International Journal of Embedded Systems (91 papers in press)
Research on electric vehicle charging scheduling algorithms based on a 'fractional knapsack'
by Zhenzhou Wang, Xinyuan Li, Pingping Yu, Ning Cao, Russell Higgs
Abstract: The large-scale disorderly charging of electric vehicles creates challenges for the security of power systems, especially power distribution systems. To avoid peak power consumption during the day and improve the use rate of the power grid at night, a charging scheduling algorithm for electric vehicles based on a 'fractional knapsack' is proposed. Considering the constraints of the users' charging demand and charging system capacity, a charging model based on a fractional knapsack is established to optimise the peak-valley load difference and reduce load fluctuation and charging cost, which is the objective function. To verify the effectiveness of the proposed algorithm, the Monte Carlo method is used to simulate the charging demand of electric vehicles, and the disorderly charging and orderly charging scheduling are simulated and compared under a time-sharing tariff mode. The results show that the proposed scheduling algorithm improves the peak-valley difference of the power grid, reduces fluctuation in the power grid load, and improves the use rate of the power grid.
Keywords: fractional knapsack; electric vehicle; charging scheduling; peak-valley load difference.
Zero-error channel capacity of quantum 5-symbol obfuscation model
by Wenbin Yu
Abstract: The existing noise channel coding methods need to be optimised in terms of channel capacity and algorithm complexity. Based on the characteristics of the quantum 5-symbol confusion-channel model and the theory of matrices, a coding method is proposed that combines the 5-symbol confusion channel with the quantum-superposition-state coding. This coding method uses two isomorphism steps to obtain the zero-error code words, which are the isomorphism between quantum superposition states and vectors and the isomorphism between channels and matrices. The theoretical derivation proves that the channel capacity is able to increase by employing quantum zero-error coding other than its classical counterpart.
Keywords: quantum coding; zero-error channel; channel capacity.
Urban waterlogging monitoring and early warning based on video images
by Fengchang Xue, Juan Tian, Xiaoyi Song, Yan Yan
Abstract: Urban flood disaster causes serious losses to urban residents. Timely access to urban waterlogging conditions has great significance for disaster prevention and disaster relief. Owing to the time resolution limitation of data, the traditional monitoring urban flood disasters using remote sensing imagery cannot realise real-time automatic monitoring and continuous monitoring of key disaster areas. This paper selects road monitoring video, uses image difference operation and SVM (Support Vector Machine) algorithm to identify the waterlogging area, and uses the region growing method to extract the waterlogging area range. The research results show that this method can be used for continuous monitoring and early warning of urban waterlogging in real time.
Keywords: waterlogging monitoring; road monitoring video; SVM; region growing method.
Enterprise internationalisation performance evaluation model based on artificial neural network
by Guojun Yang, Xiaohu Zhou, Zhiyao Laing
Abstract: The internationalisation performance of enterprises is affected by many factors. Designing scientific and effective evaluation index system and conducting comprehensive evaluation are important means to help international enterprises to evaluate their internationalisation performance reasonably. This paper comprehensively reviews the research findings of factors influencing internationalisation performance and indexes in China and abroad, focusing on two quantifiable factors of financial performance and structural proportion, and constructs a hierarchy of internationalisation performance evaluation index system. Secondly, an artificial neural network (ANN) is used for adaptive training to obtain the optimised connection weights. Finally, the data of some enterprises internationalisation performance indexes are collected for empirical research to illustrate the rationality and effectiveness of the method.
Keywords: artificial neural network; internationalisation performance; index system; evaluation; enterprise internationalisation; financial performance; structural proportion.
Attacking the Niederreiter-type cryptosystem based on rank metric
by Chungen Xu, Yingying Zhang, Lin Mei, Lei Xu, Cong Zuo
Abstract: This paper deals with the Niederreiter cryptosystem based on Gabbidulin codes which were solidly broken by Overbeck within polynomial time. In this paper, we first review the conditions under Overbeck's attack applications and then adjust corresponding parameters to target a high security level. Since permutation matrix and the scrambling matrix are used in Gabidulin codes, then the Frobenius matrices have too much structure to be hidden. By analysing the rank of the system matrix, we can find that choosing the matrix such that the dimension of the kernel of the public parity-check matrix is greater than one will achieve a good result. In addition, we also show that bounding the rank of the distortion matrix is to enhance the security of the system. Finally, we give the security analysis of the modified Niederreiter type cryptosystem and demonstrate that it can resist structural and decoding attacks.
Keywords: code; cryptosystem; rank metric; matrix; Niederreiter.
Rainfall runoff prediction via a hybrid model of neighborhood rough set with LSTM
by Xiaoli Li, Guomei Song, Shuailing Zhou, Yujia Yan, Zhenlong Du
Abstract: Accurate rainfall runoff prediction is crucial for flood forecasting and water resources management, and it remains a challenging issue in hydrological information processing. The most challenging problem is that the processing of hydrological information holds the strong locality and nonlinearity, which leads to poor prediction accuracy. Neighborhood rough sets have strong capability on data classification and reduction, which can reduce those redundant rainfall runoff data. Long and short term memory network (LSTM) is a special recurrent neural network (RNN) that is an excellent variant of RNN, it is good at handling the time series data. In the paper, a hybrid model of neighborhood rough sets with LSTM is proposed, which is used for the rainfall runoff prediction. The experimental results show that the presented model could improve the training speed of LSTM and achieve much higher prediction accuracy than the conventional rainfall runoff prediction methods.
Keywords: neighborhood rough sets; attribute reduction; rainfall runoff prediction; LSTM.
Revocable ciphertext-policy attribute-based encryption in data outsourcing systems from lattices
by Xixi Yan, Chaochao Yang, Qichao Zhang, Jinxia Yu
Abstract: Attribute-based encryption mechanism is widely used in outsourcing environment because of its characteristics of 'one-to-many' communication. However, users attributes often change dynamically. In order to solve the problem of attribute revocation in the attribute-based encryption scheme in outsourcing systems, a revocable ciphertext-policy attribute-based encryption in data outsourcing systems from lattices is proposed. The scheme uses the LWE problem to construct the encryption and decryption algorithm, which can resist the quantum attack. Tree-access structure is adopted to realise flexible fine-grained access strategy. In addition, with the help of the data outsourcing management server, the attribute key and cipher-text are updated to achieve immediate attribute revocation. The scheme is proved to be secure under the selective attribute and selective plaintext attack. The comparative analysis shows that the scheme has a significant improvement in performance, and it supports immediate attribute revocation, which is more suitable for the dynamic change of users in the outsourcing systems, such as social network platform.
Keywords: lattices; attribute-based encryption; attribute revocation; data outsourcing system.
A decision tree algorithm for forest fire prediction based on wireless sensor networks
by Demin Gao, Jie Xin
Abstract: Forest fire poses a significant threat not only to the natural environment and ecological systems but also to the safety of human life and property. Combined with new technologies, a decision tree algorithm is proposed for forest fire prediction, in which wireless sensor networks technology is used to transmit data and predict the ignition of the forest. There are four meteorological parameters as part of training data, containing temperature, relative humidity, wind speed, and daily precipitation, while the other part is prediction results of forest weather index system. The decision tree generated by our system could classify these parameters from the most significant to the least significant and so can better foretell fire occurrence. The analysis of prediction results shows that our system is effective.
Keywords: decision tree algorithm; forest fire prediction; wireless sensor networks.
Enhanced parallel CFAR architecture with sharing resources using FPGA
by Sadok Msadaa, Youness Lahbib, Ridha Djemal, Abdelkader Mami
Abstract: The real time CFAR processor needs a very high computational performance. To meet the real-time requirements, this paper presents an implementation of a new hardware parallel design using ACOSD-CFAR detector. The aim of this work is to increase the architecture throughput and decrease the power consumption while maintaining a high resolution target detection. Our proposed implementation exploits the properties of the ACOSD-CFAR detector to enhance it with a parallel architecture, including some sharing resources. Compared with conventional implementation of CFAR, the proposed architecture increases the throughput from 2576 Mbit/s to 4736 Mbit/s by 184% and reduces the power consumption by 15%. The design is implemented on a Zync 7000 FPGA board, which is considered as a common validation platform.
Keywords: CFAR; VHDL; radar; parallel; FPGA; ACOSD; radar detector; radar implantation; enhanced; radar architecture; sharing resource.
Low power transistor level synthesis of finite state machines using a novel dual gating technique
by Abhishek Nag, Subhajit Das, Sambhu Nath Pradhan
Abstract: In this work, an efficient technique of clock and power gating is concurrently introduced in Finite State Machines (FSM) with a view to minimising the overall power dissipation. The proposed power gating concept works on the principle of shutting down the power supply to the FSM during periods of inactivity. The extraction of the inactivity criteria is based on the occurrence of self-loops within the FSM or an unchanged FSM output between successive clock pulses. Clock gating, on the other hand, disables the clock signal to the sequential blocks of the FSM during this inactive/idle periods. The idea of implementing the gating in both the state logic as well as output logic is introduced in this work. The proposed approach has been introduced in three benchmark FSM circuits, simulated and synthesised in CADENCE digital design tool. The results indicate a maximum of 73% total power savings (dynamic and static) with an average penalty of 27% area (approx.).
Keywords: clock gating; power gating; finite state machine; self-loops.
A combinational convolutional neural network of double subnets for food-ingredient recognition
by Lili Pan, Cong Li
Abstract: Deep Convolutional Neural Networks (DCNNs) have become the dominant machine learning technique for visual object recognition. They have been widely used in food image recognition and have achieved excellent performance. However, not only are the food-ingredient datasets not easy to obtain, but also the scale is not big enough to learn a deep learning model. For small-scale datasets, this paper proposes a novel DCNN architecture, which constructs an up-to-date Combinational Convolutional Neural Network of Double Subnets (CBDNet) for automatic classification of food-ingredients using feature fusion. The feature fusion is a component which aggregates subnets for more abundant and precise deep feature extraction. In order to improve classification accuracy, some useful strategies are adopted, including Batch Normalisation (BN) operation and hyperparameters setting. Finally, experimental results show that the CBDNet integrating double subnets, feature fusion and BN operation extracts better image features and effectively improves the performance of food-ingredient recognition.
Keywords: food-ingredient recognition; deep feature; deep learning; deep convolutional neural network.
Using improved RFM model to classify consumers in big data environment
by Guang Sun, Xiaofeng Xie, Jiayibei Zeng, Wangdong Jiang, Yuxuan Huang, Meisi Lin
Abstract: Big data makes the marketing focus of enterprises change from products to consumers, so customer relationship management (CRM) becomes a central issue for business operation. Because customer classification is the key question for CRM, this paper starts with RFM model, combines analysis of K-means clustering, and studies the method for distinguishing between valueless customers and high-value customers. Based on this method, specific management strategies are proposed to help enterprises find core consumers. Also, quantitative analysis of the validity of the cluster is done by using the elbow method. Results of the experiment show that establishing RFM index and using K-means clustering can start from the structure of dataset of consumers of enterprises and finally compare the difference among customer classification by using the clustered scatter plot to provide an effective way of classifying consumers.
Keywords: RFM model; customer segmentation; big data; cluster analysis.
