rough topological space, and the local properties of connectedness are given.
Keywords: soft rough formal context; soft connected rough topological space; soft point; local connected space; connected branch.
Efficient publicly verifiable conjunctive keyword search over encrypted data in cloud computing
by Kai Nie, Yunling Wang
Abstract: Cloud computing has brought appealing features for its users, such as on-demand computing resources, flexible and ubiquitous access and economical cost. Individuals and enterprises are motivated to outsource data to cloud servers. However, the privacy and security of users' data are obstacles preventing application of cloud computing. Searchable encryption is a way to protect sensitive data, while preserving search ability over encrypted data. However, the server may be lazy and return part of results for self-benefit. Therefore, a verification mechanism should be established to guarantee the integrity of search results. In this paper, we present an efficient publicly verifiable conjunctive keyword search scheme. Our scheme ensures the correctness and completeness of search results even if the result is an empty set. Compared with existing keyword search schemes, our scheme is more efficient to verify the search results. Furthermore, we prove that the proposed scheme can achieve the desired security properties.
Keywords: cloud computing; privacy-preserving; keyword search; completeness.
A design methodology for mobile and embedded applications on FPGA-based dynamic reconfigurable hardware
by Darshika G. Perera, Kin Fun Li
Abstract: With the proliferation of mobile and embedded devices, multiple running applications are becoming a necessity on these devices. Apart from optimised hardware-software architectures, state-of-the-art techniques and design methodologies are needed to support complex applications running on mobile and embedded systems. We envision in the near future, many mobile devices will be implemented and delivered on FPGA-based reconfigurable chips. Our previous analysis illustrated that FPGA-based dynamic reconfigurable systems are currently the best option to deliver embedded applications that have stringent requirements. However, computation models and application characteristics also play significant roles in determining whether FPGA-based reconfigurable hardware is indeed a good match for specific applications on a mobile or embedded platform. In addition, there are different methods of reconfiguring the hardware on chip dynamically. Selecting a specific reconfiguration method and designing the corresponding hardware architectures for an application are important and challenging tasks in reconfigurable computing systems. In this work, we propose a design methodology for FPGA-based dynamic reconfigurable hardware that serves as a guideline to the embedded hardware designers in mapping the computation models and characteristics of an application to the most suitable reconfiguration methods. The most common pipelined and parallel (functional) computation models are used as case studies to illustrate the design methodology.
Keywords: design methodology; embedded applications; FPGAs; mobile devices; dynamic reconfigurable hardware.
Development of a charging system for current-controllable batteries based on a multi-stage mechanism
by Hsiung Cheng Lin, Chi-Wei Liu, Jhih-Siang Lin
Abstract: Increasing demand for back-up electric power supply has enforced the development of efficient batteries charger in industry. However, the study of an effective charging management mechanism still needs a further improvement to overcome some limitations of traditional methods. For this reason, this paper proposes a current-controllable batteries charging system using multi-stage charge mechanism for achieving more effective performance. Firstly, a high frequency two-transistor forward converter is designed to provide necessary DC power supply for both charger and control unit. For the charger, it is designed based on an ideal multi-state strategy, and it can provide a desired/changeable constant charging current. Also, each battery is ensured at a balanced level by the proposed battery equilibrium circuit before the charging process. The experimental results confirm that the proposed charging system is superior in term of flexibility, high efficiency, and self-management capability.
Keywords: battery equilibrium; lead-acid batteries; current-controllable; self-management; charger.
A method of crime rate forecast based on wavelet transform and neural network
by Li Mao, Wei Du
Abstract: Accurate prediction of crime is highly challenging. In order to improve the efficiency of situational crime prevention, the temporal distribution of the crime rate within 24 hours is analysed and a forecast model combining Discrete Wavelet Transform and Resilient Back-Propagation Neural Network (DWT-RBPNN) is presented. First, historical crime incidence sequences obtained by the sliding window are decomposed by DWT. Then RBPNN-trained decomposition sequences are used to predict the incidence of future trends and details. Finally, the trends and details are reconstructed to get the final prediction sequence. The experimental results show that the proposed model has relatively high accuracy and feasibility on the crime rate prediction compared with the single method of BPNN. The utility of the DWT-RBPNN model can offer an exciting new horizon to provide crime rate forecasting and early warning in situational crime prevention.
Keywords: crime rate forecasting; sliding window; discrete wavelet transform; neural network; resilient back-propagation.
From real-time design model to RTOS-specific models: a model-driven methodology
by Rania Mzid, Chokri Mraidha, Jean-Philippe Babau, Mohamed Abid
Abstract: The refinement of a Real-Time Operating System (RTOS)-independent real-time design model to a RTOS-specific model is a critical phase in a model-based approach. Model-based approaches allow early verification of the timing properties at the design level. At this phase, if the hardware architecture is supposed to be known, the technological platform (here the RTOS) is not defined. Hence, some assumptions on the platform are implicitly made to achieve timing verification on the one hand and to keep RTOS-independence of the design model on the other hand. However, at the implementation level, these assumptions may be not implementable for the target RTOS. In addition, a difference between the semantic of the software resources used to build the design model and those provided by the RTOS may occur, which may lead to a mismatch between the original design model and the implementation one and affect thus the timing properties. In this paper, we propose a Design Refinement toward Implementation Methodology (DRIM) to address the refinement problem. Having the real-time design model as entry and based on an abstract and a concrete platform models, the methodology firstly evaluates the feasibility of deployment of the given design model on the considered RTOS. When no feasibility problem is raised, the mapping phase generates the appropriate RTOS-specific model. Nevertheless, when the design model is not implementable, the methodology informs the designer about the problem before the effective deployment and guides him for the selection of the appropriate RTOS.
Keywords: design-level verification; real-time operating systems; model-driven approach; design model; RTOS-specific model; UML; MARTE.
A new method of vision-based seat belt detection
by Zhongming Yang, Hui Xiong, Zhaoquan Cai, Peng Yu
Abstract: In the traffic management system, it can greatly improve the management efficiency through the algorithm that monitors and automatically detects whether the driver fastens the seat belt. However, currently prevalent detecting methods cannot achieve satisfactory results in aspects of the detecting rate, the image quality requirement and the colour difference between seat belt and the surrounding environment. In this paper, we propose a method of seat belt detection based on visual positioning. The algorithm locates the window according to the licence plate position and the contour statistics obtained from the gradient. The face detection is used to adjust and determine the seat belt detection area in the window. Finally, the method of seat belt detection based on the connected area is used to detect whether the seat belt is fastened. Experiments show that the successful rate of the proposed method is much higher than other existing methods, and satisfactory results are obtained.
Keywords: seat belt detection; connected components; big data in traffic; structured image data.
Biclique cryptanalysis on block cipher Midori
by Hongluan Zhao, Guoyong Han
Abstract: Biclique cryptanalysis can greatly decrease computation and data complexity by using the main idea of meet-in-the-middle attack and the basic principle of the biclique structure. Midori is a hardware-oriented lightweight block cipher designed by Banik et al. in ASIACRYPT 2015. In this paper, we first demonstrate the lightweight block cipher Midori64/128 and present a general method of biclique attack. Next, we describe how to construct a biclique and attack a full-round Midori block cipher. By investigating the simple key schedule and the encryption structure, we construct a five-round biclique structure of four-dimensional and a four-round biclique structure for Midori64, with data complexity of 2^36 and 2^16 and with computational complexity of 2^126.45 and 2^126.69 , respectively. Moreover, we use a four-round biclique structure to attack Midori128 with data complexity of 2^72 and computational complexity of 2^127.33 . These are superior to the current known results.
Keywords: biclique cryptanalysis; Midori; meet-in-the-middle; block cipher.
