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International Conference on computer communications and networks (ICCCN)

作者： Siguang Chen Chuanxin Zhao Meng Wu Zhixin Sun Jian Jin Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China School of Electrical Engineering and Computing Curtin University Perth Australia

Since the temporal and spatial correlations of sensor readings are existent in wireless sensor networks (WSNs), this paper develops a clustered spatio-temporal compression scheme by integrating network coding (NC) and compressed sensing (CS) for correlated data. The proper selections of NC coefficients and measurement matrix are designed for this scheme. This design guarantees the reconstruction of clustered compression data successfully with an overwhelming probability and unifies the operations of NC and CS into real field successfully. Moreover, in contrast to other spatio-temporal schemes with the same computational complexity, the proposed scheme possesses lower reconstruction error by employing the independent encoding in each sensor node (including the cluster head nodes) and joint decoding in sink node. At the same time it has lower computational complexity as compared with JSM-based spatio-temporal scheme by exploiting the temporal and spatial correlations of original sensing data step by step. Finally, the simulation results verify that the clustered spatio-temporal compression scheme outperforms the other two compression schemes significantly in terms of recovery error and compression gain.

关键词： Correlation Wireless sensor networks Sensors Encoding Computational complexity Decoding Security

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Gradient sparsification for efficient wireless federated learning with differential privacy

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Science China(Information Sciences) 2024年第4期67卷 272-288页

作者： Kang WEI Jun LI Chuan MA Ming DING Feng SHU Haitao ZHAO Wen CHEN Hongbo ZHU School of Electronic and Optical Engineering Nanjing University of Science and Technology Zhejiang Lab Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education Data61 Commonwealth Scientific and Industrial Research Organisation School of Information and Communication Engineering Hainan University School of Communications and Information Engineering Nanjing University of Posts and Telecommunications School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University

Federated learning(FL) enables distributed clients to collaboratively train a machine learning model without sharing raw data with each other. However, it suffers from the leakage of private information from uploading models. In addition, as the model size grows, the training latency increases due to the limited transmission bandwidth and model performance degradation while using differential privacy(DP)protection. In this paper, we propose a gradient sparsification empowered FL framework with DP over wireless channels, to improve training efficiency without sacrificing convergence performance. Specifically, we first design a random sparsification algorithm to retain a fraction of the gradient elements in each client's local model, thereby mitigating the performance degradation induced by DP and reducing the number of transmission parameters over wireless channels. Then, we analyze the convergence bound of the proposed algorithm, by modeling a non-convex FL problem. Next, we formulate a time-sequential stochastic optimization problem for minimizing the developed convergence bound, under the constraints of transmit power, the average transmitting delay, as well as the client's DP requirement. Utilizing the Lyapunov drift-plus-penalty framework, we develop an analytical solution to the optimization problem. Extensive experiments have been implemented on three real-life datasets to demonstrate the effectiveness of our proposed algorithm. We show that our proposed algorithms can fully exploit the interworking between communication and computation to outperform the baselines, i.e., random scheduling, round robin, and delay-minimization algorithms.

关键词： federated learning differential privacy gradient sparsification Lyapunov drift convergence analysis

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International Symposium on Computational Intelligence and Design, ISCID

作者： Lijun Yang Chao Ding Meng Wu College of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China College of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Key Lab of “Broadband Wireless Communication and Sensor Network Technology” of Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781509035595

The node replica attack is known to be dangerous to wireless sensor networks (WSNs) because it enables the adversary to extend the damage throughout the network with very low cost. To stop such attack, we propose a similarity estimation based scheme with group deployment knowledge. Compared with prior works, our proposal provides extra functionality that prevent replica from generating false location claims. Through simulation experiment, we evaluate the performance of the proposed scheme and demonstrate that our scheme achieves effectiveness and efficiency under different situations and attack strategies.

关键词： Wireless sensor networks Authentication Telecommunications Electronic mail Measurement Encoding

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Reliability based radio frequency identification reader redundant optimization model in electronic product code network

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International Journal of Advancements in Computing technology 2012年第18期4卷 577-584页

作者： Xu, He Wang, Suo-Ping Wang, Ru-Chuan College of Computer Nanjing University of Posts and Telecommunications Nanjing 210003 China College of Automation Nanjing University of Posts and Telecommunications Nanjing 210003 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Jiangsu Nanjing 210003 China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Jiangsu Province Nanjing 210003 China

The Electronic Product Code (EPC) network, originally developed by the Auto-ID Center with its standards now managed by EPCglobal, is designed and implemented to enable all objects in the world to be linked via the Internet for all time. EPC is the next evolution of product identification, utilizing Radio Frequency Identification (RFID) technology to identify objects. RFID technology has been widely used in many areas, and how to deploy of a large number of readers becomes a core challenge due to the limited read range between reader and tag. This paper proposes a novel reader redundant optimization model for finding optimal redundant number of readers and their positions to cover all tags with using optimization algorithm in EPC network. Firstly, we give an overview of the collision problems in RFID system. Secondly, we propose reliability based RFID reader system model. Thirdly, we use ant colony algorithm to solve this optimization problem. Analysis results show the effectiveness of the proposed system model and simulation results show that the algorithm is effective in achieving the goal of this optimal solution.