Time-aware parallel collaborative filtering movie recommendation based on Spark
by Dan Yang
Abstract: Most of the traditional collaborative filtering (CF) recommendation models mainly consider the users' ratings on items, often ignoring the time context information of users. However, this information is non-trivial to improve the effectiveness of the recommender system. A time-aware parallel CF movie recommendation based on Spark is proposed in this paper. The CF algorithm based on matrix factorisation can associate users' interests with items through implicit features and solve the sparse matrix problem. The time-aware CF algorithm considers the dynamic features associated with the items and users, which improves the recommendation accuracy by introducing discrete time parameter into the matrix factorisation model. To solve the problem of the slow processing speed of high volume data, distributed computing based on Spark is used to achieve the parallelisation of the algorithm. The experimental results on the real dataset MovieLens show that the proposed method performs significantly better than traditional CF recommendation, which can alleviate the problem of data sparsity and significantly improve the processing speed and recommendation accuracy.
Keywords: time-aware; collaborative filtering recommendation algorithm; matrix factorisation; Spark.
Factor analysis and evaluation of Chinas higher education development in big data era
by Hangjun Zhou, Xingxing Zhou, Jing Liu, Kezhuo Chen
Abstract: Big data has become an indispensable part of higher education development, and most of the related data in higher education of China can be applied to evaluation and analysis. However, currently there is a lack of research on the development of higher education using the latest China Statistical Yearbook data. In this paper, based on the latest data, by using SPSS to analyse and evaluate the development of higher education, the new issues can be discovered and initial reliability recommendations can be proposed. Therefore, we will have more profound and unique insights and applications in the practice of the big data era, and will further provide evidence and clues to better support and develop China's higher education.
Keywords: Higher Education; Data Analysis; SPSS; Big Data.
Efficiency and safety assessment of suburban highway access management
by KeJun Long, Nuo Xu, Ling-yun Xiang, Xi Duan
Abstract: Congestion and traffic accidents often take place at the entrances and exits of highway. However, reasonable access management and design can greatly mitigate congestion and accident. This paper discusses the field of suburban highway access management by introducing three typical suburban highways access management models, including the two-way stop-controlled intersection, simplified Restricted Crossing U-Turn (RCUT), and interchange with right-in right-out. To ensure better efficiency and safety, the simulations were conducted in VISSIM and SSAM (Surrogate Safety Assessment Model) software. The travel time, delay, and throughput are used as the efficiency indexes, and the traffic conflicts are used as safety indexes to quantify the access management performance. The results show that the total delay at an interchange with right-in right-out decreased by 40.5%, and the conflict decreased by 50.0% than a two-way stop-controlled intersection design, as well as the total delay decreased by 30.7% and the conflict decreased by 67.4% than a simplified RCUT design.
Keywords: access management; traffic simulation; surrogate safety assessment model.
A software-based calibration approach to increase the robustness of embedded systems
by Md Al Maruf, Akramul Azim
Abstract: Embedded systems often interact with dynamic environments requiring not only to meet deadlines but also to achieve a certain level of accuracy. Since the inaccuracy of a task output produces a similar adverse effect such as timing violation, we propose a software-based calibration approach to increase the robustness of embedded systems by monitoring and comparing system component's output accuracy with a calibration standard to take actions for addressing any inaccuracy. The calibration standard is derived from a representative component's output with known high accuracy. As an example, we analyse the accuracy of a component that performs dynamic voltage and frequency scaling (DVFS) and explains the associated timing effects in terms of task schedulability. We also perform experiments on LITMUS-RT kernel to demonstrate the need and applicability of our calibration approach in the domain of embedded systems.
Keywords: embedded systems; task scheduling; monitoring; calibration; accuracy.
Energy-aware automatic tuning on many-core platform via adaptive evolution
by Chen Liu, Zhiliu Yang, Yijun Jiang
Abstract: Even though attaining high performance has been the users pursuit traditionally, in thernmany-core era the emphasis has shifted towards controlling the power and energy consumption so as to maintain a satisfying performance while consuming an acceptable amount of energy. This applies to both high performance and mobile computing platforms. To achieve this goal,We propose evolution algorithm based automatic tuning as one feasible solution for energy-aware computing on many-core microprocessors. In this paper, we presented several auto-tuning approaches employing Differential Evolution (DE) algorithms and Genetic Algorithm (GA). Our target is to approach the optimal setting of different power islands on a many-core platform as fast as possible when running multiple programs. Comparing with brutal-force approaches, our solution has the advantage of fast converging speed without the need to traverse the entire search space, and runtime tuning without a priori knowledge of the software workload. Our experimental results show that, AdaptivernDifferential Evolution algorithm is able to achieve reduced energy consumption as well as better energy delay product (EDP) than other representative algorithms that we examined. Based on the results we obtained, we believe adaptive evolution based auto-tuning approach is an effective method towards energy-efficient computing on many-core platforms.
Keywords: differential evolution algorithm; genetic algorithm; energy-aware computing; dynamic voltage and frequency scaling; many-core processors.
A review of regional distributed energy system planning and design
by Junjie Wang
Abstract: With the development of urbanisation in China, regional distributed energy plays a vital role in modern urban power systems, which can reduce emissions, reduce power consumption, and increase the safety and reliability of power grids. This paper focuses on three main aspects of regional distributed energy system planning and design: load forecasting, system optimisation and system evaluation. It summarises the types and research methods of each aspect and proposes the future development direction. Finally, the research framework of system planning and design is proposed.
Keywords: regional distributed energy system; load forecasting; system optimisation; system evaluation.
Real-time anomaly detection in gas sensor streaming data
by Haibo Wu, Shiliang Shi
Abstract: In order to improve the timeliness and accuracy of coal mine gas disaster risk assessments, it is important to detect anomalies in the gas sensor streaming data in real-time. In this paper, the support vector regression (SVR) algorithm combined with the normal statistical distribution technique is used to establish a real-time anomaly detection model for gas sensor streaming data. The model uses SVR to fit the nonlinear mapping relationship between the multi-dimensional monitoring data of the coal mine working face and the gas sensor streaming data to predict the gas sensor value. For the distance between the predicted and the measured gas sensor values, the normal distribution method is used to determine gas sensor anomalies in real-time. Furthermore, this paper presents a prototype system for the real-time anomaly detection in gas sensor streaming data that is built using the memory-based distributed stream processing framework Spark Streaming. Experiments show that the real-time anomaly detection system combined with the SVR algorithm and the normal statistical distribution can periodically update the anomaly detection model and determine anomalies in the sensor streaming data in real-time. For a window size of 9, an update cycle of 1 and an anomaly threshold of 0.95, the anomaly detection model is better than the boxplot, the statistical analysis and the clustering algorithm regarding the prediction precision and accuracy. Moreover, the presented approach improves the detection efficiency.
Keywords: gas sensor; anomaly detection; streaming data; SVR; Spark Streaming.
Research on fuzzy clustering method for working status of mineral flotation process
by Yanpeng Wu, Xiaoqi Peng, Nur Mohammad, Hengfu Yang
Abstract: A fuzzy clustering method based on FCM algorithm for the working status of the mineral flotation process is proposed to help workers control the mineral flotation process better. The working status of the mineral floatation process can be determined by judging dosing records, in terms of copper sulphate, lead nitrate, xanthate, 2# oil, black medicine, etc., or by observing the visual features of the foam layer including the average grayscale, R-means, G-means, B-means, the average bubble size, the skewness of bubble size, the standard deviation of bubble size and bubble stability. Nearly 6000 continuous dosing data were collected from an automatic dosing system installed in a gold mine flotation workshop and normalised to the range of [0, 1]. Those dosing data were first pre-classified into steady condition and unsteady condition according to the degree of change in neighbouring data and then clustered respectively. Ninety-six categories of steady condition and 24 categories of unsteady condition were obtained by an FCM program using Euclidean distance. Similarity coefficient analysis on mean, standard deviation, and variation coefficient indicate that the dosing clusters in steady condition are more trustworthy to dosing operators than those in unsteady condition. Meanwhile, FCM algorithm is suitable for dosing data clustering with high consistency.
Keywords: FCM; fuzzy clustering; dosing records; visual characteristic; mineral flotation.
Matrix completion based prediction analysis of carbon emissions
by Wei Huang, Danqing Wei, Cheng Wang, Chongze Lin
Abstract: China's carbon emissions data at this stage are mainly concentrated at the provincial and national levels. As a major area for the implementation of carbon emission reduction measures, cities have not had a complete carbon inventory for a long time owing to the lack of basic data. In order to solve this problem, this paper constructs a set of prefecture-level CO2 emission forecasting methods to study the carbon emissions of 11 urban areas in Zhejiang Province. The two-dimensional matrix is formed by one-to-one correspondence between city and time. Through the analysis of the historical data of carbon emissions, the intrinsic relationship is found, and the missing data is predicted by the method of matrix completion. Experiments show that compared with Zhejiang's actual carbon emissions statistics data, the difference is found to be within 1%, and can achieve 69.3% higher than the latest method.
Keywords: carbon emissions; carbon inventory; matrix completion.
LEG-PER - LiDAR-enhanced GNSS positioning error reduction
by Walter Balzano, Fabio Vitale
Abstract: Internet of Things is a promising research area, with many applications in smart home devices but also in vehicular communication and self-driving vehicles. Using this technology, vehicles are able to establish P2P connections in order to share information, improving road security and reliability against accidents due to road and traffic conditions. IoT and vehicle technologies are strongly dependent on positioning, which in outdoor environments is normally determined using satellite systems like GPS and GLONASS. However, the precision is not very reliable due to multipath issues and when the sky visibility is limited, like in urban canyons. In this paper we present LEG-PER - LiDAR-Enhanced GNSS Positioning Error Reduction, a new methodology that improves the accuracy of satellite-based systems using a combination of V2V and an elastic graph generated by the vehicles in the area using LiDAR-determined distances.
Keywords: vehicle-2-vehicle; LiDAR; global navigation satellite system.
Effect of the crowdfunding description on investment decisions from the perspective of prosocial behaviour
by Yonghui Dai, Tao Wang, Ziyi Wang, Bo Xu
Abstract: Alleviating poverty is a global challenge and requires extensive mobilisation of the participation from every social force. A feasible way for alleviating poverty is crowdfunding through the internet, which could receive financial aid from a number of people all over the world to some reasonable projects that were released online officially. This work argues that prosocial behaviour can increase the possibilities of achieving the fundraising goals of projects. Based on the prosocial behaviour theory, this work analyses the prosocial factors from the poverty alleviation projects textual data published on an international crowdfunding platform, Kiva, and establishes the logit model to test and verify the feasibility of these factors. The results show that the fundraiser identity and fundraising project orientation in the crowdfunding description both play critical roles in the financial funding process.
Keywords: poverty alleviation; crowdfunding; prosocial behaviour; investment decisions.
Turtle-shell data embedding method with high image fidelity
by Guo-Hua Qiu, Chin-Feng Lee, Chin-Chen Chang
Abstract: In this paper, a modified version of the turtle-shell data-hiding (TDH) scheme is proposed, aiming at improving the quality of stego-image. TDH scheme was first proposed by Chang et al. in 2014. In comparison to other schemes at that time, the scheme exhibited a superior visual quality in the premise of ensuring high embedding capacity (EC). However, in the TDH scheme proposed by Chang et al., the rules to seek for the search space of the hiding message suffer the drawback of omitting the better alternative pixel pair. The scheme proposed in this paper modifies the embedding rule by allowing the selection of a proper region other than the search space proposed by Chang et al., so as to better hide data and minimise the image distortion. According to the experimental results with the same EC, the visual quality of the stego-image in the proposed method is 0.41 dB higher than that of stego-image in Chang et al.s method.
