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IEEE International Conference on Communication and Information Systems (ICCIS)

作者： Daren Bi Linghua Zhang Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Provincial Engineering Research Center of Telecommunications and Network Technology Nanjing China

ISBN: (数字)9781728162973

ISBN: (纸本)9781728162980

For classic wireless sensor networks (WSN), the received data is only stored and forwarded by nodes [1]. Existing research has shown that network coding can make the nodes form a store-encode-forward mode, which is a great improvement on the performance of wireless sensor networks. According to the above analysis, this paper chooses the SPIN protocol as our research object and looks for opportunities to use the network coding in the SPIN protocol when the intermediate node broadcasts the ADV message [2]. On this basis, this paper proposes a new improved routing protocol NC-SPIN based on network coding. Through simulation experiments, it can be found that the NC-SPIN routing protocol has improved throughput and communication performance and decreased energy consumption at intermediate nodes compared to the SPIN protocol.

关键词： network coding Encoding Wireless sensor networks Metadata Throughput Routing protocols

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QoE-Driven Big Data Management in Pervasive Edge Computing Environment

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Big Data Mining and Analytics 2018年第3期1卷 222-233页

作者： Qianyu Meng Kun Wang Xiaoming He Minyi Guo Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunications Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai Jiao Tong University

In the age of big data, services in the pervasive edge environment are expected to offer end-users better Quality-of-Experience(QoE) than that in a normal edge environment. However, the combined impact of the storage, delivery, and sensors used in various types of edge devices in this environment is producing volumes of high-dimensional big data that are increasingly pervasive and redundant. Therefore, enhancing the QoE has become a major challenge in high-dimensional big data in the pervasive edge computing environment. In this paper, to achieve high QoE, we propose a QoE model for evaluating the qualities of services in the pervasive edge computing environment. The QoE is related to the accuracy of high-dimensional big data and the transmission rate of this accurate data. To realize high accuracy of high-dimensional big data and the transmission of accurate data through out the pervasive edge computing environment, in this study we focused on the following two ***, we formulate the issue as a high-dimensional big data management problem and test different transmission rates to acquire the best QoE. Then, with respect to accuracy, we propose a Tensor-Fast Convolutional Neural network(TF-CNN) algorithm based on deep learning, which is suitable for high-dimensional big data analysis in the pervasive edge computing environment. Our simulation results reveal that our proposed algorithm can achieve high QoE performance.

关键词： Quality-of-Experience(QoE) high-dimensional big data management deep learning pervasive edge computing

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WSN Location Algorithm Based on Simulated Annealing Co-linearity DV-Hop 2

WSN Location Algorithm Based on Simulated Annealing Co-linea...

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2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

作者： Sun, Weiguo Zhang, Linghua College of Telecommunication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing Jiangsu210003 China Jiangsu Provincial Engineering Research Center of Telecommunications and Network Technology Nanjing University of Posts and Telecommunications Nanjing Jiangsu210003 China

ISBN: (纸本)9781538618035

Aiming at high error ration in the uneven distribution of network topology technology and the sensitive react to the distance error ration using the least squares method in the algorithm, an optimized positioning algorithm based on simulated annealing is put forward. The optimized positioning algorithm is improved firstly by the method of setting the threshold to realize the selective topological relation and avoid the ranging error. Secondly, the least squares method is replaced by the simulated annealing algorithm. The experiment result is proved that the optimized positioning algorithm has lower error ration than the traditional algorithm. © 2018 IEEE.