关键词： Ant colony optimization

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The Proof is in the Pudding: Decision-Oriented Machine-Type Wireless Video Transmission

The Proof is in the Pudding: Decision-Oriented Machine-Type ...

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IEEE Conference on Global communications (GLOBECOM)

作者： Jie Song Yun Gao Dan Wu Liang Zhou Yi Qian Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing China Institute of Communications Engineering Army Engineering University of PLA Nanjing China Department of Electrical and Computer Engineering University of Nebraska-Lincoln Lincoln USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

With the rise of the Internet of Everything, machine-type wireless video applications like intelligent surveillance are increasingly becoming mainstream network services. However, the massive transmission of video streams heavily burdens wireless networks. Existing methods typically establish a correlation between the transmitted content and the quality of experience or service, making it challenging to balance trade-offs among decision quality, bandwidth utilization, and latency requirements. To address this issue fundamentally, this paper proposes a novel perspective by designing wireless video transmission strategies from the angle of decision quality, where only the content that impacts the decision outcomes is transmitted. The highlight of this paper is designing a lightweight yet efficient binary classifier that predicts whether the current content will change decision outcomes based on content discrepancies, measured only through pixel-level differences and macroblock-level similarity. Furthermore, these discrepancies can be used to distinguish the background, thereby further saving bandwidth by reusing the background information. Additionally, a fine-tuning module is incorporated to flexibly update the classifier model, ensuring adaptability across various scenarios. Extensive results demonstrate that the proposed strategy achieves over 81% redundant bandwidth savings while maintaining decision quality and catering to strict latency requirements on computation-constrained devices.

关键词： Correlation Wireless networks Surveillance Current measurement Bandwidth Streaming media Quality of experience Internet of Things Global communication Streams

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Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA communications

arXiv

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arXiv 2021年

作者： Shi, Zhaoyuan Yang, Helin Xie, Xianzhong Lu, Huabing Cai, Jun Ding, Zhiguo School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada Department of Electrical Engineering Princeton University PrincetonNJ08544 United States School of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom

The combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency and spectral efficiency of the upcoming beyond fifth generation network (B5G), especially for support the wireless sensor communications in Internet of things (IoT) system. However, how to realize intelligent frequency, time, and energy resource allocation to support better performances is an important problem to be solved. In this paper, we study joint spectrum, energy, and time resource management for the EH-CR-NOMA IoT systems. Our goal is to minimize the number of data packets losses for all secondary sensing users (SSU), while satisfying the constraints on the maximum charging battery capacity, maximum transmitting power, maximum buffer capacity, and minimum data rate of primary users (PU) and SSUs. Due to the nonconvexity of this optimization problem and the stochastic nature of the wireless environment, we propose a distributed multidimensional resource management algorithm based on deep reinforcement learning (DRL). Considering the continuity of the resources to be managed, the deep deterministic policy gradient (DDPG) algorithm is adopted, based on which each agent (SSU) can manage its own multidimensional resources without collaboration. In addition, a simplified but practical action adjuster (AA) is introduced for improving the training efficiency and battery performance protection. The provided results show that the convergence speed of the proposed algorithm is about 4 times faster than that of DDPG, and the average number of packet losses (ANPL) is about 8 times lower than that of the greedy algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Resource allocation

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Deep Feature Surgery: Towards Accurate and Efficient Multi-Exit networks

arXiv

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arXiv 2024年

作者： Gong, Cheng Chen, Yao Luo, Qiuyang Lu, Ye Li, Tao Zhang, Yuzhi Sun, Yufei Zhang, Le College of Software Nankai University China College of Computer Science Nankai University China National University of Singapore Singapore School of Information and Communication Engineering University of Electronic Science and Technology of China China HAIHE Lab of ITAI China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education

Multi-exit network is a promising architecture for efficient model inference by sharing backbone networks and weights among multiple exits. However, the gradient conflict of the shared weights results in sub-optimal accuracy. This paper introduces Deep Feature Surgery (DFS), which consists of feature partitioning and feature referencing approaches to resolve gradient conflict issues during the training of multi-exit networks. The feature partitioning separates shared features along the depth axis among all exits to alleviate gradient conflict while simultaneously promoting joint optimization for each exit. Subsequently, feature referencing enhances multi-scale features for distinct exits across varying depths to improve the model accuracy. Furthermore, DFS reduces the training operations with the reduced complexity of backpropagation. Experimental results on Cifar100 and ImageNet datasets exhibit that DFS provides up to a 50.00% reduction in training time and attains up to a 6.94% enhancement in accuracy when contrasted with baseline methods across diverse models and tasks. Budgeted batch classification evaluation on MSDNet demonstrates that DFS uses about 2× fewer average FLOPs per image to achieve the same classification accuracy as baseline methods on Cifar100. The code is available at https: //***/GongCheng1919/dfs. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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Defense technology of Wormhole Attacks Based on Node Connectivity

Defense Technology of Wormhole Attacks Based on Node Connect...