Keywords: data hiding; steganography; turtle-shell; visual quality; embedding capacity.
Scalable and efficient routing protocol for internet of things by clustering cache and diverse paths
by Zhiqiang Ruan, Haibo Luo
Abstract: This paper proposes SENR, a Scalable and Efficient Named-based Routing protocol for the Internet of Things (IoT). Unlike traditional IoT data collection mechanisms, SENR is a consumer-driven data retrieval scheme, which is motivated by the different architecture designs in Named Data Networking (NDN). SENR explores semantic notification and opportunistic caching strategy to enhance the data retrieval efficiency for smart systems. It includes three aspects: 1) for data and Interest aggregation, SENR performs data aggregation according to the hierarchy of namespace, and stores data over in-network storage node; in the data retrieval process, it marks each route notification at the edge nodes, and allows the Interest with the same name prefix to choose the same edge routing node towards to the storage node, which improves the cache hit ratio and reduces the network traffic; 2) for adjustable path selection, SENR adopts link-state routing protocols to collect network topology and compute the shortest path to the content source nodes, moreover, it uses distance-vector routing protocols to select multiple alternative paths to cope with node failures; 3) for partial information updates, SENR employs partial routing update to explicit support of dynamic paths and caching capabilities, so that each node simply informs its neighbours by the available routing updates, and allows the neighbours to fetch such part of updates. Simulation results demonstrate that SENR can significantly reduce the content retrieval delay and routing overheads.
Keywords: IoT; semantic query; opportunistic caching; future network.
MOGATS: a multi-objective genetic algorithm-based task scheduling for heterogeneous embedded systems
by Mohaddeseh Nikseresht, Mohsen Raji
Abstract: Multi-objective optimisation is an unavoidable requirement in different steps of embedded systems design, including task mapping and scheduling. In this paper, a new Multi-Objective Genetic Algorithm-based Task mapping and Scheduling (abbreviated as MOGATS) is presented for heterogeneous embedded system design. In MOGATS, the architecture of the hardware platform and the set of tasks in the form of a task graph are assumed to be given as the inputs. The task mapping and scheduling problem is modelled as a genetic algorithm-based optimisation approach in which the execution time, energy consumption, and reliability of the scheduling are considered as the objectives of the optimisation method. In MOGATS, we are interested in finding the Pareto frontier of the solutions (scheduled tasks) in order to help the designer to pick out the best outcome according to different design considerations. The experimental results on real application task graphs show that MOGATS provides a better solution than the greedy algorithm if it is applied as a single-objective task scheduling method. Moreover, the superiority of MOGATS in comparison with the state-of-the-art single- and multi-objective task scheduling algorithms is shown in terms of standard performances factors, such as scheduling length ratio and speed-up parameters.
Keywords: heterogenous embedded systems; task scheduling; multi-objective optimisation; genetic algorithm.
Research on teaching practice of app inventor course with embedded in computational thinking
by Hexiao Huang, Leilei Chen, Siwei Jin, Ying Wang, Hongzhi Hu
Abstract: At present, the concept of computational thinking is spreading in education around the world. From the point of view of current research and application in China, the research on the cultivation of computational thinking in K-12 education is still in the early stage. Based on the graphic programming design course of app inventor in primary and secondary schools, this study embeds computing thinking into teaching objectives and contents, constructs the teaching model of app inventor course with embedded computing thinking, and discusses how to cultivate students' computing thinking ability in teaching practice. The method provided in this paper is helpful to analyse the changes of students' computational thinking during the teaching process, and it can effectively identify teaching effects.
Keywords: computational thinking; app inventor; abstract thinking; teaching practice.
Anonymous chaotic based identity authentication protocol in IoT
by Jing Long, Xin Su
Abstract: With the rapid development of the Internet-of-Things (IoT), the credible identity authentications among the connected devices are widely examined by researchers and institutes. Traditional identity authentication protocols have issues in terms of security, complexity and authentication efficiency. To address these problems, this work introduces a chaotic mapping technique into identity authentication. Besides, anonymity and interactivity are also considered to realise a highly efficient and secure IoT device authentication. Firstly, Kent chaotic mapping technique is used to generate the chaotic sequence as the authentication key. After that, the interactive authentication among the server, reader and tag is performed. The authentication information is encrypted in the transmitted channel. The identity information is anonymous in the proposed protocol. Finally, the security, complexity and efficiency are analysed and discussed. The comparison results show that the proposed protocol is superior to other protocols.
Keywords: IoT; identity authentication; chaotic mapping; anonymity.
Analysis and compensation of inner lever arm effect in strapdown inertial navigation system
by Qi Wang, Changsong Yang, Shaoen Wu
Abstract: The inner lever arm effect is analysed under the ideal condition that the three sensitive axes of the accelerometers equipped in inertial measurement unit are perpendicular to each other and intersect at one point. The virtual normal acceleration integral and the virtual tangential acceleration integral cannot be completely cancelled, and the velocity drift rate of the inner lever arm effect is independent of the angular vibration centre. It is directly proportional to the length of the inner arm and directly proportional to the square of the vibration frequency. The method to compensate the effect of inner lever arm is presented in this paper to convert the measured acceleration at the sensitive point of accelerometer into the intersection point of their sensitive axes. The analysis and compensation process of the internal lever-arm effect will be more complicated in the case where the sensitive axes of the IMU accelerometer are not perpendicular to each other or do not intersect at one point. Inner lever arm cannot be avoided in the IMU of the redundant configuration of the multi-accelerometer. Simulation experiments were carried out and the results show that the method of compensating the inner lever arm is effective and greatly decreases the error of lever arm.
Keywords: inner lever arm effect; inertial measurement unit; compensation; simulation experiments.
Service component recommendation based on LSTM
by Xiao Yang, Hong Xu, Hongping Shu, Yaqiang Wang, Kui Liu
Abstract: Service component selection is a core problem in software development process. With an enormous number of components available, it is often difficult for the developer to select the most appropriate one, as he or she might not be aware of all the possible business scenes ahead of time. Taking these challenges into consideration, we propose a deep learning based system that automatically recommends service components based on component selection history during the software development process. We employ a sequential model with two long-short-term memory (LSTM) layers and two fully connected layers, using SoftMax as an activation function, to predict the next service component. The model was trained, validated and tested on a dataset with more than 120,000 examples from a real-world software company. The proposed network outperforms the baseline methods in terms of the evaluation criteria. In addition, the model results were deployed in a real-world software tool and yielded positive feedback.
Keywords: service component; recommendation system; long-short-term memory network.
Applying analytical and empirical schedulability analysis techniques to a real spacecraft flight software
by Nunzio Cecere, Massimo Tipaldi, Davide De Pasquale
Abstract: Software schedulability analysis is a crucial aspect for real-time software system verification. This paper presents a combined approach of analytical and empirical techniques for measuring task execution times and verifying the timing constraints of Flight Software (FSW) applications in real space projects. The proposed methodology is based on the following two main steps. Firstly, we perform an analytical verification via the Response Time Analysis (RTA) in order to show that all the hard deadlines defined for time-critical SW tasks are met. In particular, the Worst-Case Execution Times (WCET) of such SW tasks are measured via static code analysis. Secondly, we analyse the activities that are executed in the context of less time-critical tasks via a more empirical argumentation. The CPU load for the whole running FSW is measured in significant and time-consuming operational scenarios with the aim of proving that the CPU load requirements are fulfilled. Such approach has been applied to an industrial-sized spacecraft FSW and points out the importance of focusing on this topic in the early phases of the FSW development.
Keywords: SW schedulability analysis; spacecraft flight software; response time analysis; worst-case execution time; spacecraft operations.
Improved NSGA-II for the minimum constraint removal problem
by Bo Xu, Feng Zhou, Yonghui Xu, Xu Haoran, Kewen Xu
Abstract: This paper focuses on the minimum constraint removal (MCR) problem. Based on a preliminary study, this paper proposes a comprehensive multi-objective evaluation model to derive a feasible solution to MCR path planning that, from the robots individual perspective, is driven by the costs and benefits, and takes into account factors such as the minimum constraint set, the route length, and the cost. The feasible solution to the MCR path is evaluated using this model. A typical multi-objective algorithm NSGA-II is applied to the MCR problem. The algorithm test results show that compared with single objective algorithm planning, the NSGA-II-based path planning algorithm can find a shorter path that traverses fewer obstacle areas and can thus perform the MCR path planning more effectively.
Keywords: minimum constraint removal; minimum constraint set; path planning; multi-objective optimisation; robot.
Information hiding mechanism based on QR code and information sharing algorithm
by Xiaohui Cheng, Tong Niu
Abstract: With the rapid development of internet technology, information security is receiving more and more attention. In order to solve the problems of information leakage and information tampering during the transmission of information, an information hiding mechanism based on the QR code is proposed. This mechanism uses the error correction mechanism of the QR code to hide secret information, and then splits and transmits it through a secret sharing algorithm. Experiments show that the proposed information hiding mechanism can effectively hide secret information, and has strong security and robustness. The proposed information hiding mechanism has the characteristics of high flexibility and anti-theft, and can be applied to the actual information transmission process.
Keywords: QR code; information security; secret sharing; information hiding.
Design and implementation of a cloud encryption transmission scheme supporting integrity verification
by Zengyu Cai, Zuodong Wu, Jianwei Zhang
Abstract: In the cloud storage environment, the integrity of private data is one of the most concerned issues for users, which has become the focus of cloud storage research. For this kind of problem, the existing schemes usually sacrifice the communication efficiency of users for higher security, which often causes a lot of computing overhead. Therefore, the purpose of this paper is to achieve the coexistence of safety and efficiency, and adopts the ideas of Chinese commercial encryption algorithms SM2 and SM3, proposes a cloud encryption transmission scheme that supports integrity verification, and gives a security analysis under the assumption of discrete logarithm problem on elliptic curve and Diffie-Hellman problem. Finally, the actual test and comparative experiment results show that our scheme can not only realise the cloud data transmission encryption and cloud storage integrity verification functions at the same time without affecting the performance of the cloud server. Moreover, it can effectively resist all kinds of common attacks, reduce the storage and computing burden of cloud users, and has certain guiding significance for the research of user privacy protection in the cloud environment.
Keywords: cloud storage; Chinese commercial encryption algorithms; SM2; SM3; discrete logarithm; Diffie-Hellman; elliptic curve; cloud data transmission encryption; integrity verification; privacy protection;.
A combination classification method based on Ripper and Adaboost
by Min Wang, Zuo Chen, Zhiqiang Zhang, Sangzhi Zhu, Shenggang Yang
Abstract: With the growing demand for data analysis, machine learning technology has been widely used in many applications, such as mass data summarising rules, predicting behaviours and dividing characteristics. The Ripper algorithm presents better pruning and stopping criteria than the traditional decision tree algorithm (C4.5), while its error rate is less than or equal to C4.5 by O (nlog2n) time complexity. As a result of that, Ripper can maintain high efficiency even on the massive dataset which contains lots of noise. Adaboost is one of the iterative algorithms, which combines a group of weak classifiers together to set up a strong classifier. In order to improve the accuracy of Ripper classification algorithm and reduce the computational complexity, this paper proposes a Ripper-Adaboost combined classification method (Ripper-ADB). The experiment result shows Ripper-ADB could improve the classifier and get higher classification accuracy than decision tree and SVM.