Efficient public integrity auditing with secure deduplication in cloud computing
by Huixia Huo, Tao Jiang, Shichong Tan, Xiaoling Tao
Abstract: With the rapid development of cloud computing, storing data to cloud servers has become an increasing trend, which promotes integrity auditing and data deduplication to be two hot research topics. Recently, some existing schemes addressed a problem about integrity auditing with deduplication. However, these schemes did not support aggregating authentication tags of different users in the integrity auditing process, which caused a heavy computation cost to the third party auditor (TPA), especially in the batch auditing process. In this paper, we propose an efficient public auditing with secure deduplication scheme using the idea of aggregate signature, which allows the TPA to verify the correctness of integrity proof generated by the cloud service provider with a constant computation cost. Our scheme can also efficiently support batch auditing, whose auditing complexity on the TPA is independent of the number of auditing tasks. Finally, we prove that our scheme is secure and efficient through security analysis and performance evaluation.
Keywords: efficient integrity auditing; secure deduplication; aggregate signature; batch auditing; cloud computing.
A novel colour image watermarking scheme based on Schur decomposition
by Qingtang Su, Lin Su, Gang Wang, Leida Li, Jianting Ning
Abstract: A novel colour image watermarking scheme based on Schur decomposition is proposed in this paper. By analysing the 3
Keywords: Schur decomposition; colour image watermarking; blind extraction.
Reliable routing schemes in 3D network on chip
by Habib Chawki Touati, Fateh Boutekkouk
Abstract: 3D Network on Chip (3D-NoC) is the replacement for traditional infrastructures and the new design paradigm for communication for future very large scale System on Chip (SoC), because it provides flexibility, extensibility and low power consumption. One of the most important issues of a 3D-NoC design is the implementation of an efficient and reliable routing algorithm, which has a direct impact on the overall network performance. A routing algorithm aims predominantly at fulfilling three distinct objectives: deadlock freedom, congestion awareness and fault tolerance, which is a highly desired but somewhat a challenging task. In this paper, a non-exhaustive list of the most relevant routing algorithms in 3D-NoC are surveyed and classified based on their objectives, the advantages and drawbacks of each algorithm are also presented, as well as the possible enhancements to improve their reliability.
Keywords: 3D network on chip; network on chip; reliability; routing algorithms; congestion awareness; fault tolerance; deadlock freedom.
A separable reversible data-hiding scheme in encrypted image for two cloud servers
by Haidong Zhong, Xianyi Chen
Abstract: This paper presents a separable reversible data hiding in encrypted image (SRDH-EI) based on code division multiplexing (CDM) for two cloud servers. In this method, the image owner can encrypt the cover image by using exclusive-or encryption, and two pre-processed images are uploaded to two cloud servers respectively. In these two cloud servers, two encrypted pixels in the same position of uploaded images consist of the embedded vector, and secret bits can be encoded to different spreading sequences based on the principle of CDM. After that, the sequences can be embedded into the vector. That means each one secret bit can be embedded into two encrypted pixels. At the receiver side, the phase of data extraction and image recovery is commutative. There are three cases as follows. (1) If the receiver only has the data-hiding key, the secret bits can be extracted from the embedded vector. (2) If the receiver only has the encryption key, the original image can be recovered. And the decrypted image is same as the original image. (3) If the receiver has the two keys, the original image and the secret bits can be recovered without any error. Different from the classic dual-images RDH method, the proposed method can protect the content security of cover image when the images are outsourced to the cloud server. Compared with current RDH-EI method, the proposed method has a better performance on the visual quality of decrypted image and the embedding rate.
Keywords: reversible data hiding; dual images; encryption key; cloud sever.
An IoT-oriented real-time storage mechanism for massive small files based on Swift
by Dongjie Zhu, Haiwen Du, Yuhua Wang, Xuan Peng
Abstract: In the Internet of Things (IoT), large amounts of small files are generated from various structure sensors in cloud storage platforms. Real-time storage of massive small files will put great pressures on the traditional file system. The impact on IOPS performance for massive small files is considerable. We provide a unique aggregation storage strategy, Sequential Data Aggregation Strategy (SDAS), for storage of small files. We design a two-level index structure to improve writing rate by transfering randomly write to sequentially write. To improve overall data access efficiency and solve the performance bottleneck of proxy node, we use a files potential relevance of timing to prefetch related files that are merged in other blocks. Simulation results show that relative to the original system, SDAS has shorter response time of writing operation, lower cost of index maintenance cost, more balanced node load, and 30% reduction in disk overhead.
Keywords: cloud storage; IoT; massive small files; object storage; Swift.
A farmland-microclimate monitoring system based on the internet of things
by Maoling Yan, Pingzeng Liu, Cezhong Tong, Xiujuan Wang, Fujiang Wen, Changqing Song, Russell Higgs, Gregory M.P. O’Hare
Abstract: Farmland microclimate is a vital environmental factor that affects crop growth and yield formation. With the rapid and mature development of sensor technology and wireless communication technology, the Internet of Things (IoT) is gradually replacing the ineffectiveness of traditional means of environmental monitoring. It brings new approaches and a broader space for further environmental science research. Based on clear perception, reliable transmission and intelligent processing of IoT concepts, a monitoring system for farmland microclimate is developed in this paper. The farmland environmental-monitoring system consists of three layers. The perception layer integrates meteorological, soil, hydrological and other sensors to form a ground-to-air sensor cluster. The transport layer uses the GPRS (a general packet radio service) technology, which covers the entire country for long distance and effectively transfers the collected data to the server in real time. The application layer is developed for receiving PC software and data storage. On this basis, it establishes a platform for big data service, thus implementing the modelling and analysis of farmland microclimate. After three years of system operation, we have done statistical analysis on the length of life, the loss of data and the reliability of data. Results reveal that the system could ensure more than 80% of data integrity, and it can also secure good stability and reliability of the data. At present, this system has been popularised and applied in the granary project area of Bohai and the desert area of China, which provides accurate data support for the local precise agricultural production.
Keywords: internet of things; microclimate; wireless sensor networks; monitoring; precision agriculture.
Visual field movement detection model based on low-resolution images
by Guangli Li, Lei Liu, Tongbo Zhang, Hang Yu, Yue Xu, Shuai Lu
Abstract: In robotic mapping and navigation, simultaneous localisation and mapping (SLAM) is the computational problem of constructing a map of an unknown environment and simultaneously keeping track of an agent's location. The popularity of sweeping robot has made SLAM famous in the last few years, while the recent visual simultaneous localisation and mapping (VSLAM) based on three-dimensional vision makes it more mainstream. To detect the direction and distance of visual field movement, we build a visual field movement detection model on a low-resolution image. Considering the features of image edge and corners, we mainly use the similarity computation of feature points and matching methods in this model to detect the moving direction and distance of vision field. The experimental results show that the proposed detection model is more accurate and efficient in three different conditions, and can precisely figure out where the vision field moves in a short period of time.
Keywords: low-resolution image; visual field movement detection; template matching.
Exploration and application of the value of big data based on data-driven techniques for the hydraulic internet of things
by Yue Qiang, Liu Fusheng, Song Changqing, Liang Jing, Liu Yanmin, Cao Guangsheng
Abstract: The use of big-data technology to screen the massive amounts of hydraulic engineering data in the internet of things is important for its efficient application. This research applies big-data methodology to water management to solve numerous problems, such as the demand diversification of related interest groups, overall water difficulties, and other problems that arise in hydraulic engineering. A historical database that contains a large amount of data and feedback information is used to design an early-warning health model for a reservoir using big-data methods and based on the C5.0 decision-tree algorithm. The health status of Dingdong reservoir is forecast using the model as a case study. The results show that the reservoir is in a healthy state corresponding to no warning level. The early-warning health model is feasible and effective for using abundant case resources, and could be used widely in reservoir health management.
Keywords: big-data methods; early health warning; water resources data; internet of things; decision tree.