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IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

作者： Jiu-hu Zheng Huan-yan Qian Lei Wang School of Information Technology Jiangsu Institute of Commerce Nanjing Jiangsu China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing Jiangsu China National Mobile Communications esearch Laboratory Southeast University Nanjing Jiangsu China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing Jiangsu China

ISBN: (纸本)9781509018949

After wormhole attacks the routing mechanism of wireless sensor network (WSN) is damaged, then the hop count and distance between nodes cannot be well matched, so that the WSN becomes the anisotropic sensor network, resulting in security treats for WSN. Considering that wormhole attacks can cause the changing of node connectivity, through the theoretical analysis and derivation, the paper has proposed a method which detects wormhole attacks with the connected relation of high connectivity nodes, and optimizes routing with the help of the normal nodes outside the wormhole. Simulation results show that the algorithm can well detect wormhole attacks and has better localization accuracy In addition, the effect is more obvious in the case of the wormhole nodes with large communication radius and long distance.

关键词： Wireless sensor networks Routing Probability Algorithm design and analysis Security Simulation computer science

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Gossip-Based Indexing Ring Topology for 2-Dimension Spatial Data in Overlay networks

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Procedia Engineering 2012年 29卷 3727-3731页

作者： Z.Q. Zou Y. Wang B. Hu R.C. Wang College of Computer Nanjing University of Posts and TelecommunicationsNanjing Jiangsu 210003 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing Jiangsu 210003 China Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications) Ministry of Education Jiangsu Province Nanjing Jiangsu 210003 China Key Laboratory of Virtual Geographic Environment Ministry of Education Nanjing Normal University Nanjing 210046 China

Overlay networks are used widely in the Internet, such as retrieval and share of files, multimedia games and so on. However, in distributed system, the retrieval and share of 2-dimension spatial data still have some difficult problems and can not solve the complex retrieval of 2-dimension spatial data efficiently. This article presents a new indexing overlay networks, named 2D-Ring, which is the ring topology based on gossip for 2-dimension spatial data. The peers in our overlay networks exchange the information periodically and update each local view by constructing algorithm. 2-dimension spatial data is divided by quad-tree and mapped into control points, which are hashed into 2D-Ring by SHA-1 hash function. In such way, the problem of 2-dimension spatial data indexing is converted to the problem of searching peers in the 2D-Ring. A large of extensive experiments show that the time complexity of constructing algorithm of 2D-Ring can reach convergence logarithmically as a function of the network size and hold higher hit rate and lower query delay.

关键词： Overlay networks 2-Dimension Spatial Data Indexing Gossip

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Age of Information Minimization in Intelligent Reflecting Surface-Aided Covert communications

Age of Information Minimization in Intelligent Reflecting Su...

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IEEE Globecom Workshops

作者： Chao Wang Zan Li Yue Zhao Derrick Wing Kwan Ng Naofal Al-Dhahir State Key Laboratory of Integrated Service Network Xidian University Xi’an China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia Department of Electrical and Computer Engineering the University of Texas at Dallas Richardson TX USA

ISBN: (纸本)9781665459761

In this work, we investigate the covert information freshness in intelligent reflecting surface (IRS)-aided multi antenna communications, where a public full-duplex user (Alice) and a private full-duplex user (Bob) exchange information in the presence of a watchful warden (Willie). Specifically, the covert transmission from Bob to Alice is shielded from Willie under the mask of Alice’s undisguised signal transmission. To minimize the covert information age, we exploit the channel statistics to jointly design the active beamforming at Alice and Bob, the passive beamforming at the IRS, and the blocklength of the confidential data packet under the constraints of the maximum tolerable age of information (AoI) at Bob and communication covertness. Although the considered joint optimization problem is nonconvex, we employ the successive convex approximation to design an iterative algorithm to obtain an efficient suboptimal solution. Numerical results demonstrate the superiority of our proposed joint optimization algorithm and further show that the enhanced covert channel quality can reduce the required blocklength to improve information freshness.

关键词： Array signal processing Error probability Simulation Full-duplex system Information age Approximation algorithms Reflector antennas

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