关键词： Simulated annealing

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Scale Adaptive Block Target Tracking Based on Multi-layer Convolution Features and Kernel Correlation Filter

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Ting Zhang Dong Hu Jing Zhang Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing 210003 China Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing 210003 China Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing 210003 China

Target tracking is currently a hot research topic in Computer Vision and has a wide range of use in many research fields. However, due to factors such as occlusion, fast motion, blur and scale variation, tracking method still needs to be deeply studied. In this paper, we propose a block target tracking method based on multi-convolutional layer features and Kernel correlation filter. Our method divides the tracking process into two parts: target position estimation and target scale estimation. First, we block the target frame based on the condition number. Second, we extract the features by the convolutional layer and apply it to the kernel correlation filter to get the center position of different block targets. With the reliability of different blocks measured by the Barker coefficient, the overall target position center is obtained. Then, the affine transformation is adopted to achieve the scale adaptation. The algorithm in this paper is evaluated by the public video sequences in OTB-2013. Numerous experimental results demonstrate that the proposed tracking method can achieve target scale adaptation and effectively improve the tracking accuracy.

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Joint DBF and SAO Parallel Filtering Based on Multithread Load Balancing

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Hao Ma Dong Hu Yi Li Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing 210003 China Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing 210003 China Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing 210003 China

This paper presents a joint parallel loop filtering algorithm based on multi-thread load balancing in HEVC decoding, which implements the parallel processing of deblocking filtering (DBF) and sample adaptive compensation (SAO). Because of the diversity of video, the texture of different regions in an image is also different, which leads to various CTU partition methods. Therefore, the number of the boundary to be filtered is greatly different, resulting the computation load among multiple threads unbalanced in parallel processing. To solve this problem, an area division scheme is proposed, which divides the image into multiple areas, and the number of boundaries to be filtered in each area is similar. Then, the mapping relationship table is used to allocate these areas to multiple threads for parallel processing, so as to achieve the load balancing among the filtering threads. Finally, the cache technology is used to combine DBF and SAO to reduce the delay between them and improve the overall parallelism of the loop filter. Experimental results show that the performance of the proposed load balancing joint filtering algorithm is 8.15% higher than the previous scheme.

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A Siamese network Tracking Algorithm Based on Hierarchical Attention Mechanism

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Journal of Physics: Conference Series 2021年第1期1828卷

作者： Hu Zhang Dong Hu Yingcan Qiu Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing 210003 China Nanjing University of Posts and Telecommunications Nanjing 210003 China Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing 210003 China Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Nanjing 210003 China

A siamese network tracking algorithm based on hierarchical attention mechanism is proposed in this paper. In order to obtain more robust target tracking results, different layer features are fused effectively. In the process of extracting features, attention mechanism is used to recalibrate the feature map, and AdaBoost algorithm is used to weight the target feature map, which improves the reliability of the response map. Besides, the Inception module is also introduced which not only increases the width of the network and the adaptability of the siamese network to the scale, but also reduces the parameters and improves the speed of network training. Experimental results show that this method can effectively solve the impact of background clutter and improve the accuracy of tracking.

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Computation Offloading and Resource Allocation for MEC in C-RAN: A Deep Reinforcement Learning Approach 19

Computation Offloading and Resource Allocation for MEC in C-...

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19th IEEE International Conference on Communication technology, ICCT 2019

作者： Jin, Xiaoyan Zhang, Jun Sun, Xinghua Zhang, Ping Cai, Shu Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510006 China Key Laboratory of Wireless Sensor Network and Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Key Laboratory of Computer Application Technology Anhui Polytechnic University Wuhu241000 China

ISBN: (纸本)9781728105352

Mobile edge computing (MEC) technology has become a promising example for cloud radio access networks (CRAN) to provide close-range services, thereby reducing service delays and saving energy consumption. In this paper, we consider a multi-user MEC system and solve the problem of the computation offloading strategies and resource allocation policies. We set the total cost of delays and energy consumption as our optimization goal. However, getting an optimal strategy in a dynamic environment is challenging. Reinforcement learning (RL) aims at long-term cumulative rewards, which are essential for time-varying dynamic systems. Therefore, we propose an optimization framework based on deep RL to solve these problems. The deep neural network (DNN) is used to estimate the value function of the critics, thereby reducing the state space complexity of the optimization target. The actor part uses another DNN to represent a parametritis stochastic strategy and improve the strategy with the help of critics. Compared with other schemes, the simulation results show that the scheme significantly reduces the total cost. © 2019 IEEE.