A trust and attribute-based access control framework in internet of things
by Junshe Wang, Han Wang, Hongbin Zhang
Abstract: The integration of the Internet of Things (IoT) and cloud computing is the most up-to-date trend of new network technology, which will bring about great changes for future life. With the rapid development of wireless sensor networks and the gradual maturity of cloud computing technology, IoT, which realises information communication from objects to objects, will be widely used in kinds of environment where cloud computing provides superstrong computing support and ultrastrong data storage capacity for thin smart nodes. However, it has become a key problem that each node accesses data under control with the human intention and that data is shared securely in a distributed IoT environment. Moreover, nodes may belong to different security domains in IoT, and data must be accessed only by selected types of user, which can ensure the security of the IoT system. Therefore, an effective access control technique is the key to solve this issue. To address the problems of security, scalability and cross-domain, this paper proposes a fine-grained and dynamic access control model that combines ABAC with a trust mechanism and considers dynamic trust attribute and static multi-attribute as synthetic constraints. The simulation results show the feasibility and effectiveness of the proposed scheme, which has superior characteristics and improves the security of the IoT system.
Keywords: internet of things; attribute-based; trust evaluation; access control.
A branch-and-bound approach to scheduling of data-parallel tasks on multicore architectures
by Yang Liu, Lin Meng, Ittetsu Taniguchi, Hiroyuki Tomiyama
Abstract: This paper studies a task scheduling problem that schedules a set of data-parallel tasks on multiple cores. Unlike most of previous literature where each task is assumed to run on a single core, this work allows individual tasks to run on multiple cores in a data-parallel fashion. Since the scheduling problem is NP-hard, a couple of heuristic algorithms that find near-optimal schedules in a short time were proposed so far. In some cases, however, exactly-optimal schedules are desired, for example, in order to evaluate heuristic algorithms. This paper proposes an exact algorithm to find optimal schedules. The proposed algorithm is based on depth-first branch-and-bound search. In our experiments with up to 100 tasks, the proposed algorithm could successfully find optimal schedules for 135 test cases out of 160 within 12 hours. Even in case where optimal schedules were not found within 12 hours, our algorithm found better schedules than state-of-the-art heuristic algorithms.
Keywords: task scheduling; multicore; data parallelism; branch-and-bound.
Traffic flow combination forecasting method based on improved LSTM and ARIMA
by Boyi Liu, Jieren Cheng
Abstract: Traffic flow forecasting is hot spot research of in intelligent traffic system construction. The existing traffic flow prediction methods have problems such as poor stability, high data requirements, or poor adaptability. In this paper, we define the traffic data time singularity ratio in the dropout module and propose a combination prediction method based on the improved long short-term memory neural network and time series autoregressive integrated moving average model (SDLSTM-ARIMA), which is derived from the Recurrent Neural Networks (RNN) model. It compares the traffic data time singularity with the probability value in the dropout module and combines them at unequal time intervals to achieve an accurate prediction of traffic flow data. Then, we design an adaptive traffic flow embedded system that can adapt to Java, Python and other languages and other interfaces. The experimental results demonstrate that the method based on the SDLSTM-ARIMA model has higher accuracy than the similar method using only autoregressive integrated moving average or autoregressive. Our embedded traffic prediction system integrating computer vision, machine learning and cloud has the advantages such as high accuracy, high reliability and low cost. Therefore, it has a wide application prospect.
Keywords: traffic flow forecasting; LSTM; embedded system; depth learning.
Energy-aware fixed-priority scheduling for periodic tasks with shared resources and I/O devices
by Yiwen Zhang
Abstract: Many researches have focused on energy management for the processor. However, DPM via I/O device scheduling for real time periodic tasks with shared resources has drawn little attention. We address the problem of the I/O device energy consumption minimisation for shared resources. We present an energy-aware I/O devices fixed-priority scheduling algorithm based on RM scheduling. It contains two parts: job scheduling and device scheduling. Job scheduling ensures that each task can complete its execution within its deadline. Device scheduling decides to turn off devices to save energy. The experimental results show that it can achieve significant energy savings.
Keywords: I/O device scheduling; energy management; real time scheduling; embedded systems.
A hybrid optimisation algorithm based on genetic algorithm and ACO algorithm improvements for routing selection in heterogeneous sensor networks
by Mei Wu, Ning Cao, Haihui Wang, Lina Xu, Guofu Li
Abstract: Wireless sensor applications have been pushed to the forefront in last several years mostly owing to the advert of the internet of networks. Using genetic algorithms and ant colony optimisation algorithms, many achievements have been made on engineering design problems, but the results optimised by these methods are often not satisfied by wireless sensor applications. In this paper, GAAC, a hybrid routing algorithm, is designed aiming to the defects of simple genetic algorithms and ant colony algorithms. With improvement, simulation results show that GAAC has great effects on convergence precision.
Keywords: routing; clustering; genetic algorithm; improvement; sensor networks.
A survey of rainfall forecasting using artificial neural network
by Qi Liu, Yanyun Zou, Xiaodong Liu, Nigel Linge
Abstract: Rainfall has a great impact on agriculture and peoples daily travel, so accurate prediction of precipitation is well worth studying for researchers. Traditional methods like numerical weather prediction (NWP) models or statistical models cant provide satisfactory results of rainfall forecasting because of nonlinear and dynamic characteristics of precipitation. However, artificial neural network (ANN) is able to obtain complex nonlinear relationship between variables, which is suitable to predict precipitation. This paper mainly introduces background knowledge of ANN and several algorithms using neural networks applied to precipitation prediction in recent years. It is proved that neural networks can greatly improve the accuracy and efficiency of prediction.
Keywords: rainfall; prediction; precipitation forecasting; neural networks; ANN; training algorithms.
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.
IBBO-LSSVM-based network anomaly intrusion detection
by Peng Zhou, Wenkuang Chou
Abstract: Owing to the variety and complexity of network intrusion, the traditional network anomaly intrusion detection model cannot accurately classify and identify the abnormal intrusion behaviour of the network, resulting in poor performance when detecting the network anomaly intrusion. In order to improve the performance of network intrusion detection, we propose a novel network anomaly intrusion detection method by means of IBBO-LSSVM. In this paper, the least squares support vector machine is applied to model and analyse the network abnormal intrusion detection, which can capture the relationship between network anomaly intrusion types and their corresponding features. Then, an improved biogeography-based approach is applied to optimise the parameters of the network intrusion detection model. Finally, the model is simulated and evaluated on a standard network anomaly intrusion test database. The accuracy of the network anomaly intrusion detection for the proposed method is higher than 90%, demonstrating that the proposed approach is superior to the traditional methods.
Keywords: abnormal network behaviour; intrusion detection; modelling and analysis; improved biogeography-based optimisation; support vector machine.
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.
Secure data deletion in cloud storage: a survey
by Minyao Hua, Yinyuan Zhao, Tao Jiang
Abstract: With the rapid development of cloud computing, individual people, firms, industries and governments are moving their data to the cloud to meet the data explosion
challenge. Secure data deletion is becoming a hot issue in cloud storage research. Different from traditional data deletion, the securely deleted data should be non-recoverable. In other words, executing secure data deletion can make the data completely erased. For private cloud storage, the application of traditional secure data deletion solution is relatively easy. However, it becomes challenging for public cloud storage because the cloud users lose the physical control over their data, where the lazy, selfish or malicious cloud storage service providers may not completely delete the requested data. In this paper, we present a survey of current secure data deletion technologies and compare them for both private cloud storage and public cloud storage. For private cloud storage, we introduce the mainstream secure data deletion technologies that can be classified into the physical destruction and the disk replication. For public cloud storage, we analyse the existing secure data deletion methods, such as the secure data deletion based on balanced tree, secure data deletion based on trusted third parties, policy-based secure deletion and so on, in accordance with the two aspects of verifiable secure data deletion and verifiable non-recoverability of data. Finally, we analyse the deficiencies among current researches and propose some future directions for improvement in the application of secure data deletion.
Keywords: cloud storage; secure data deletion; private cloud; public cloud.