Keywords: Ripper; feature selection; Adaboost; NSL-KDD; C4.5.
A movie recommendation model combining time information and probability matrix factorisation
by Huali Pan, Jingbo Wang, Zhijun Zhang
Abstract: A deep analysis and discussion of matrix factorisation technologies are given in this paper taking into account the defects of traditional collaborative filtering recommendation algorithms. In addition, we provide an analysis of the effects of feature vector dimensions on the recommendation quality and efficiency of a probability matrix factorisation (PMF) algorithm. A PMF algorithm will lead to inaccurate recommendations if it does not consider possible dynamic changes in a users interest over time. Accordingly, a TPMF model, a PMF algorithm integrated with time information, is proposed in this article. Its feasibility and effectiveness are empirically verified using movie recommendation datasets, and higher prediction accuracy is confirmed compared with existing recommendation algorithms.
Keywords: collaborative filtering; matrix factorization; movie dataset; personalised recommendation; time information.
Quantum image filtering and its reversible logic circuit design
by Gaofeng Luo, SheXiang Jiang, Liang Zong
Abstract: Quantum information processing can overcome the limitations of classical computation. Consequently, image filtering using quantum computation has become a research hotspot. Here, a quantum algorithm is presented on the basis of the classical image filtering principle to detect and cancel the noise of an image. To this end, a quantum algorithm that completes the image filtering task is proposed and implemented. The novel enhanced quantum representation of digital images is introduced. Then, four basic modules, namely, Position-Shifting, Parallel-CNOT, Parallel-Swap, and Compare the Max, are demonstrated. Two composite modules that can be used to realise the reversible logic circuit of the proposed quantum algorithm are designed on the basis of these basic modules. Simulation-based experimental results show the feasibility and the capabilities of the proposed quantum image filtering scheme. In addition, our proposal has outperformed its classical counterpart and other existing quantum image filtering schemes supported by detailed theoretical analysis of the computational complexity. Thus, it can potentially be used for highly efficient image filtering in a quantum computer age.
Keywords: quantum computing; reversible logic circuit; image filtering.
Mining constant information for readable test data generation
by Mingzhe Zhang, Yunzhan Gong, Yawen Wang, Dahai Jin
Abstract: Automated test data generation tools produce test data that can achieve high coverage faster than test data generated manually by a tester. However, the test data generated by automated tools has been shown to not help developers find more bugs. The main reason is that it is difficult for human testers to understand and evaluate the test data. In this paper, an approach is introduced to automatically generate readable test data, which has been implemented in a tool called CTS. CTS can mine constant information from projects under testing and obtain heuristic information by aggregating and rating related constants. CTS adds heuristic information to the automatic test data generation process to generate test data that is quick and easy for a human to comprehend and check. Empirical experiments show that the proposed approach can improve the efficiency of test data generation and generate test data that is more convenient for a human oracle.
Keywords: test data generation; readable test data; constraint-based testing; symbolic execution.
A TAO-based adaptive middleware for pervasive computing
by Yanhui Guo, Zhenmei Yu, Fuli Qu, Hui Liu
Abstract: Over the past years, a considerable amount of effort has been devoted, both
in industry and in academia, towards the development of innovative applications for the Internet of Things(IoT). An important challenge of IoT application is to adapt to the dynamical environments, which can be modified at runtime considering the emergence of other requirements. To address this issue, pervasive computing can provide us with a good solution. Aiming at the environments that are open, dynamic and heterogeneous, we propose an adaptive middleware named PAmiddleware. PAmiddleware is service-oriented, context-aware, and supported by QoS. The architecture of PAmiddleware is based on TAO, which is a standards-based, CORBA middleware framework. Meanwhile, we present a model for context awareness to allow the adaptation and to give a modelling method of context description for context resource. We also propose an adaptive strategy that uses a genetic algorithm for optimisation. The proposed model can well meet the needs of pervasive computing, and provide more convenient service.
Keywords: pervasive computing; middleware; components; context awareness; adaptive.
Home security alarm system for middle-aged people living alone
by Guangyi Ma, Hui Xu, Xijie Zhou, Wei Sun
Abstract: How to ensure middle-aged people's security has become an urgent social issue in China. Existing security systems are difficult to initialise with fast electrodes consumption. Therefore, we designed a smart security device suitable for middle-aged and elderly users. We select STM32 and CC2530 as master controller for the device, which is equipped with a camera module, GSM/GPRS module, smoke sensors, flame sensors, and infrared sensors. The camera module is used to capture the live pictures of the monitored areas, then these pictures will be transmitted to the users by the GSM module. Multiple CPU can increase the speed of operation, and ZigBee technology for wireless transmission can lower the loss of the supply. Compared with other security systems, the proposed program optimises the interface to make interaction operation easier for middle-aged and older users. The experimental results show that the proposed system has low power dissipation, convenient operation and high stability.
Keywords: home security alarm system; wireless sensor network; WSN; embedded system; Zigbee; internet of things; IoT.
Container-based task scheduling for edge computing in IoT-cloud environment using improved HBF optimisation algorithm
by Srichandan Sobhanayak, Kavita Jaiswal, Ashok Kumar Turuk, Bibhudatta Sahoo, Bhabendu Kumar Mohanta, Debasish Jena
Abstract: In conventional cloud computing technology, cloud resources are provided centrally by massive data centres. Therefore, edge computing technology has been proposed, where cloud services can be extended to the edge of the network to decrease a network congestion. The management of the resources is a major challenge before the researcher. Therefore, in this paper, a task scheduling algorithm based on hybrid bacteria foraging optimisation (HBFA) has been proposed for allocating and executing an application's tasks. The proposed algorithm aims to minimise the completion time and maximise resource utilisation in the edge network. A rigorous simulation has been done to test performance of the proposed strategy to compare it with state of art algorithms. The proposed strategy shows better performance compared to the existing work.
Keywords: internet of things; IoT; cloud; edge computing; container.
RFID aided SINS integrated navigation system for lane applications
by Qi Wang, Chang-song Yang, Shaoen Wu
Abstract: To improve the lane vehicle position accuracy, RFID technology is applied to correct the position of the SINS irregularly with label positioning. The acceleration data of the vehicle in three directions is measured by the accelerometers of the inertial measurement unit; the attitude matrix is updated in real time using the angular velocity of the gyroscope output space, and the acceleration component is transformed into the geographic coordinate system, and the acceleration of the inertial measurement unit. The data is subjected to an integral operation process to obtain a spatial displacement value of the vehicle. The real-time updating algorithm of the attitude matrix and the processing of the inertial measurement unit signal are presented. The quaternion-based algorithm is used to solve the attitude matrix as well as updating the coordinate system of the inertial navigation attitude matrix in real time. The Hilbert-Huang transform is used to filter the acceleration signal to solve the integrator saturation problem caused by the low-frequency component of the acceleration signal. The EMD algorithm based on the continuous root mean square error is applied in rejecting the low-frequency components in the signal. The simulation experiments show that the system can be reliable and high precision.
Keywords: radio frequency identification; RFID; strapdown inertial navigation system; SINS; attitude matrix; simulation experiments.
Application of vision aided strapdown integrated navigation in lane vehicles
by Qi Wang, Chang-song Yang, Shaoen Wu
Abstract: The application of global navigation satellite system (GNSS) is extensive in lane applications with the development of science and technology. Vision aided strapdown integrated navigation is an effective aided-navigation method in the case of GNSS failure in lane vehicles, which plays an important role in realising high-precision navigation of lane navigation system. A vision-aided navigation system based on GNSS positioning is constructed using the electrical powered platform as the research object. The hardware platform of vision navigation system is presented and digital image processing is used to segment the collected lane image. The image pre-processing operation, including denoising filtering and greyscale processing, is carried out to complete the segmentation and get the effective navigation area. According to the effective area of navigation, a navigation datum line is extracted by the least squares linear fitting and Hough transform. According to the camera imaging model and the camera's internal and external parameters, the navigation datum line in the image coordinates is transformed into the world coordinates, and the heading angle is calculated. Kalman filter algorithm is used to fuse the navigation parameters of the vision navigation module and the GNSS positioning module, and the integrated navigation model is established.
Keywords: lane vehicle; vision aided strapdown integrated navigation; image segmentation; navigation line detection; Kalman filter.
Extending the lifetime of NAND flash-based SSD through compacted write
by Hai-Tao Wu, Tian-Ming Yang, Ping Huang, Wen-Kuang Chou
Abstract: In the traditional file system, the partial page write will result in internal fragmentation and write amplification of NAND flash-based SSDs due to the page-alignment of write. Although large page sizes are useful for increasing the flash capacity and throughput, they may decrease the performance and lifetime of flash storage systems for the frequent partial page writes. After analysing the various realistic workload traces, we observe that the partial page writes are common for those heads and tails of large write requests. This observation makes compacted write possible, which compresses two partial page writes from the same large write request into one page before data are written into flash. Therefore, we propose a compacted write for page-level FTL scheme, called CWFTL, to extend the lifetime of SSD. The experiment results show that CWFTL really reduces the times of data written to flash and the average read/write response time.
Keywords: solid state drive; SSD; flash translation layer; FTL; write amplification; compacted write; partial page write.
A software control flow checking technique in multi-core processors
by Mohammad Reza Heidari Iman, Pejman Yaghmaie
Abstract: Multi-core processors can benefit performance, power consumption and level of parallelism, which is the primary reason why they are employed in safety-critical embedded systems. Nowadays the use of safety-critical multi-core embedded systems in different industries is growing significantly. In these systems, an error may result in a severe failure which can lead to disaster. In order to prevent such failure, the fault tolerance of the systems should be improved. Some of the important errors, which can cause failures, are the control flow errors changing the execution flow of a multi-core program and eventually leading to core failure. To detect them, different control flow checking techniques have been proposed, almost all of which have so far aimed to detect errors in single-core processors. In this paper, a software control flow checking technique in multi-core processors, called SCFC-MC, has been proposed wherein, in addition to executing the program, each thread monitors the execution flow of another thread, thereby eliminating every single point of failure. Experimental results show that applying SCFC-MC to a quad-core processor [Intel(R) Core(TM) i7-4710HQ] results in the detection of about 95% of control flow errors with less than 20% performance overhead.
Keywords: fault tolerance; multi-core processors; control flow errors; CFEs; software control flow checking techniques; SCFC-MC technique; safety-critical embedded systems.
The design and implementation of a wearable human activity recognition system based on IMU
by Wei Zhuang, Suyun Xu, Yue Han, Jian Su, Chunming Gao, Dan Yang
Abstract: In recent years, with the rapid development of the inertial measurement unit (IMU) and wireless body area network, and the maturity of pattern recognition theory, the technology of human activity recognition gradually gets the attention of researchers, becoming the research hotspot in this field. On the basis of existing IMU, this paper designs a wearable human activity recognition system, which consists of microprocessor, three-axis accelerometer, three-axis gyroscope, power module and so on. The system can provide real-time, continuous human motion information (acceleration and angular velocity information) to the Android control unit, and it can realise the real-time receiving, dynamic display and storage of human motion information.