关键词： Reinforcement learning

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research On TEEN With network Coding 2

Research On TEEN With Network Coding

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2017 IEEE 2nd Information technology, networking, Electronic and Automation Control Conference (ITNEC 2017)

作者： Jiazheng Wang Linghua Zhang College of Telecommunication & Information Engineering NJUPT Jiangsu Provincial Engineering Research Center of Telecommunications and Network Technology

ISBN: (纸本)9781509064151;9781509064144

For traditional routing model in wireless sensor networks(WSN), the function of intermediate nodes is storage and forwarding. Due to the limitation of WSN in energy, capability of communication, computing and the capability of storage, it is difficult for nodes to communicate. Existing researches show that using network coding in WSN can enhance throughput and the security transmission of network, and reduce the energy consumption of nodes [1]. This paper chooses a typical TEEN protocol as the research object of network coding. We attempt to employ network coding to achieve the improvement on throughput of WSN and communication performance, and decrease the energy consumption of sensor nodes. Based on these points, this paper proposes to create network coding opportunities on cluster head nodes in TEEN protocol by utilizing the advantages of opposite links, and a new network coding model basing on TEEN(NCTEEN). The results of simulations show that NCTEEN model has better performance than TEEN in terms of throughput and energy consumption. To be specific, the throughput and performance of network are improved and the energy consumption in cluster head nodes is reduced.

关键词： Wireless sensor network network coding NCTEEN model Cluster head node

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An Optimization Algorithm Based on GA for Multicast network

An Optimization Algorithm Based on GA for Multicast Network

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IEEE International Conference on Automation, Electronics and Electrical engineering (AUTEEE)

作者： Along Xu Linghua Zhang College of Telecommunication & Information Engineering NJUPT Nanjing China Jiangsu Provincial Engineering Research Center of Telecommunications and Network Technology Nanjing China

ISBN: (纸本)9781538678626;9781538678619

In the traditional wireless sensor network (WSN), the multicast router uses the store-and-forward mode to transmit data, which limits the throughput of the wireless sensor network. In 2000, the network coding is proposed by Rudolf Ahlswede, Li Shuoyan et al. Therefore, the wireless sensor network can reach the theoretical maximum throughput, save energy and improve the security of data transmission. However, network coding operations involve complex data operations. Excessive coding consumes a large amount of computer CPU and memory resources. Therefore, how to reduce the number of network coding to reduce the computational cost is a prerequisite academic problem on the premise of reaching rate of multicast. An extremely important academic research issue is called network Coding Resource Minimization (NCRM). Aiming at the NCRM problem in static wireless sensor networks, this paper proposes an improved path-relinking optimization algorithm based on genetic algorithm, which can locate the initialization parameters to the feasible solution region and adopt local search method to search optimal solution. The strategy further enhances the search ability of chromosomes in optimization algorithm. Experiments show that this algorithm spends less coding times and calculation time than the traditional simple genetic algorithm when achieving the same network throughput.

关键词： Biological cells network coding Genetic algorithms Wireless sensor networks Encoding Throughput Optimization

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Optimization of SEP Protocol Based on Relative Residual Energy

Optimization of SEP Protocol Based on Relative Residual Ener...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Chenghui Wu Linghua Zhang College of Telecommunication & Information Engineering NJUPT Nanjing China Jiangsu Provincial Engineering Research Center of Telecommunications and Network Technology Nanjing China

This paper proposes an improved cluster head election algorithm, which mainly solves the problem that the Stable election protocol (SEP), a network topology protocol for wireless sensor networks (WSN), does not take the available energy of nodes into account after the first round election. This solution may cause the node which has less energy to act as the cluster head, causing a problem that this node may be invalid due to excessive energy consumption. In this paper, the relative residual energy is taken as a factor to consider to achieve the purpose of optimizing the network performance. The simulation results indicate that the algorithm has better performance from the perspective of rational use of energy and extension of network life cycle.

关键词： Computational intelligence Design optimization Protocols

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