A randomized Kaczmarz method based matrix completion algorithm for data collection in wireless sensor networks
by Ying Wang, Guorui Li, Sancheng Peng, Cong Wang, Ying Yuan
Abstract: This paper proposes a novel matrix completion algorithm for data collection in wireless sensor networks through incorporating a randomised version of the Kaczmarz method. By splitting the matrix completion problem into two convex sub-problems and solving the optimal probability computing problem in the randomised Kaczmarz method approximately with the D-Optimal Design solution, we reduce the reconstruction error and accelerate the convergence speed of the matrix completion computation. The synthetic data experiments show that the proposed algorithm presents more accurate reconstruction accuracy and faster reconstruction speed than the state-of-the-art matrix completion algorithms. Furthermore, we verify the practicality of the proposed matrix completion algorithm in real data collection scenario of wireless sensor networks through the experiments based on the real sensed dataset.
Keywords: wireless sensor networks; data collection; matrix completion; randomized Kaczmarz method; optimization; reconstruction.
Design for the external frame of a resonant accelerometer sensor
by Jing Li, Jing Jiang, Yunchan Zhou, Pengfei Guo, Xinze Li, Dongchen Xu
Abstract: In this paper, a model of the resonant accelerometer is studied in order to improve its sensitivity and quality factor. We establish both the mechanical and mathematical models of the accelerometer, and analyse the system energy dissipation. Some conclusions are obtained for decoupling the internal structure and the external frame, which can help to improve the performance of the system. Simulation results show that the choice of the structure parameters can influence the energy dissipation of the system greatly, and all of this can be establish the theoretical basis of designing a high quality resonant accelerometer.
Keywords: resonant accelerometer; external frame; energy dissipation; quality factor.
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: 3PGCIC 2014 The Challenges of Pervasive Computing Security and Intelligence
Static compliance checking beyond separation of duty constraints
by Yang Bo, Chunhe Xia, Yang Luo, Qing Tang
Abstract: Compliance requirements, such as separation of duty and binding of duty, have
to be satisfied in many application domains. Existing compliance checking frameworks
either have limited expressiveness or rely on model checking, which has a small applicable range and low efficiency. To overcome these limitations, we improve high level expression separation of duty algebra to (1) describe both SoD and BoD constraints to make it more expressive; (2) to describe a user-task relationship to perform compliance checking. In order to make the improved high level policy act on the concrete process, we (1) construct mapping rules to translate the improved high level policies to low level constraints described in description logic; (2) propose a reasoning framework to check for business process compliance. We report on the applicability of our approach via a case study.
Keywords: business process compliance; high level policy; low level constraint; description logic; framework.
An algorithm of video network transmission based on unbalanced multiple description coding
by ShanGuo Lv, YiQin Cao
Abstract: The drop-rate and net delay during video network transmission are two important factors in ensuring video network transmission quality. The purpose of network congestion control is to reduce the bad quality impact of video network transmission caused by net delay and the drop-rate. This paper proposes an algorithm of video network transmission based on unbalanced multiple description coding, which is capable of quickly recovering from packet losses and ensuring continuous playback, and furthermore is adaptive to both multiple path and single path transmission. A Markov model was employed to predict the state of network congestion based on available bandwidth detection, and the video transmission path was changed on the basis of the prediction. Experiments show that, compared with the RED algorithm, the algorithm is much more effective to estimate network congestion, reduce video packet loss-rate and net delay, thus the video network transmission quality can be ensured more effectively.
Keywords: unbalanced multiple description coding ;bandwidth detection ;Markov model.
Study on the local path planning for intelligent vehicles based on an improved VFH method
by Yingwei Yan, Yu Du, Wenan Zhou
Abstract: A local path planning method has been proposed and implemented for intelligent vehicles, the vector field histogram sharp (VFH#). In this study, an intelligent vehicle was made to manoeuvre through obstacles by the VFH# method. This local path planning method is the result of an improvement on a method called VFH (Vector Field Histogram). The VFH method is sensitive to the threshold value for determining candidate sectors. It is difficult for an intelligent vehicle to travel through narrow corridors by the VFH method. The VFH# method enlarges obstacles to avoid collisions. It also designs a new means of acquiring the magnitude of the obstacle vector to eliminate sensitivity. In each sector, it chooses the largest magnitude as the resistance value among obstacle vectors. The method is applied to an electric vehicle to verify the performance, and it achieves an excellent result.
Keywords: local path planning; vector field histogram sharp; intelligent vehicle
Special Issue on: PCC 2014 Frontiers in Pervasive Computing
Energy-aware list-based scheduling for parallel applications in cloud
by Yongxing Liu, Kenli Li, Zhuo Tang, Keqin Li
Abstract: As the growth of energy consumption has been explosive in current data centres and cloud systems, it has drawn greater attention in academia, industry and government. Task scheduling is core to systems, it has become an important method to reduce energy dissipation. This paper proposes an Energy Aware List-based Scheduling algorithm called EALS for parallel applications in the context of Service Level Agreements (SLA) on cloud data centres. First, the EALS algorithm comprehensively considers the high power processors to minimise the number of high power processors used. Then, the algorithm tries to migrate some tasks from a high power processor to a low power processor for energy saving. Finally, the EALS algorithm takes a more efficient way to assign the time slots among tasks based on the dynamic voltage scaling (DVS) technique. To demonstrate the effectiveness of the EALS algorithm, randomly generated graphs and several real-world applications are tested in our experiments. The experimental results show that the EALS algorithm can save up to 43.96% energy consumption for various parallel applications as well as balance the scheduling performance.
Keywords: cloud data centre; directed acyclic graph; dynamic voltage scaling; energy-aware scheduling; service level agreement.
User similarity based gender-aware travel location recommendation by mining geotagged photos
by Zhenxing Xu, Ling Chen, Haodong Guo, Mingqi Lv, Gencai Chen
Abstract: The popularity of camera phones and photo-sharing websites, e.g. Flickr and Panoramio, has led to huge volumes of community-contributed geotagged photos, which could be regarded as digital footprints of photo takers. Thus, mining geotagged photos for travel recommendation has become a hot topic. However, most existing work recommends travel locations based on the knowledge mined from photo logs (e.g. time, location), and largely ignores the knowledge implied in the photo contents. In this paper, we propose a geotagged photos mining-based personalised gender-aware travel location recommendation approach, which considers both photo logs and photo contents. Firstly, it uses an entropy-based mobility measure to classify geotagged photos into tour photos or non-tour photos. Secondly, it conducts gender recognition based on face detection from tour photos. Thirdly, it builds the gender-aware profile of travel locations and users. Finally, it recommends personalised travel locations considering both user gender and similarity. Our approach is evaluated on a dataset, which contains geotagged photos taken in eleven cities of China. Experimental results show that our approach has the potential to improve the performance of travel location recommendation.
Keywords: geotagged photos; gender recognition; travel location recommendation
An abstraction layer enabling pervasive hardware-reconfigurable systems
by Alessandro Cilardo, Nicola Mazzocca, Paolo Prinetto
Abstract: Field-programmable gate array technologies are creating a new range of challenges for pervasive and ubiquitous systems. Revisiting and extending approaches borrowed from the purely software domain is a fundamental opportunity in this scenario. In particular, this paper addresses code mobility, a well-established approach used to dynamically adapt a distributed system based on the actual application needs, and extends it to a deep code mobility concept, allowing "logical" hardware components to be migrated across a pervasive infrastructure. The work presents the architecture and the prototype implementation of a reconfigurable computing framework providing full support to deep code mobility through an abstraction layer which exposes a portable view of the underlying reconfigurable hardware. The paper then thoroughly discusses two application scenarios, hardware-accelerated distributed data mining and autonomous online testing, confirming the impact of deep code mobility in real-world pervasive computing contexts.
Keywords: pervasive computing; ubiquitous systems; code mobility; adaptive systems; reconfigurable computing; field programmable gate array.