Keywords: inertial measurement unit; IMU; wearable technology; posture recognition; support vector machine.
Design of a hand gesture recognition system based on forearm surface electromyography feedback
by Wei Zhuang, Yi Zhan, Yue Han, Jian Su, Chunming Gao, Dan Yang
Abstract: This paper presents a study of using surface electromyography (SEMG) for hand gesture recognitions. A SEMG acquisition system is discussed in this paper. The characteristics of SEMG are introduced and the transformation characteristics are analysed as well. Then the selected gestures and the location for the SEMG sensor are determined. The process of pattern recognition and the reason of selecting SVM classifier are presented in detail, and the kernel function selection of SVM is discussed. Three optimisation methods of parameters are compared using the cross-validation method. Finally, the parameters obtained by the genetic algorithm are used to test the model and the recognition performance.
Keywords: MYO; wearable technology; gesture recognition; support vector machine.
Release and collection method of residual energy for one-dimensional linear-zone internet of things
by Haibo Luo, Zhiqiang Ruan
Abstract: Energy consumption is one of the most important considerations when designing routing and transmission protocols of sensing networks in internet of things. In order to achieve low and balanced power consumption, an effective method is to refer to the residual energy of nodes. Some existing energy-efficiency routing algorithms are designed according to the residual energy of nodes, but they do not give the way of residual energy collection. In this paper, we design a strategy for releasing and collecting residual energy for one-dimensional linear topology networks. By listening to the broadcast messages and evaluating residual energy of forward nodes, nodes can dynamically update the residual energy information of all potential forwarders. The collection error is also analysed theoretically. This method can be applied to opportunistic routing and relay node selection algorithms. The simulation results show that our proposed method has very low collection error, and the error will not accumulate with the operation of the network.
Keywords: internet of things; residual energy; one-dimensional linear zone; collection method.
A new upper bound of the completion time of the background task in a foreground-background system
by Amin Danial Asham
Abstract: A foreground-background scheduling system is a simple real-time pre-emptive scheduler, which is commonly used in uniprocessor embedded systems. In this system, there is a single background task of the lowest priority and multiple foreground tasks have higher priorities. Foreground tasks may have different levels of priorities. Foreground tasks are allowed to pre-empt the background task. The background task takes a longer time to complete its execution because of the frequent interruptions caused by the foreground tasks. The completion time of the background task is calculated using the utilisation of the processor by the foreground tasks. In this paper, a new upper bound formula of the completion time of the background task is derived. The proposed formula gives a closer upper bound to the exact completion time compared to the existing bounds in the case of few foreground tasks and even it gives the exact time in certain cases for the heavily utilised systems. In addition, the proposed upper bound is not a recursive formula like that of the existing response time analysis.
Keywords: real-time; fixed priority; upper bound; foreground-background; response-time; completion time.
Optimal path selection for logistics transportation based on an improved ant colony algorithm
by Xiangqian Wang, Huizong Li, Jie Yang, Chaoyu Yang, Haixia Gui
Abstract: The ant colony algorithm, as a heuristic intelligent optimisation algorithm, has succeeded in solving many real-world problems, such as the vehicle routing. However, the traditional ant colony algorithm has suffered from several shortcomings, including the premature stagnation and slow convergence. To address these issues, an improved ant colony algorithm is proposed in this paper. The main contribution is to adaptively adjust key parameters during the evolution. Later the proposed algorithm is validated by addressing the vehicle routing problem. Two real-world datasets are collected from two logistic enterprises separately (i.e., YUNDA and YTO) based in Huainan City, China. Comprehensive experiments have been performed by applying the proposed algorithm to search for the optimal path. Meanwhile, the comparison between the traditional ant colony algorithm and the improved algorithm has been conducted accordingly. Experimental result shows that the proposed algorithm achieves better performance in minimising routing path and reducing the computational cost.
Keywords: ant colony algorithm; optimal path; logistics transportation; vehicle routing.
A robust error control coding-based watermarking algorithm for FPGA IP protection
by Zhenyu Liu, Xin Su, Dafang Zhang, Jing Long
Abstract: The ownership of intellectual property (IP) for integrated circuit (IC) is difficult to identify when the watermarks are attacked and damaged in previous work. To address this issue, we propose a robust error control coding (ECC)-based watermarking algorithm for FPGA IP protection. Firstly, the Blakley threshold scheme is used to share the signature of IP user and generate watermarks. The watermarks are then encrypted and finally embedded into FPGA IP design. The signature sharing makes it unnecessary to ensure that all watermarks are reliable in authentication. The complete signature can be retrieved with several watermarks even if other watermarks are damaged. Secondly, the IP owner's image signature is used for copyright identification, and the image is shared by the threshold multi-secret sharing method to solve the resource overhead problem. The image itself is fault-tolerant. Even if the image has a bit error rate of 16.74%, the copyright content can be successfully identified. The fault tolerance of the image has greatly improved the robustness of the watermark. Experiments show that the algorithm not only has low overhead for watermark embedding, but also achieves good results in terms of robustness.
Keywords: IP watermark; error control; FPGA; secret sharing; robustness.
Bio-inspired security analysis for IoT scenarios
by Vincenzo Conti, Andrea Ziggiotto, Mauro Migliardi, Salvatore Vitabile
Abstract: Computer security has recently become more and more important as the world economy dependency from data has kept growing. The complexity of the systems that need to be kept secure calls for new models capable of abstracting the interdependencies among heterogeneous components that cooperate at providing the desired service. A promising approach is attack graph analysis, however, the manual analysis of attack graphs is tedious and error prone. In this paper we propose to apply the metabolic network model to attack graph analysis, using three interacting bio-inspired algorithms: topological analysis, flux balance analysis, and extreme pathway analysis. A developed framework for graph building and simulations as well as an introductory to some IoT scenarios as use cases are also outlined.
Keywords: security analysis; attack graphs; network security; system security; bio-inspired techniques; IoT; metabolic networks; bio-inspired algorithms.
Towards deterministic FPGA reconfiguration
by João Gabriel Reis, Antônio Augusto Fröhlich
Abstract: Rigid partitioning of components in hardware/software co-design flows can lead to suboptimal choices in embedded systems with dynamic runtime requirements. FPGAs allow systems to cope with such unforeseen conditions by changing portions of hardware dynamically while other parts are still active. Nevertheless, to guarantee a transparent reconfiguration, it is necessary to ensure that it does not disrupt the timing requirements of the running tasks and vice-versa. This work proposes a deterministic FPGA reconfiguration mechanism capable of mitigating the interference generated by I/O operations occurring in parallel. The reconfiguration is confined in the idle time without interfering with or being interfered by other activities occurring in the system, including peripherals performing I/O. The scheme decomposes the reconfiguration process in small steps such that it is preemptable, and compliant with timing requirements. To quantify the impact of I/O interference on FPGA reconfiguration, we measured the execution time to load bitstreams from memory to the FPGA reconfiguration interface with multiple peripherals performing I/O in parallel. Results show that if the I/O interference is not taken into account and mitigated, the reconfiguration time can grow up to 8,800% when peripherals are performing I/O operations through DMA.
Keywords: field-programmable gate array; FPGA; dynamic reconfiguration; I/O interference; embedded systems; partial reconfiguration; power management; interference mitigation; speculative reconfiguration; operating system.
A sparse system identification algorithm based on fractional order LMS
by Yun Tan, Jiaohua Qin, Xuyu Xiang, Wentao Ma
Abstract: In this paper, a zero-attracting fractional order least mean squares (ZA-FLMS) algorithm is proposed for adaptive sparse system identification. l1-norm of the filter coefficients is considered and zero-attracting correction is introduced into the updating equation. The extension of the algorithm is also proposed for distributed local sensors of information-centric IoT. The convergence speed and MSE performance are investigated in the simulations, which show effective improvement for sparse system identification compared with traditional LMS, zero-attracting LMS and fractional order LMS, especially with lower sparsity and smaller fractional order. But the decrement of fractional order and step size will lead to slower convergence speed, while the bigger fractional order and step size will lead to bigger variation of MSE. Therefore, optimised ZA-FLMS is further introduced, which uses l0-norm in the initial stage of the algorithm and shows improvement of convergence speed for smaller fractional order.
Keywords: fractional order; sparse system identification; least mean square; LMS; zero-attracting.
A genetic algorithm-based tasks scheduling in multicore processors considering energy consumption
by Hassun Vakilian Zand, Mohsen Raji, Hossein Pedram, Hossein Heidari SharifAbadi
Abstract: Energy consumption has been always an important issue in multicore processors which are getting more and more popular in embedded systems. In this paper, we propose an energy-aware task scheduling approach taking advantages of heuristic algorithms based on genetic algorithm. The proposed approach includes both static and dynamic scheduling schemes. The task scheduling is modelled as a genetic algorithm problem which is mainly used when the tasks are ready before run-time; i.e., static task scheduling. The tasks which arrive after beginning task execution are dynamically scheduled using a proposed heuristic algorithm in combination with the genetic algorithm. The experimental results show that the proposed algorithm achieves more energy efficiency in both static and dynamic task scheduling for multicore processors as compared with similar energy-aware scheduling methods.
Keywords: multicore processor; task scheduling; energy consumption; genetic algorithm; evolutionary algorithm; embedded system; heuristic algorithm.
A new software-defined network architecture to solve energy balance problems in wireless sensor networks
by Yinxiang Qu, Yifei Wei, Mei Song, Dan Liu, Xiaojun Wang
Abstract: Wireless sensor networks (WSNs) are an important part of the internet of things. In WSNs, the sensor battery is limited, and the entire network has a data aggregation node called Sink. When the sensor transmits data to the Sink in a multi-hop manner, the sensors around the Sink also help other sensors to forward the data to the Sink. This causes those sensors to die faster than other sensors. This problem is called energy hole problem. Previous solutions such as sensor uneven distribution schemes and clustering schemes are not only difficult to implement, but also increase the design cost of the system. In particular, traditional WSNs cannot be updated once deployed. In order to solve the above problem, we propose a method of combining SDN and WSNs. The simulation results show that the proposed method has good performance in terms of the number of surviving sensors, network capacity and residual energy.
Keywords: soft-defined network; SDN; wireless sensor networks; WSNs; energy hole.
Localisation algorithm based on weighted semi-definite programming
by Jianfeng Lu, Xuanyuan Yang
Abstract: In order to improve the performance of reduced complexity positive semi-definite programming (RCSDP) algorithm based on time difference of arrival (TDOA), a weighted positive semi-definite programming (WSDP) scheme is proposed in this paper. Based on the squared distance differences between the target node to one anchor node and to the other anchor node, the location of the target node is described as the optimal solution of a non-convex optimisation problem. The semi-definite relaxation technique is used to transform the original non-convex problem into a weighted convex problem, which takes the measurement noise into consideration, and then the estimated location of the target node is obtained. The simulation results show that the localisation performance of WSDP algorithm is better than that of RCSDP algorithm, regardless of whether the target node is located inside or outside the area surrounded by anchor nodes.
Keywords: localisation; semi-definite programming; time difference of arrival; TDOA; weight factor.