Scheduling deadline-constrained scientific workflow using chemical reaction optimisation algorithm in clouds
by Chaokun Yan, Huimin Luo, Zhigang Hu
Abstract: The advent of cloud computing as a new model of service provisioning in distributed systems encourages researchers to investigate its benefits and drawbacks on executing scientific applications such as workflows. One of the most challenging problems in clouds is workflow scheduling, i.e., the problem of satisfying the QoS requirements of the users as well as minimising the cost of workflow execution. In this paper, a novel meta-heuristic method, called chemical reaction optimisation (CRO), is developed to solve deadline-constrained workflow scheduling, which tries to minimise the cost of workflow execution while meeting a user-defined deadline. A set of appropriate parameter can be obtained based on orthogonal experimental designs (OED) and factor analysis. Experiments are done in two real workflow applications, and the results demonstrate the effectiveness of the proposed algorithm.
Keywords: cloud; SaaS; deadline; scientific workflow; chemical reaction optimisation
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: ICNC-FSKD'15 Theory and Practice on Internet of Things
Using zero moment point preview control formulation to generate nonlinear trajectories of walking patterns on humanoid robots
by Yunn-Lin Hwang, Thi-Na Ta, Kun-Nan Chen
Abstract: This paper design a desired Zero Moment Point (ZMP) trajectory and create a Centre of Mass (CoM) trajectory based on this ZMP, in order to solve the problem of generating nonlinear trajectories of walking pattern in stability a humanoid robot. The preview control formulation which follows the planned ZMP patterns while reducing the jerk (acceleration derivation) is considered to implement this trajectory. The walking pattern for a bipedal robot is generated by using a cart-table model. After that, the hip and foot trajectories planning that make the robot walk smoothly on the different ground conditions are proposed by using the cycloid function. According to these trajectories, the position of each leg joint of humanoid robot could be determined by inverse kinematics. Then, computer simulations are performed by using RecurDyn and MATLAB softwares to validate the proposed preview control formulation for the nonlinear walking patterns generation of a Bioloid humanoid robot. Finally, an experimental analysis with the real Bioloid robot is also described in this study.
Keywords: centre of mass; humanoid robot; zero moment point; cart-table model; preview control; CoM Jacobian.
An adaptive denoising method for colour images of mobile phone based on bivariate shrinkage function
by Xuehui Wu, Xiaobo Lu
Abstract: The photography function of mobile phones has become the current trend, and the image quality has become our focus. The requirements for image denoising performance of mobile phones are higher and higher. This is different from traditional image denoising because mobile phone image denoising needs faster speed, less computing space and time, and higher efficiency. Wavelet transform denoising algorithm based on Bayesian theory can meet the denoising demand of mobile phone images because of its rapidity, validity and so on. However, denoising by wavelet transform is optimal for Gaussian noise, and the actual noise of images taken by mobile phones does not completely conform to Gaussian distribution, and then a down-sampling method is adopted to simulate the Gaussian noise. In this article, according to the actual requirements of mobile phone image denoising, an adaptive denoising method for colour images of mobile phones based on bivariate shrinkage function is proposed. Firstly, the actual image noise was made similar to the Gaussian noise by the down-sampling method, and then a kind of adaptive noise variance of the bivariate shrinkage function based on Bayesian denoising method is proposed to estimate the variance of the down-sampled image. Finally, the denoised image was grayed to further eliminate the noise in the flat areas. Simulation and actual experimental results showed that the method of this paper can get better denoising effect than other methods.
Keywords: mobile phone image; denoising; down-sampling; bivariate shrinkage function; noise variance; graying.
Special Issue on: ICICS 2016 Advances in Humanised Computing Systems and their Applications
A system for application development using aerial robots
by Mohammad Fraiwan, Ahmad Alsaleem, Hashem Abandeh, Omar Al-Jarrah
Abstract: Autonomous airborne systems have generated a lot of interest in civilian as well as military applications. The application development process for such systems is inherently expensive and is prone to costly errors. The majority of the developed applications involve routing and navigation toward targets coupled with obstacle avoidance. In this paper, we present the approach and algorithms that were employed to tackle the routing and avoidance problems. The proposed solutions were applied to land-based robots as well as a quad-rotor aerial system. The quad-rotor flight path navigation and routing was programmed based on GPS and on-board measurement data. Obstacle avoidance was implemented based on an algorithm that relies on the idea of a virtual potential field. Kalman filters were implemented to improve the accuracy of the measured data, while 3D visualisation was used to visually identify obstacles. In the case of in-building reconnaissance, where GPS signals are very weak and largely useless, data from laser sensors and proximity feeds was aggregated and processed to identify obstacles and the topology of the surrounding environment. The organisation and interaction of these various modules is described in detail.
Keywords: quad-rotor; obstacle avoidance; Kalman filter; maps; potential field.
Petri net-based verification of security protocol implementation in software evolution
by Mohd Anuaruddin Bin Ahmadon, Shingo Yamaguchi, Brij Gupta
Abstract: Implementation of security protocols in software plays an important role to protect the whole system from vulnerabilities. In order to protect the system from new threats, software needs to adapt to new security requirements. Thus security upgrades and patches are implemented to the software. Previous works only focus on logical correctness of security protocols, but we focus on the successful implementation of security protocols in a program. A program evolves as programmers apply security patches to its source code. Hence, the process of verifying important security protocol implementation is difficult. In this paper, we propose model-driven security verification throughout software evolution. It consists of two major methods: (i) a reverse engineering method to translate a program into a Petri net model; and (ii) a model-driven verification method to confirm that the security protocol implementation is valid. Concretely, for a program X that implements a security protocol specification A, does its derivation Y also implement A? The answer is yes if Y inherits the behaviour of X. We applied behavioural inheritance analysis to verify security protocol implementation. We also illustrated the methods with an example in software evolution.
Keywords: software evolution; reverse engineering; security protocol; Petri net; behavioural inheritance.
Knowledge-based auto-configuration system using ubiquitous robotics for services delivery in smart homes
by Mustafa Al-Khawaldeh, Xi Chen, Philip Moore, Ibrahim Al-Naimi
Abstract: The wide availability of services and devices within contemporary smart home environments make their management a challenging and rewarding task. Maintaining complex smart home systems throughout their lifecycle entails considerable resources and effort. These challenges have stimulated the need for dynamic auto-configurable services amongst such distributed systems. This research aims to devise methods to automate the activities associated with customised service delivery for dynamic home environments by exploiting recent advances in the field of ubiquitous robotics and Semantic Web technologies. It introduces a novel approach called the Knowledge-based Auto-configuration Ubiquitous Robotics for Smart Home Environments, which uses the Sobot to achieve auto-configuration of the system. A proof-of-concept testbed has been designed, implemented and validated via several case studies. The results show that the Sobot is capable of creating and executing new or alternative feasible configurations to meet the systems goal by using inferred facts based on the smart home ontological model, so that the system can adapt to the changed environment.
Keywords: smart home; ubiquitous robotics; service delivery; Semantic Web; knowledge-based auto-configuration systems.
Special Issue on: Learning for Robotics
Conditional proxy broadcast re-encryption with fine grain policy scheme for cloud data sharing
by Sun Maosheng, Ge Chunpeng, Fang Liming, Wang Jiandong
Abstract: Conditional proxy broadcast re-encryption (CPBRE) enables a semi-trusted proxy to convert Alice's ciphertext satisfying a certain condition into a set of users' ciphertext. The proxy, however, cannot learn anything about the underlying plaintext. The traditional CPBRE schemes cannot support flexible control on conditions. In this paper, we present a new primitive named conditional proxy broadcast re-encryption with fine grain policy (CPBRE-FG). In a CPBRE-FG scheme, the re-encryption key is generated with an access tree, and the ciphertext is constructed under a set of descriptive conditions. The proxy can convert a ciphertext if and only if the set of conditions satisfies the access tree. We formalise the notion of CPBRE-FG and present an efficient CPBRE-FG scheme. Finally, we prove its effectiveness against chosen-ciphertext attack (CCA) security in the random oracle model.
Keywords: conditional proxy re-encryption; proxy broadcast re-encryption; access policy; random oracle model.
Special Issue on: ICEDSA-2016 Advances in Electrical Engineering and Computer Science Systems and Applications
Analysing and modelling worm propagation speed in the smart grid communication infrastructure
by Anas AlMajali, Waleed Dweik
Abstract: In an Advanced Metering Infrastructure (AMI), smart meters may communicate in an ad-hoc fashion to perform functionalities such as remote metering and demand response. However, the AMI is susceptible to cyber-physical threats caused by malware such as worms. In this work, we derive a probabilistic model for worm propagation in the AMI. The accuracy of the model is validated using three worm propagation scenarios. This model can be used to estimate the time required to infect $N$ meters in the AMI, based on the worm propagation technique deployed by the attacker. In addition, we use the derived model to investigate the sensitivity of worm propagation to different parameters, such as transmission range, worm size, number and location of target meters. We also demonstrate how an attacker can improve the propagation speed of the worm by modifying the worm's target list. The latter is done by comparing the three introduced propagation scenarios. Improving the worm's propagation speed can amplify the possible physical consequence of the attack.
Keywords: smart grid; cyber-physical security; advanced metering infrastructure.
Design of embedded atrial fibrillation detection scheme for wireless body area networks
by Manan AlMusallam, Adel Soudani
Abstract: Wireless Body Sensor Networks (WBSNs) and wearable technology are the new trends in healthcare applications. This technology can provide real-time monitoring of the patients biosignals and health condition. In this context, the analysis of ECG signals, reflecting the heart activity, is considered as a key tool in diagnosing cardiac disorders such as Atrial Fibrillation (AF) that can lead to strokes and heart failure. Classical approaches for sensor-based AF detection require continuous transmission of ECG signals to a remote server, which can rapidly exhaust the sensor energy and shorten the lifetime of the application. In this paper, we propose a new low-power scheme for AF episodes detection in the ECG signal that is intended for implementation in WBSN. The paper details the design of this scheme and demonstrates its high accuracy for AF detection and shows that it saves 93% of energy.
Keywords: WBSN; ECG signal processing; features extraction; atrial fibrillation.
Neural network based study of PV panel performance in the presence of dust
by Moh'd Sami Ashhab, Omar Akash
Abstract: Neural networks are used to study PV solar panel performance dependence on accumulated dust and tilt angle. For every location in the world there is an optimum tilt angle of dust-free PV panels for best performance. However, in some areas dust is an obstacle for PV panel operation. Examples of such areas are the Middle East and the Arabian Gulf countries. The accumulation rate of dust depends on the location as well as the tilt angle of the PV panel. Relevant data are collected for two identical sets of PV panels in the city of Ras Al Khaimah in UAE, where significant amounts of dust are present. Each set includes four different PV panels with various tilt angles. One set is kept clean while the other set is left unclean. The data includes information about PV panel cleaning condition, tilt angle, date and output power. Available data are used to train a neural network model that predicts the PV panel power under the given conditions. Furthermore, the neural network model serves as a key for adjusting the tilt angle of the PV panels at an optimum value for best performance, taking into account the dust factor.
Keywords: neural networks; optimisation; PV panels; tilt angle; dust.
An efficient fuzzy logic-based and bio-inspired QoS-compliant routing scheme for VANET
by Mohammed El Amine Fekair, Abderrahmane Lakas, Ahmed Korichi, Nasreddine Lagraa
Abstract: Vehicular ad hoc networks (VANET) are increasingly gaining popularity among researchers for their capacity to enable a wide range of applications associated with road traffic safety, infotainment and road transportation management. However, for real-time and multimedia applications to be successfully deployed in a vehicular network environment, certain quality of service (QoS) requirements needs to be met. In this paper we present a QoS-based routing protocol named FBQoS-Vanet which purpose is to accommodate various applications and services with specific QoS requirements. QoS-based routing over a vehicular ad hoc networks consists of finding routes that satisfy multiple QoS constraints simultaneously. These routes have to be effective in routing and delivering data of various real time and non-real time applications. We propose a protocol, which uses a bio-inspired Artificial Bee Colony (ABC) approach for discovering routes that respond to multi-QoS criteria. In addition, we use fuzzy logic based method to identify a feasible path among several available ones that are discovered by ABC for different traffic classes. In this method, we rely on the evaluation of a composite fuzzy cost expressed in terms of the QoS metrics, where the path must satisfy multiple criteria such as bandwidth, delay, jitter, and link expiry time. In this paper we present the architecture and the design of our protocol, and present the performance results that show the benefits of using our scheme for the purpose of routing various kind of traffic in VANET with different QoS requirements.
Keywords: vehicular ad-hoc network; quality of service; fuzzy logic; artificial bee colony; bio-inspired; ad-hoc networks.
Radiofrequency and microwave spectroscopy investigation of bacteria solutions: determination of the aggregation threshold.
by Mustapha Merabet
Abstract: This paper presents novel approaches to assess and characterise the biomass concentration of bacteria in a culture medium. It is based on the use of the colloid inductive permitivity probe, which is not affected by the electrode polarisation, and on the analysis of the distribution of relaxation processes. The dielectric methods probe the interactions between the membranes of the cells and the applied electric fields in the radiofrequency and the dipolar interactions in the microwave frequency ranges. They allow the characterisation of each type of cell with a specific relaxation process and the estimation of the dipole-dipole interactions. The observed dielectric relaxation induced by the presence of bacteria in the medium is thoroughly investigated and the specific relaxation frequency is used as fingerprint of the cell strain, while the relaxation amplitude is related to its concentration. Specifically, the frequency-dependent dielectric responses of Lactobacilli in DMEM are investigated in the [75 kHz to 30 MHz] and in the [130 MHz to 20 GHz] ranges. The dielectric method is tested against biological standard methods, and used successfully to predict concentration of Lactobacilli in adverse conditions. Moreover, because of the direct measurement over large concentration ranges, the concentration threshold of the bacteria aggregation can be precisely identified.The transition from a first linear evolution to a second one having a different slope indicates a drastic changes in the interactions between cells and the electric field, which can be attributed to the cell aggregation. As a matter of fact, the behaviour of the amplitude of the medium-frequency relaxation process shows two distinct linear evolutions with an intersection corresponding to the aggregation threshold. The dielectric method can be easily adapted to the online monitoring of the growth of biological cells and to the control of the fermentation processes.
Keywords: aggregation; biological cells; biological process; biomass; conductivity; conductivity measurement; dielectrics; dielectric measurement; dielectric spectroscopy; electromagnetic absorption; materials testing; microwave measurement; permitivity.
Point clouds reduction model based on 3D feature extraction
by Hadeer Mostafa, Shereen Taie, Reda Elkhoribi, Ibraheem Farag, A.K. Helmy
Abstract: Light Detection and Ranging (LIDAR) is a remote sensing method that scans the Earths surface with high density to construct the digital elevation model. In this paper, we present a point clouds reduction model based on two 3D feature extraction techniques, namely the sharp feature detection algorithm and feature extraction technique based LIDAR point attributes. These techniques are used as initial selection criteria and are compared with the maximum and the minimum elevation criteria that give reduction with the highest accuracy. However, point clouds reduction algorithms lead to high consumption of time to generate a reduced file with high accuracy, which causes the need to propose a new model that considers the trade-off between the processing time and the accuracy. The results showed that the proposed model significantly reduced the processing time at the expense of accuracy reduction by 0.7% and 1.3% for the two used techniques respectively, which is acceptable for realistic applications.
Keywords: light detection and ranging; digital elevation model; 3D features extraction; radial basis function; data reduction.