An improved method for indoor positioning based on ZigBee technique
by Jiaqi Zhen, Boshen Liu, Yanwei Wang, Yong Liu
Abstract: With the popular use of smart mobile terminals, it is possible to locate the mobile terminals. The traditional indoor positioning methods often bring about large errors under the circumstance of interference. In this paper, an improved K-nearest neighbourhood method based on ZigBee wireless sensor network is proposed, it selects K nearest database vectors from the minimum distance, and calculate the average of these coordinates as the final location result. The accuracy of indoor positioning is effectively improved, meanwhile, there is no significant increase of the computing time. The effectiveness of the method has been verified in the end.
Keywords: indoor positioning; ZigBee; K-nearest neighbourhood method; wireless communication; internet of things; wireless sensor network; received signal strength; mesh network; ad-hoc network; database; embedded systems.
DOA estimation of wideband sources by sparse recovery based on uniform circle array
by Jiaqi Zhen, Yanwei Wang, Yong Liu
Abstract: The conventional direction of arrival (DOA) estimation by compressed sensing is based on wideband focusing and sparse recovery in discrete domain. The former needs high signal to noise ratio (SNR), and the latter requires the space division, or their performance will degrade seriously. Therefore, a new algorithm of wideband DOA calculation in frequency domain based on uniform circle array (UCA) is proposed, the optimisation problems are respectively disposed at each frequency, then the sparse support set is acquired by the corresponding semidefinite programming, after that DOA is calculated through fusing the data of every frequency, simultaneously the original sources can also be reconstructed. This algorithm averts the grid partition in discrete domain, and it also performs well in condition of small samples and low SNR.
Keywords: direction of arrival; wideband sources; sparse recovery; uniform circle array; array signal processing; semidefinite programming; linear frequency modulation; support set; signal parameter estimation; embedded systems.
Improved Faster R-CNN identification method for containers
by Ning Chen, Xiaohu Ding, Hongyi Zhang
Abstract: In a complex port environment, the fast and effective automatic visual recognition of containers is an important part of the intelligent operation and management of ports. Due to the large amount of container image data of complex scale and shape, the traditional target detection and recognition algorithm is limited by the illumination, weather and scenes of the port, it has created challenges and difficulties in port container recognition and identification. This paper proposes a deep learning method for container target recognition detection based on the Faster R-CNN framework, the deep separable network structure is introduced into the VGG network, and the DS-VGG network is designed to improve the accuracy while reducing the network parameters to improve the recognition speed, by introducing the adversarial spatial transformer network (ASTN) to the Faster R-CNN network training to enhance the diversity of data features and improve recognition performance. In order to enhance the convolution feature extraction of container targets, a strategy training network that enhances sample target foreground features, multi-scale training learning and data amplification are used. Finally, the performance test and comparison test of the improved model proposed in this paper are carried out. The test results show that the target recognition speed is 50 frames/s on the container test set, the average accuracy rate is 97.7% and the recall rate is 94.45%. Compared with Faster R-CNN, the recognition performance is significantly improved in complex scenes such as fog, rain and night.
Keywords: container; port intelligence; deep learning; target recognition; Faster R-CNN network; adversarial spatial transformer network; ASTN.
Design of network intrusion detection system based on parallel DPC clustering algorithm
by Jing Wang, Dezhi Han
Abstract: With the advent of the era of big data, network intrusion detection systems based on K-means algorithm cannot meet the detection efficiency and detection speed requirements in big data environment. The DPC algorithm can be applied to high-dimensional network traffic and large-scale data application environments, but there are problems of large calculated amount and limited serial processing capability. Aiming at the problems of DPC algorithm, the DPC algorithm is adjusted firstly to improve the clustering accuracy of the algorithm. Then, the DPC algorithm a parallelised on the Spark platform, so that the processing ability and running speed of the DPC algorithm is greatly improved by running in parallel in the memory of multiple virtual machines. The experimental results show that the network intrusion detection system based on parallel DPC clustering algorithm has higher detection rate and lower false rate. The parallelisation clustering efficiency is much higher than the single-computer clustering efficiency.
Keywords: DPC; clustering; network intrusion detection; Spark; parallel.
Based on the GF1 and GF4 radiation calibration analysis
by Shao Wen, Tao Zui, Xie Yong, John J. Qu, Huan Hai, Tian Chuan Yang
Abstract: In order to test the in-orbit radiation characteristics of GF-1 and GF-4, and obtain accurate radiometric calibration coefficients, this paper proposes a MODIS and LandSat 8 cross-calibration method based on Dunhuang radiation correction field and a method for comparing the radiance error of GF-1 and GF-4 satellite images based on uniform regions. First, this article introduces the cross-calibration principle and the overview of the uniform target sample area, and compares the band matching and spectral response of the four remote sensors. Then, cross-calibration based on the scenes of the same area at the same time of GF-1, GF-4, MODIS, and LandSat 8 on 28 July 2017. The results show that the calibration coefficient error obtained by this method is within 6%. The error of the calibration coefficient obtained by cross-calibration with LandSat 8 satellite is smaller, which satisfies the basic remote sensing quantitative demand. The GF-1 satellite has high calibration accuracy and is suitable for cross-calibration research of China's independent satellite remote sensors. It is of great significance for the establishment of China's in-orbit calibration system.
Keywords: cross calibration; Dunhuang radiation correction field; GF-1; MODIS; GF-4; LandSat 8; visible light; near infrared.
Comparative analysis on detection performance with ground-based microwave radiometer and radiosonde
by Lina Wang, Chaoyao Shen, Sheng Yuan, Yongjun Ren, Yong Wang, Jinyue Xia
Abstract: Ground-based microwave radiometer is a new type of automatic atmospheric detection device with high precision and multi-channel. In order to investigate the reliability of detection data, this article compares and analyses the meteorological elements retrieved from microwave radiometer (MWR) and observational data from radiosonde. The results show that temperature profile and water vapour density profile from MWR have better positive correlation with those from radiosonde. The temperature retrieved from MWR is lower than that from radiosonde in non-precipitation condition, but higher in precipitation condition. The bias may be related to sampling methods, MWR retrieval methods, precipitation and so on.
Keywords: comparative analysis; microwave radiometer; radiosonde; temperature; water vapour density; correlation.
Research on urban transport network topology vulnerability identification under rainfall conditions
by Weiwei Liu, Yang Tang, Fei Yang, Yi Dou, Jin Wang
Abstract: Under the impact of global climate change, meteorological disasters, such as rainstorms, have occurred frequently during recent years and have led to several severe disruptions of transport network in urban areas. In order to minimise the risk of potential losses of life and properties, this paper presents a method to identify topology vulnerability of road networks for a range of rainfall scenarios. Meanwhile, a review is presented regarding the theories and methods of vulnerability analysis. The paper defines the concept of vulnerability of road network considering the intensity of rainfall. It establishes a comprehensive assessment index of 'importance of nodes and edges' by combining the 'betweenness and traffic flow' to find out the source of road network fragility considering the intensity of rainfall. Afterwards, the assessment index 'mincuts frequency vector' is introduced into the assessment of topology vulnerability. This paper provided the scientific basis for disaster control and reduction of the urban road network.
Keywords: urban traffic; topology vulnerability; road network; rainfall; mincuts frequency vector.
Special Issue on: ICCIDS 2018 Computational Intelligence in Embedded Systems
A feature selection model for prediction of software defects
by Amit Kumar, Yugal Kumar, Ashima Kukkar
Abstract: Software is a collection of computer programs written in a programming language. Software contains various modules which make it a complex entity and it can increase the defect probability at the time of development of the modules. In turn, cost and time to develop the software can be increased. Sometimes, these defects can lead to failure of entire software. It will lead to untimely delivery of the software to the customer. This untimely delivery can responsible for withdrawal or cancellation of project in future. Hence, in this research work, some machine learning algorithms are applied to ensure timely delivery and prediction of defects. Further, several feature selection techniques are also adopted to determine relevant features for defect prediction.
Keywords: software; defect; prediction; classifier; feature selection; cognitive weight.
Attribute-based access control and authentication mechanism using smart cards for cloud-based IoT applications
by B.B. Gupta, Megha Quamara
Abstract: With exploding growth in information technology (IT), numerous services and applications having enhanced capabilities are coming into picture with an aim to serve the users. Internet of things (IoT) along with its enabling cutting-edge technologies is establishing a scenario where these services can be utilised effectively. However, with large number of users and applications, it becomes challenging to safeguard the identifying information being transmitted to provide access to these services. This paper presents a refined version of an integrated attribute-based access control and authentication mechanism using smart cards for cloud-based IoT applications. System-wide attributes not only restrict the users to access the remote cloud services, but also ensure user anonymity. We also implement the proposed mechanism on ACPT and AVISPA tool for its validation and to verify its correctness. Moreover, we present an analysis of its security and performance efficiency on the basis of different parameters.
Keywords: attribute; access control; authentication; authorisation; smart cards; cloud; internet of things; IoT; access control policy testing; ACPT; AVISPA; on-the-fly model checker; OFMC.
Feature selection optimisation of software product line using metaheuristic techniques
by Hitesh Yadav, A. Charan Kumari, Rita Chhikara
Abstract: The role of software product line (SPL) is very important in representing the same system with multiple variants. Feature models are used to define SPL. In this paper, genetic algorithm (GA), hyper-heuristic algorithm and particle swarm optimisation (PSO) have been applied for feature selection optimisation in SPL. Also, an improved fitness function is applied for optimisation of features in SPL. The objective function is designed by taking reusability and consistency of features (components) into consideration. Furthermore, we have used a case study and discussed about software product line in detail. A non-parametric test, i.e., Kruskal-Wallis test has been performed to analyse performance and computation time of 20 to 1,000 features sets and identify core features. Through extensive experimental analysis, it is observed that PSO outperforms GA and hyper-heuristic algorithm.
Keywords: genetic algorithm; product line; feature model; particle swarm optimisation; PSO; software product line; SPL; hyper-heuristic evolutionary algorithm.
Self-adjustive DE and KELM-based image watermarking in DCT domain using fuzzy entropy
by Virendra P. Vishwakarma, Varsha Sisaudia
Abstract: With advances in machine learning and development of neural networks that are efficient and accurate, this paper explores the use of kernel extreme learning machine (KELM) to develop a semi-blind watermarking technique for grey-scale images in discrete cosine transform domain. Fuzzy entropy is employed for selection of the blocks where the watermark bits are to be embedded. A dataset formed from these blocks is used to train KELM. The nonlinear regression property of KELM predicts the values where watermark bits are embedded. Self-adjustive differential evolution (SeAdDE) controls the strength of the scaling factors finds their optimal values. The adaptiveness of differential evolution (DE) helps in self-adjustment and varies the DE parameters to explore best solutions. This saves time as the manual hit and trial method for finding the appropriate parameter values is avoided. The scheme presented shows robustness against various attacks like histogram equalisation, resizing, JPEG compression, Weiner filtering, etc. and still also retains the quality of the watermarked image. Thus, the proposed technique can be used as a solution to ensure authenticity via watermarking.
Keywords: image watermarking; kernel extreme learning machine; KELM; self-adjustive DE; fuzzy entropy; grey-scale images; discrete cosine transform; DCT.