Designing a wireless sensor with ultra-capacitor and PV microcell for smart building energy management
by Fernando Del Ama Gonzalo, Belen Moreno, Juan A. Hernandez
Abstract: Building owners face a number of challenges, as they search for ways to reduce energy consumption, lower operating costs, and manage modern technology and systems. A research group from Aerospace School of Madrid has addressed these aims by developing a real-time Building Management System that consists of a plurality of transmitter nodes, a central controller, and a user interface. Monitoring, control, and actuation systems lack reliable and cost effective data logging and measuring devices of energy parameters in buildings. In this paper, a new concept of wireless sensors is recommended, powered by PV microcells and electric double layer capacitors. With these power sources, the sensor can send data every minute without being affected by the absence of sunlight. The feasibility of such a sensor was studied empirically and the findings are reported in this paper.
Keywords: wireless sensors; ultra-capacitor tests; smart home energy management.
Adaptive FIR filter for frequency and power estimation of sinusoids
by Fadia Elissa, Mohammad Mismar
Abstract: A new frequency and power estimation method which detects frequencies of an unknown number of source sinusoidal signals is suggested. The system contains an adaptive finite impulse response filter (FIR) that exploits one of several random search algorithms, particle swarm optimisation (PSO). PSO will work on minimising the overall output power by finding the frequencies of the roots on the unit circle. The pseudo-spectrum is achieved by frequency elimination of the roots, and the real power spectrum is derived from the pseudo-spectrum and the filter polynomial. The frequency, real power, and number of source signals are all estimated from the real power spectrum. After estimating the frequencies of the undesired interfering signals, the system can then work on suppressing these signals and maintaining the strength of the desired signals.
Keywords: frequency estimation; signal power estimation; particle swarm optimisation.
Experimental evaluation of various modified Smith predictor-based fractional order control design strategies in control of a thermal process with time delay
by Necdet Sinan Özbek, İlyas Eker
Abstract: In this study, a number of modified Smith Predictor (SP)-based fractional order control strategies are investigated experimentally on a thermal process. Controller design methods and tuning strategies are elaborated step by step. Further, control strategies are discussed in relation to design specifications, control cost, implementation issues, and operating modes encountered in practice. Performance comparison is presented via several illustrations and numerical measures. Particular attention is paid to the tracking precision, quality of the control signal, robustness against disturbances and energy efficiency. Complementary comments are addressed based on merits and demerits of each control technique.
Keywords: time delay systems; Smith predictor; fractional order control; fuzzy logic.
Special Issue on: Smart X 2016 Pervasive Computing for Smart Life
An improved human physiological simulation model for healthcare applications
by Liang Yu, Nan Jia, Ruomei Wang, Jiao Jiao, Qingzhen Xu
Abstract: Health care becomes more and more important in modern society. In order to prevent some health symptoms occurring in daily life, it is important to develop an efficient model to simulate the human physiological performance for predicting and reducing accidents such as dehydration, exertional heatstroke, syncope, even sudden death and so on. In this paper, a novel human physiological computer simulation model is introduced. A nonlinear heart rate regulation model and a two-node thermal regulation model are integrated together to simulate the human physiological indices such as core temperature, dehydration amount and heart rate. Experiment results show that the proposed physiological simulation model can well simulate the human physiological mechanisms, and some important numerical computation results predict the same trends as the experimental measurements. These simulation results can be used to analyse human physiological symptoms and assist health risk assessment.
Keywords: computer simulation; human physiological model; health care; thermal regulation.
An improved incomplete AP clustering algorithm based on K nearest neighbours
by Zhikui Chen, Yonglin Leng, Yueming Hu
Abstract: With the fast development of the Internet of Things (IoT), a large amount of missing data is produced in the process of data collection and transmission. We refer to these data as incomplete data. Many traditional methods use imputation or discarding strategy to cluster incomplete data. In this paper, we propose an improved incomplete Affinity Propagation (AP) clustering algorithm based on K nearest neighbours (IAPKNN). IAPKNN firstly partitions the dataset into complete and incomplete dataset, and then clusters the complete dataset by AP clustering directly. Secondly, according to the similarity, IAPKNN extends the responsibility and availability matrices to the incomplete dataset. Finally, clustering algorithm is restarted based on the extended matrices. In addition, to address the clustering efficiency of a large scale dataset, we give a distributed clustering algorithm scheme. Experiment results demonstrate that IAPKNN is effective in clustering incomplete data directly.
Keywords: incomplete data; affinity propagation clustering; K nearest neighbours.
A PID-FEC mechanism using cross-layer approach for video transmission over multi-hop wireless networks
by Longzhe Han, Xuecai Bao, Hongying Yu, Huasheng Zhu, Tanghuai Fan, Jia Zhao, Yeonseung Ryu
Abstract: Multi-hop Wireless Networks (MWNs) provide an important infrastructure for ubiquitous multimedia content access. MWNs consist of multiple wireless links, and each link may suffer channel fluctuation, signal fading, hidden terminal, and other imperfections. The accumulated packet losses greatly decrease the quality of multimedia services, particularly video streaming services. In order to overcome packet losses in MWNs, in this paper we propose a Proportional Integral Derivative control-based Forward Error Correction (PID-FEC) mechanism to improve the quality of video streaming services. The traditional FEC schemes require feedback information from the receiver in order to calculate redundant rates. In MWNs, the transmission delay and various multi-link statuses, however, limit the accuracy of feedback information for representation of real-time network conditions. Our proposed method adopts the cross-layer approach, and leverages the functionalities of different network layers. The Automatic Repeat reQuest (ARQ) on the Media Access Control (MAC) layer is used as an indicator of packet losses. With the packet loss information, the redundancy rates are adaptively regulated based on the PID control algorithm. Since the FEC feedback control mechanism is implemented on each wireless node, it does not generate any unnecessary FEC packets, supposing that the original packets are successfully received. Experimental results, presented herein, show that the PID-FEC achieves a better quality of video streaming, as well as a higher FEC efficiency, as compared with conventional FEC schemes, over a variety of network conditions.
Keywords: forward error correction; cross-layer; video streaming; multi-hop wireless networks.
An iterative shrinkage threshold method for radar angular super-resolution
by Xin Zhang, Xiaoming Liu, Chang Liu, Zhenyu Na
Abstract: This paper proposes a fast Iterative Shrinkage Threshold (IST) method for improving radar angular resolution. Based on radar signal processing theory, the implementation of angular super-resolution is equivalent to restoring the radar's target angular information without changing the radar's working system. In this method we first establish a convex quadratic programming model by orthogonalising the antenna pattern matrix, which transforms the radar angular super-resolution problem into a constrained optimisation problem. Consequently, the restored angular information can be regarded as the optimal solution of a convex quadratic programming model. Then, the IST algorithm is employed by modifying the residual at each iteration to find this optimal solution of the model. The advantage of this method is to overcome the shortcoming of the ringing effect at a low signal to noise ratio (SNR), and the ill-posed problem existing in those classical super-resolution methods is addressed effectively. Simulations further confirm our theoretical discussion, and manifest that a desirable resolution performance is gained and comparisons of signal to restoration error ratio provide an amazing result that our method is superior to other methods in terms of efficiency while the SNR is less than 20 dB.
Keywords: radar; super-resolution; constrained optimisation; iterative shrinkage threshold.
The power big data based energy analysis for intelligent community in smart grid
by Yiying Zhang, Kun Liang, Yeshen He, Ying Liu
Abstract: A smart grid deploys large numbers of intelligent terminals, to monitor or control the operating status and improve the energy efficiency and functional applications. In the intelligent community and smart industrial park, we deployed a variety of internet of things sensors to carry out applications for business users, ordinary users, and commercial users, and conducted an energy analysis. In this paper, we study the power efficiency problem of intelligent cells based on the power big data, and present the system architecture and key algorithm. We first propose a business intelligence architecture based on cloud computing for intelligent community (industrial park), which can meet the requirements of efficient storage and analysis of massive data. Then, we establish a multivariable, multi-dimensional intelligent electricity energy analysis model that improves the orderly power consumption efficiency. Meanwhile, based on Hadoop, HBase, Hive etc., we realise the ETL, OLAP, data mining and BIReport functions. We also present a novel parallel algorithm to achieve the data mining algorithms and data analysis algorithms, and solve the issue of processing speed of large-scale data analysis. We have deployed the business intelligence system in Gansu Province, Beijing, Shanghai and other areas of China. In terms of energy efficiency, it can save more than 8% costs. We focused on the user characteristics and electricity consumption, and analysed the energy efficiency and energy-related parameters. The analysis results guided the efficient use of electricity, and increased the functions of the intelligent community and the intelligent life preliminarily.