Special Issue on: Lightweight Solutions for Cyberspace Security Research Advances and Challenges
Design of an outdoor position certification authority
by Roberto De Prisco, Alfredo De Santis, Pompeo Faruolo, Marco Mannetta
Abstract: We present the design of an outdoor position certification authority. Such an authority aims at certifying the geolocalisation of a mobile device equipped with a global navigation satellite system receiver. Such a receiver is capable of acquiring radio signals (low-level data) and navigation messages (high-level data) in outdoor environments coming from different constellations of global/regional satellite navigation systems and satellite-based augmentation systems. To date, this information is unreliable from a security point of view because it can be easily forged by malicious attackers through specialised spoofing techniques. An outdoor position certification authority defines a client/server architecture through which a user can certify his position by sending the geolocalisation information needed to verify it to one or more remote servers. There are several scenarios for which this service can be very useful and, with the advent of the internet of things age, devices that might require such a service will grow in number.
Keywords: certification authority; outdoor positioning; geolocalisation; global navigation satellite system; GNSS; GPS; spoofing.
Decentralised control-based interaction framework for secure data transmission in internet of automated vehicles
by Brij B. Gupta, Megha Quamara
Abstract: Recent technological advancements in the field of communication and control are transforming the transportation industry by extending the capabilities of conventional human-controlled vehicles to partially or fully automated vehicles. These vehicles create a network, also termed as internet of automated vehicles (IAV), having the capability of sensing the data from the surroundings and using it as feedback mechanism in order to assist drivers and the static infrastructure for safe navigation and control. However, a uniform framework is required to isolate the interactions among the vehicles and different entities for secure transmission and control. In this paper, we propose a decentralised control-based interaction framework to promote smooth transmission of sensor data in automated vehicular systems and verify the correctness of the underlying policy model on access control policy testing (ACPT) tool. In addition, we present some case studies to show the effectiveness of the proposed framework in real-time applications.
Keywords: automated vehicles; decentralised control; security; policy; provenance; embedded sensors; transportation; access control policy testing; cloud; fog.
An experimental estimate of the impact produced on PNU by new generation video codecs
by Andrea Bruno, Giuseppe Cattaneo
Abstract: The resolution of video cameras has increased considerably in recent years leading to a new generation of codecs with higher compression rates, such as H.265, VP9 and AV1. However, compression can heavily affect the noise present in each frame. Other encoder specific features, like the intra-frame prediction for H.265 and AV1, can flatten the pixel non-uniformity (PNU) noise. We implemented a test-bed to establish whether source camera identification can still be achieved using the PNU when videos are encoded in those formats. The experiments proved that the results are less accurate than those obtained from videos encoded with traditional codecs. Nevertheless, the most original result achieved was the methodology we designed. In order to avoid hidden artifacts the source videos were initially acquired as a raw data stream from a dedicated embedded system and then recorded with an open-source YUV encoder.
Keywords: pixel non-uniformity; PNU; pixel non-uniformity noise; sensor pattern noise; video forensics; source camera identification; video codecs; video compression; intra-frame prediction; embedded systems; experimental estimate; H.264; H.265; VP9; AV1.
Lightweight and efficient approach for multi-secret steganography
by Katarzyna Koptyra, Marek R. Ogiela
Abstract: This paper compares an efficiency of two approaches of multi-secret steganography in lightweight systems: interlacing and multi-level. The study was conducted for two and three secrets with use of F5 algorithm for both approaches. The embedding times were measured with and without I/O operations. At the end the application of these techniques in lightweight solutions is discussed.
Keywords: steganography; internet of things; IoT; efficiency; interlacing; multi-level steganography; image steganography.
A survey on screenlogger attacks as well as countermeasures
by Hugo Sbai, Jassim Happa, Michael Goldsmith, Samy Meftali
Abstract: Keyloggers and screenloggers are one of the active growing threats to user's confidentiality as they can run in user-space, easily be distributed and upload information to remote servers. They use a wide number of different technologies and may be implemented in many ways. Keyloggers and screenloggers are very largely diverted from their primary and legitimate function to be exploited for malicious purposes compromising the privacy of users, and bank customers notably. Due to the recent multiplication of mobile devices with a touchscreen, the screenlogger threat has become even more dangerous. This threat is even harder to fight given the limited resources of the affected devices. This paper is the first step of a project aiming at proposing efficient countermeasures against screenloggers. It provides a complete overview of the different techniques used by this malware and discusses an extensive set of plausible counter measures.
Keywords: screenlogger; virtual keyboard; noise; screenshot; malware detection; OCR; shoulder surfing.
Secure RGB image steganography based on modified LSB substitution
by Laiali Almazaydeh
Abstract: Different steganography techniques have been presented based on RGB image as the image is considered secure cover for hidden data. Specifically, this paper presents edge-based image steganographic method using parameterised Canny edge detector that relies on embedding the secret message bits into variable LSB length of the blue colour channel of the cover image. The blue colour channel is selected because the steganography-based research showed that the visual perception of blue colour intensity is less distinct than the red and green colours. Secret message bits are embedded up to four bits of LSB which are selected by a random number generator. Additionally, as the less significant information is carried out by the LSB of each pixel so slight changes of LSB will not affect the visual quality of the cover image. The proposed algorithm was tested on a set of RGB colour images, and satisfactory results were demonstrated regarding minimum distortion in the blue colour of a pixel and visually identical original and stego image.
Keywords: security; cryptography; steganography; LSB; Canny edge detector; RGB image.
Modelling performances of an autonomic router running under attack
by Lelio Campanile, Marco Gribaudo, Mauro Iacono, Michele Mastroianni
Abstract: Modern warehouse-scale computing facilities, seamlessly enabled by virtualisation technologies, are based on thousands of independent computing nodes that are administered according to efficiency criteria that depend on workload. Networks play a pivotal role in these systems, as they are likely to be the performance bottleneck, and because of the high variability of data and management traffic. Because of the scale of the system, the prevalent network management model is based on autonomic networking, a paradigm based on self-regulation of the networking subsystem, that requires routers capable of adapting their policies to traffic by a local or global strategy. In this paper we focus on performance modelling of autonomic routers, to provide a simple, yet representative elementary performance model to provide a starting point for a comprehensive autonomic network modelling approach. The proposed model is used to evaluate the behaviour of a router under attack under realistic workload and parameters assumptions.
Keywords: performance evaluation; network security; autonomic networks; Petri nets; embedded systems; stochastic models; autonomic router; attacks to autonomic networks; network resilience; cloud networking; cloud security.
Research on intelligent obstacle avoidance control method for mobile robot in multi-barrier environment
by Yafei Wang, Ming Ma
Abstract: In a current multi-obstacle environment, mobile robots are needed to control obstacle avoidance ability intelligently. Therefore, a method of mobile robot obstacle avoidance control in multi-obstacle environment is proposed based on the minimum risk index. This method first segments the path of obstacle environment according to certain rules. Then, robots are constrained through the constrained time according to requirements of robot obstacle avoidance. Therefore, calculating motion obtains necessary and sufficient conditions with no collision avoidance relationship. Then, impact danger level of each movement stage for the robot is evaluated by considering the spatial distance, inertia, and relative movement speed of multiple obstacle environments. The global safety path is planned, the intelligent avoidance function is calculated, and intelligent avoidance control is implemented. Finally, simulation results show that our proposed method is a common method in intelligent obstacle avoidance, timely tracking of moving target, and makes full use of space.
Keywords: multi-obstacle; mobile robot; intelligence; obstacle avoidance control.
Heuristic approach to minimise the energy consumption of sensors in cloud environment for wireless body area network applications
by P. Kumaresan, Manoharan Prabukumar, S. Subha
Abstract: The wireless sensor networks are single user centric and end users who do not own sensors are unable to have access to any wireless network specific application. This sensor cloud is a new paradigm to manage the physical sensors which are deployed in any WSN application. Existing research work on sensor cloud is limited to guaranteeing minimal energy consumption. In this paper, a novel mathematical model based on virtual sensor grouping is proposed to minimise the cumulative energy consumption of sensors in cloud environment. Energy consumption of non-virtual sensor with virtual sensor group for two different applications is compared and the results are shown. Further the proposed sensor cloud infrastructure with power model is compared with traditional WSN with respect to energy efficiency, throughput and performance with quick synchronisation time for random run trials and results were found to be better than the conventional WSN.
Keywords: physical sensors; virtual sensor group; sensor cloud infrastructure; WSN; energy consumption; WBAN applications.
Mass internet of things data security exchange model under heterogeneous environment
by Wenbo Fu
Abstract: At present, the data classification based on SOA data exchange method of internet of things (IoT) data is not perfect, the effectiveness of data filtering is low, and the security of data exchange is poor. In this paper, the mass data of IoT are classified by transfer-boost method. The auxiliary training data are used to help source training data and build a reliable classifier to make the classifier more accurate in the test data. Hedge grammar is used to process massive data of heterogeneous IoT. The buffer mechanism is introduced to deal with the unstable data flow in the IoT, so as to enhance the effectiveness of data filtering, and realise the secure data exchange through modules such as server request, identity authentication and receiving data. Experimental results showed that the proposed model can improve the classification accuracy and data filtering effect, and achieve a more secure data exchange effect.
Keywords: heterogeneous environment; internet of things data; IoT; security exchange.
Tracking algorithm of weak disturbance signal under multi-device interference in internet of things
by Shuai Yang, Zhi-Hui Zou, Gihong Min
Abstract: It was easy to generate weak disturbance signals under the condition of multi device interference and was necessary to track and control the signals. There were some errors in the traditional signal tracking methods. However, tracking accuracy was low. Therefore, a new tracking algorithm for weak disturbance signals with multi-device interference in the internet of things was proposed. This method was a new method which combined the full differential Thevenin equivalent parameter tracking method and the disturbance signal control method of the internet of things. Before tracking and controlling the weak disturbance signal, the complex wavelet detection method was used to detect the disturbance signal, and then the David superconducting magnetic method was used to track the weak disturbance signal. Experimental results showed that the proposed method can make the system run stably at the minimum cost. Besides, tracking accuracy of the proposed method was about 2.2%, while that of the traditional method was about 0.5%, which indicated that the proposed method can effectively track the weak disturbance signal of the internet of things under the interference of multiple devices, and made the internet of things in a stable operation state.
Keywords: equipment interference; internet of things; weak disturbance signal; tracking; signal tracking.
A channel matching scheme for cross-chain
by Wei She, Zhi-hao Gu, Wei Liu, Jian-sen Chen, Bo Wang, Zhao Tian
Abstract: At present, the cross-chain technology works on becoming a bridge to build trust and transmit the information among chains. However, most of the existing schemes have poor universality, and they also have insufficient protection for the process of information cross-chain transmission. Contraposing the deficiencies above, we propose a channel matching scheme for cross-chain (CMSCC), CMSCC combines the ideas of the relay chain scheme and the channel in Fabric. The relay chain named chain-anchor and relay-chain (CARC) can be connected to other blockchains by multi-blockchain communication and consociation protocol (MBCCP). In CARC, the order peers will match the peers which on different chains and will create the peer matching channel (PMC) among matched peers. So, the information can be transmitted among matched peers. Finally, the experiment verifies that CMSCC enables information to be transmitted among chains through PMC, and CMSCC can also protect the process of information cross-chain transmission by PMC simultaneously.
Keywords: blockchain; cross-chain; relay chain; channel; transmission.