Keywords: power big data; intelligent community; smart grid; power cloud.
The attack efficiency of PageRank and HITS algorithms on complex networks
by Yangqian Su, Yunfei Yi, Qin Jun
Abstract: With the growing of the network scales, network attack strategies with high attack efficiency and computational efficiency are becoming more and more important. Various attack strategies with various sorting methods have been proposed. However, most of them neglected the computational efficiency. Inspired by the high computational efficiency of PageRank and HITS algorithms that are used in web pages network, this paper introduces those two algorithms to the network attack separately and tries to explore the feasibility of those two new strategies. The initial experiments choose Degree and BC attack as the contrast group, and compare the attack efficiency in six virtual networks. The results indicate that considering the computational efficiency and attack efficiency simultaneously, PageRank strategy has a better attack performance than other compared strategies. Initial discussions about the results are also given.
Keywords: PageRank algorithm; HITS algorithm; attack strategy.
Comprehensive vulnerability assessment and optimisation method of power communication network
by Chenchen Ji, Peng Yu, Wenjing Li, Puyuan Zhao
Abstract: Vulnerability assessment and optimisation for a power communication network can enhance the robustness and sustainability of the network. However, the current vulnerability assessment method lacks service and availability indicator considerations, and the corresponding optimisation method ignores dynamic processes for temporal factors as well. Aiming at this problem, a novel and comprehensive vulnerability assessment and optimisation method is proposed. Firstly, for vulnerability assessment, the influence factors are analysed from both static and dynamic aspects. Integrating these factors, a comprehensive vulnerability indicator is designed to assess the vulnerability of nodes and edges. And then, to relieve unbalanced vulnerability distribution in the network, a routing optimisation method is proposed through reconfiguration for service routes on the edge with high vulnerability. Finally, the simulation is taken under a real network. Vulnerability assessment with the defined indicator is executed, and the network vulnerability can be balanced with the optimisation method, which takes on effective theoretical and practical significance.
Keywords: power communication network; comprehensive vulnerability; vulnerability balance; routing optimisation.
BranChain: a novel chain-based routing protocol in wireless sensor networks
by Li'e Zi, Wanli Chen, Xingcheng Liu, Xiang Chen
Abstract: Owing to their excellent scalability and efficiency, hierarchical routing protocols including LEACH and PAGASIS have become hotspots in wireless sensor networks (WSNs) research. As an improvement of the LEACH protocol, PEGASIS reduces the total amount of data transmitted in WSNs and prolongs the lifetime of the WSNs. Being such an elegant solution to energy consumption, however, there are still three deficiencies with PEGASIS: (1) the inevitability of long link between some neighbour nodes owing to the local optimal result of adopting greedy algorithm; (2) the overhead of the ineligible cluster head (CH); and (3) the overhead and time cost of chain rebuilding whenever a node becomes invalid. In order to solve these problems, an improved protocol, called the BranChain, short for the branched chain routing protocol, is proposed here. In the proposed algorithm, a novel scheme is employed, in which a long link avoiding algorithm, a network topology re-adjustment strategy, and a CH re-election mechanism are combined. In the BranChain protocol, whenever a long link comes into being, the node ready to be originally connected is supposed to form a new independent branched chain with the greedy algorithm. When all nodes get connected in the chain, the system will connect all the independent branched chains together by searching for the optimal paths between each two of the branched chains. When the sensor nodes, except for end points, die, the two broken branched chains will be connected with the same algorithm as that of the optimal paths searching. Simulation results show that, compared with PEGASIS, the BranChain protocol can significantly prolong the network lifetime, which is vital to WSNs.
Keywords: wireless sensor networks; PEGASIS protocol; BranChain; energy efficiency; sensor nodes.
Special Issue on: Security of Mobile and Embedded Corporate Infrastructures in the BYOD Era
Privacy-preserved traffic forecast scheme for intelligent transportation system
by Shuzhen Pan, Yan Kong, Qi Liu
Abstract: Traffic forecast in intelligent transportation system (ITS) takes the responsibility of traffic conductor, traffic control and so on, which is an important part in our daily life. Nowadays, traffic forecast schemes in ITS have been widely studied by researchers in different countries. However, these traffic forecast schemes don't take users' privacy into consideration. Users' privacy information is always leaked to the infrastructures and other users in the same system, and this can cause great damage to the information owner. In this paper, a privacy-preserved traffic forecast scheme is proposed, for ITS, to solve the problem of traffic forecast and privacy leakage together. The proposed scheme is based on a recurrent neural network that is operated by the infrastructures. In addition, the infrastructures or other vehicles can get nothing about the sender's privacy from the data package sent by a target vehicle. Our simulation can prove the advantages of our scheme in terms of the forecast accuracy. The security analysis can prove the privacy-preserved property. At the end of simulation part, we give the detailed analysis on forecast accuracy and the fault tolerance of our traffic forecast scheme.
Keywords: intelligent transportation systems; traffic forecast; privacy preservation.
Special Issue on: IT4OD 2014 Information Technology for Organisation Development
An improved CUDA-based hybrid metaheuristic for fast controller of an evolutionary robot
by Nour EL-Houda Benalia, NourEddine Djedi, Salim Bitam, Nesrine Ouannes, Yves Duthen
Abstract: This paper proposes a novel parallel hybrid training approach to conceive an evolutionary robot. The proposed design aims to provide efficient behaviours to perform its tasks in a complex area, such as walking toward a hidden destination. Embedded in the robot brain, this training and evolution combination is typically accomplished by evolving considerable Recurrent Neural Networks (RNNs) using an Evolutionary Strategy (ES). The effectiveness of this proposal is improved by employing CUDA technology that executes the evolutionary process of RNNs in a parallel way. The modifications applied are intended to meet CUDA requirements in terms of CPU/GPU cooperation and memory management. Using a set of experiments performed by a GPGPU-based physical simulator named Open Dynamics Engine (ODE) and CUDA-based evolution, the effectiveness of the proposed parallel evolutionary training technique was validated for real movements of humanoid robots. This validation showed a promising speed-up, since this field requires very powerful computational resources.
Keywords: artificial life; robotics; parallel evolutionary algorithms; recurrent neural network; GPU.
Special Issue on: IEEE/IFIP EUC 2013 Embedded and Ubiquitous Computing
Energy-efficient analysis with end-to-end delay constraints in wireless sensor networks
by Gaocai Wang, Xinsheng Yu, Daofeng Li, Jin Ye
Abstract: Longer time sleep (low duty cycle) of sensor nodes with sleep-wake mechanism in wireless sensor networks will result in longer delay for transmission of data-collected, although it can reduce the nodes energy consumption and extend its lifetime. Therefore, it is an important and significant topic to balance energy consumption and delay in wireless sensor networks. In this paper, we mainly focus on an energy-efficient mechanism with end-to-end delay constraints. We divide input flows into multiple sub-flows in terms of a configurable weight of links in order to avoid excessive energy consumption of sensor nodes caused by forwarding too much collected data. So, collected data pass through multiple different links and sensor nodes to reach the sink node. We establish an analytic framework for end-to-end delay based on network calculus according to arrival process of flows and service process of sensor nodes. On the other hand, sensor nodes provide rate-latency guaranteed service for arrival flow based on their active time and the maximum capacity of links. The case study and numerical results show that the proposed method can provide an effectively balanced strategy between energy consumption and delay by adjusting the duty cycle of sensor nodes properly in wireless sensor networks.
Keywords: wireless sensor networks; network calculus; delay; duty cycle; energy consumption.