A novel algorithm for TOP-K optimal path on complex multiple attribute graph
by Kehong Zhang, Keqiu Li
Abstract: In the rapidly-changing information world, the various users and personalised requirements lead to an urgent need for complex multiple attribute decision-making. In addition, the TOP-K path of single attribute decision cannot meet the actual needs. The TOP-K mainly has non-repeatable vertex, repeatable vertex, index and other algorithms. But these techniques are mainly based on the single attribute. There are few documents introducing the complex multiple attribute decision-making problem so for. Therefore, a Tdp algorithm is presented in this paper. Firstly, it uses the technology of interval number and extreme value to solve the uncertain attribute value. Then, TOPSIS technique solves the complex multiple attribute decision-making problems. By this way, the comprehensive score is achieved. Secondly, the paper proposes blocking and bidirectional shortest path algorithm for TOP-K optimal path. Finally, comparison and analysis between Tdp and the Yen were made. Result confirms that Tdp algorithm improves the TOP-K optimal technology.
Keywords: multiple attribute; optimal path; blocking; bidirectional; deviation vertex; TOP-K; decision-making; network.
A novel Monte Carlo-based neural network model for electricity load forecasting
by Binbin Yong, Zijian Xu, Jun Shen, Huaming Chen, Jianqing Wu, Fucun Li, Qingguo Zhou
Abstract: The ongoing rapid growth of electricity over the past few decades greatly promotes the necessity of accurate electricity load forecasting. However, despite a great number of studies, electricity load forecasting is still an enormous challenge for its complexity. Recently, the developments of machine learning technologies in different research areas have demonstrated their great advantages. General vector machine (GVM) is a new machine learning model, which has been proven very effective in time series prediction. In this article, we apply it in electricity load forecasting. A detailed comparison with traditional back-propagation neural network (BP) is presented in this paper. To improve the load forecasting accuracy, we propose many methods to train the GVM model. Analysis of our approach on the historical Queensland electricity load dataset has demonstrated that GVM could achieve better forecasting results, which shows the strong potential of GVM for general electricity load forecasting.
Keywords: electricity load forecasting; general vector machine; GVM; time series prediction; neural network.
A novel localised network coding-based overhearing strategy
by Zuoting Ning, Lan He, Dafang Zhang, Kun Xie
Abstract: Network coding is a very effective approach to improve network throughput and reduce end-to-end delay. However, the existing approaches cannot thoroughly solve the problem of how to deal with newly overheard packets when the buffer is full, meanwhile, coding node does not schedule the packets in coding queue according to the packets' information in overhearing buffer. As a result, these methodologies lack flexibility and require quite a few assumptions. To address these limitations, we propose a new network coding overhearing strategy which is based on data packet switching and scheduling (DPSS) algorithm. First, when overhearing buffer is full and the sink nodes have overheard new packets, sink nodes will drop the recently overheard packets but record their IDs; second, sink nodes report the packets' information to the coding node that schedules the packets in coding queue for ease of encoding. Finally, sink nodes delete the packets which have been used for decoding, and call for the ever dropped packets when decoding ratio reaches the threshold. Theoretical analysis and simulation demonstrate that, compared with traditional overhearing policies, our scheme gets higher coding ratio and less delay.
Keywords: network coding; data packet switching and scheduling; DPSS; overhearing; threshold; throughput; delay.
An algorithm for determining data forwarding strategy based on recommended trust value in MANET
by Jianbo Xu, Shu Feng, Wei Liang, Jian Ke, Xiangwei Meng, Ruili Zhang, Danping Shou
Abstract: In mobile ad hoc networks (MANET) with selfish nodes and malicious nodes, the network performance is seriously affected. We propose an algorithm based on the recommended trust value, i.e., collaborative computing trust model (CCTM) algorithm, to decide the data forwarding strategy. In the algorithm, the carrier node carrying the message collects recommended data of neighbour nodes adopts K-nearest neighbour (KNN) algorithm principle to filter the false recommended data and select K neighbour nodes as collaborative computing nodes to calculate the recommended trust value of neighbour nodes respectively, and then selects the neighbour node with the highest recommended trust value as the next hop node. The simulation experiments show that when the selfish and malicious nodes number is 10, CCTM is higher than Epidemic algorithm and MDT about 3% and 8% respectively in terms of transmission success rate; CCTM is higher than Epidemic about 14% and lower than MDT about 15% in terms of average transmission delay; CCTM is lower than MDT about 3% in terms of routing overhead. Overall, CCTM algorithm not only has better performance in terms of transmission success rate, delay and routing overhead, but also improves the security of data transmission.
Keywords: mobile ad hoc networks; MANET; selfish nodes; malicious nodes; trust model.
Hierarchical bucket tree: an efficient account structure for blockchain-based system
by Weili Chen, Zibin Zheng, Mingjie Ma, Pinjia He, Yuren Zhou, Jing Bian
Abstract: Systems built on top of blockchain are often inefficient. One reason for the inefficiency is that these systems include too many hash operations. To address this problem, we conduct an empirical study on the transaction history of a real-world blockchain-based system (i.e., Ethereum), which contains 300,821 accounts and 14,240,095 transactions. We found that the account usage frequency is highly heterogeneous. Based on this finding, this paper presents hierarchy bucket tree (HBT), an efficient account structure with user transaction behaviour information embedded, to reduce the number of hash operations and thus enhance the efficiency of blockchain-based systems. Extensive experiments have been conducted and the experimental results show that HBT reduces hash operations by nearly 80% compared with the existing account structure.
Keywords: blockchain; Ethereum; tree structure; hyperledger; account structure; hash operation.
Special Issue on: CPSCom2018 Human-Centered Cloud/Fog/Edge Computing in Cyber-Physical-Social Systems
Hash-based and privacy-aware movie recommendations in a big data environment
by Tingting Shao, Xuening Chen
Abstract: Movie recommendation is an important activity in the people's daily entertainment. Typically, through analysing the users' ever-watched movie list, a movie recommender system can recommend appropriate new movies to the target user. However, traditional movie recommendation techniques, e.g., collaborative filtering (CF) often face the following two challenges. First, as CF is essentially a traversal technique, the recommendation efficiency is often low. Second, traditional movie recommender systems often assume that the users' ever-watched movie list for decision-making is centralised, which makes it hard to be applied to the distributed movie recommendation scenarios. In view of these challenges, in this paper, we bring forth an efficient and privacy-aware online movie recommendation approach based on hashing technique. Through experiments on famous MovieLens dataset, we show that our proposal shows a better performance compared with other approaches in terms of recommendation efficiency and accuracy while users' private information is protected.
Keywords: movie recommendation; collaborative filtering; efficiency; privacy preservation; SimHash.
A hybrid model of empirical wavelet transform and extreme learning machine for dissolved oxygen forecasting
by Juan Huan, Weijian Cao, Yuwan Gu, Yilin Qin
Abstract: The accurate predicting trend of dissolved oxygen (DO) can reduce the risks to aquaculture, so a combined nonlinear prediction model based on empirical wavelet transform (EWT) and extreme learning machine (ELM) optimised by adaptive disturbance particle swarm optimisation (ADPSO) is proposed. First of all, DO series are decomposed into a term of relatively subsequence by EWT, secondly, the decomposed components are reconstructed using the C-C method, and thirdly an ELM prediction model of every component is established. At last, the predicted values of DO datasets are calculated by using RBF to reconstruct the forecasting values of all components. This model is tested in the special aquaculture farm in Liyang City, Jiangsu Province. Results indicate that the proposed prediction model of EWT-ELM has better performance than WD-ELM, EMD-ELM, ELM and EWT-BP. The research shows that the combined forecasting model can effectively extract the sequence characteristics, and can provide a basis for decision-making management of water quality, which has certain application value.
Keywords: water quality prediction; hybrid model; dissolved oxygen; empirical wavelet transform; EWT.
An improved TFIDF algorithm based on dual parallel adaptive computing model
by Yuwan Gu, Yaru Wang, Juan Huan, Yuqiang Sun, Shoukun Xu
Abstract: The double parallel cloud computing framework based on graphics processing unit (GPU) and MapReduce is proposed. The method aims at the low efficiency for the large data sets on the stand-alone by text categorisation algorithm, constructs the adaptive computation process of double parallel computing and combines the advantage of improved term frequency-inverse document frequency (TFIDF) algorithm, and improves TFIDF text categorisation algorithm with double parallel adaptive computing. In different operating environments, the efficiency of improved TFIDF algorithm will be compared with different computing nodes. The result shows that the improved TFIDF based on dual parallel adaptation has an increase of 6.48% on Macro_F1 compared to the TFIDF based on CPU, and the operating efficiency has increased by nearly seven times. With the number of nodes increasing, the algorithm execution efficiency with double parallel adaptive computing is getting more and more effective.
Keywords: improved TFIDF algorithm; MapReduce; graphics processing unit; GPU; parallel computation.
A homomorphic range searching scheme for sensitive data in internet of things
by Baohua Huang, Sheng Liang, Dongdong Xu, Zhuohao Wan
Abstract: With the popularisation and development of the internet of things, big data and cloud computing, the search of data in cloud-based internet of things becomes a hot research topic. However, the sensitive data, such as the medical data collected by wearable devices, is inevitable to be stored in the cloud server. Homomorphic encryption has the ability to calculate the ciphertext without decryption. We separate the calculating and the decryption into different security domains to preserve the privacy of sensitive data, so the original plaintext would not be exposed in the cloud. Hence, we can compare two ciphertexts to get the difference of them in a privacy preserving way. In order to accelerate the search process of range query, we build an encrypted self-balancing binary index tree. Based on oblivious RAM, the searching scheme can hide the access patterns of the node of tree. The actual nodes and logic relation of tree are stored on different servers. A sample implementation of the proposed scheme is given, and the experimental results and analysis are presented to illustrate the scheme's effectiveness and security.
Keywords: internet of things; range search; homomorphic encryption; oblivious random-access memory.
Maintenance cycle optimisation of multi-component systems under the constraints of overall cost and reliability
by Hong Xiao, Rongyue Zhang, Zhigang Chen, Yingshuang Liu, Yubin Zhou
Abstract: Periodic maintenance inevitably leads to 'over-maintenance' or 'non-timely maintenance' of equipment or systems. To improve reliability and reduce maintenance cost, this paper presents a dynamic maintenance cycle optimisation model for multi-component systems. In this paper, the influence of different maintenance intervals on system reliability and total maintenance cost is determined under the conditions that the system has a limited service time and the components are not fully maintained. The model uses the optimisation of system maintenance cost as the objective function to determine the optimal preventive maintenance times and maintenance intervals under the constraint of the lowest reliability of the system. The simulation results of the final example verify that the proposed method can significantly improve the maintenance cost, system reliability, and reliability fluctuation stability. And it can be used to guide subway maintenance cycle optimisation.
Keywords: subway vehicles; lifetime maintenance cost; imperfect maintenance; optimisation; multi-component.
Special Issue on: Advances in Intelligence, Security, Privacy and Trust Technologies for the Social Internet of Things
Solution to the conformable fractional differential systems with higher order
by Yong-fang Qi, Liang-song Li, You-hua Peng
Abstract: This manuscript presents a method to solve the conformable fractional differential systems. In the first place, some results about conformable fractional are introduced. Secondly, the method used to get the general solutions of conformable fractional equations is developed. Finally, some examples of application of the method are presented.
Keywords: conformable fractional; differential system; general solution.