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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： XiQiang Hou YiFan Hu Fuqiang Wang School of information and control engineering Qingdao University of Technology Qingdao China Shandong Computer Science Center and Shandong Provincial Key Laboratory of Computer Networks Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Computer Science Center and Shandong Provincial Key Laboratory of Computer Networks Qilu University of Technology (Shandong Academy of Sciences) China

This review article introduces the concepts, server architecture and application scenarios of Mobile Edge Computing (MEC) and Wireless Sensor Network (WSN). By differentiating between rechargeable and non-rechargeable systems, it presents the research status of task offloading in WSN in an edge computing environment. Various strategies are analyzed and compared in terms of their characteristics and application domains. The existing methods can reduce offloading latency and energy consumption, but still face some issues. Based on this, the research hotspots of edge computing task offloading for WSN are summarized, and its research and development trends are pointed out.

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Autonomous Navigation of Agricultural Robots Utilizing Path Points and Deep Reinforcement Learning

Autonomous Navigation of Agricultural Robots Utilizing Path ...

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International Joint Conference on Neural networks (IJCNN)

作者： Jipeng Kang Zhibin Zhang Yajie Liang Xu Chen Key Laboratory of Wireless Networks and Mobile Computing School of Computer Science Inner Mongolia University Hohhot China Engineering Research Center of Ecological Big Data School of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

When using deep reinforcement learning algorithms for reactive navigation, it is common to encounter situations where the agent gets stuck in a local optimum. In autonomous exploration without any prior knowledge, the exploration of environments is often suboptimal. Moreover, the agricultural robots performing tasks in farm fields face even more great challenges. To address this, we propose an Agricultural Robot Autonomous Exploration System (ARAES) that uses deep reinforcement learning algorithms to drive the robot to explore unknown farm environments. The whole system consists of a Global Path Planning (GPP) module and a Local Path Planning (LPP) module. The GPP module utilizes path points acquired along the heading direction in the environment and selects waypoints based on the collected path points to guide the robot towards the global goal, effectively alleviating local optima issues in reactive navigation. The LPP module learns local navigation motion policies in a simulated environment. It incorporates the learned policies into the motion planning stack, enabling the robot to move towards global goals based on the path points and way points. The advantages of the ARAES system are its autonomy and real-time performance, allowing the robot to operate without needing to know detailed prior information about the environment and enabling real-time adjustments of navigation policies according to current environments. Meanwhile, ARAES can construct maps in real-time as the robot moves in the environment. Experiments demonstrate that ARAES is advantageous because it does not rely on complex static and dynamic environment maps or prior information and delivers superior performance compared to other methods.

关键词： Agricultural robots Simultaneous localization and mapping Navigation Production Deep reinforcement learning Real-time systems Path planning

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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Multidimensional Voronoi Constellations vs. Short Blocklength Probabilistic Shaping: A Comparison for Multilevel Coding Approach

arXiv

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arXiv 2024年

作者： Sheng, Yajie Chen, Bin Lei, Yi Deng, Jingxin Xu, Jiwei Fu, Mengfan Zhuge, Qunbi Li, Shen Department of Computer Science and Information Engineering Hefei University of Technology Hefei China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Department of Electrical and Computer Engineering Université Laval Québec Canada

Performance of concatenated multilevel coding with probabilistic shaping (PS) and Voronoi constellations (VCs) is analysed over AWGN channel. Numerical results show that VCs provide up to 1.3 dB SNR gains over PS-QAM ... 详细信息

关键词： Probability distributions

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Incremental Semi-Supervised Learning for Data Streams Classification in Internet of Things

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2489-2501页

作者： Jiang, Jun Wang, Bin Tang, Quan Zhong, Guoxiang Tang, Xuhao Rodrigues, Joel J. P. C. Pengcheng Laboratory Department of New Networks Shenzhen China South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Lusófona University COPELABS Campo Grande 376 Lisbon1749-024 Portugal

Data stream classification is widely used in Internet of Things (IoT) scenarios such as health monitoring, anomaly detection and online diagnosis. Due to the continuous data stream changing dynamically over time, it is impossible to classify all the data simultaneously. Moreover, labeling each sample in practical data stream applications is time-and resource-consuming. The realistic situation is that only a few instances in a data stream are labeled. Therefore, classifying data streams with limited labels has become challenging in IoT scenarios. In this paper, we propose an incremental dynamic weighted semi-supervised method for classifying IoT data streams. Considering the dynamics and continuity in data streams, we use a chunk-based approach to learn the features in the data stream and assign weights to the classifier dynamically. Moreover, we deploy incremental learning methods to continuously learn from the sampled labeled data stream to update the classifier model, which can take advantage of newly incoming labeled data to improve learning performance. Experimental evaluations on seven IoT datasets show that the proposed method outperforms semi-supervised methods in accuracy, precision, and geometric mean (Gmean) by 10% and 5% over supervised methods, respectively. © 2004-2012 IEEE.

关键词： Self-supervised learning

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Enhancing Low-Latency and High-Reliability Data Forwarding for In-Vehicle Time-Sensitive networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Sun, Haitong Zhang, Haijun Guan, Wanqing Liu, Xiangnan Ma, Hui Leung, Victor C.M. Hebei Key Laboratory of Space-Air-Ground Intelligent Communication China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden Department of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China

To satisfy the low delay, low jitter, and high success rate requirements for in-vehicle networks, IEEE 802.1 Task Group proposed Time-Sensitive Networking (TSN), which has aroused increasing attention in managing time-critical services in networks. This paper resolves the following issue: how to minimize the worst-case response time (WCRT) of frame forwarding in TSN under no-wait scenarios to guarantee low-latency transmission, stability, and high success rate for in-vehicle TSN networks? Currently, some work resolves this problem by leveraging integer linear programming, and others apply heuristic methods to avoid the time complexity problem, but the success rate is affected since they are prone to get trapped in local optima. To reach a balance between time-complexity and success rate, a scheme named Frame Dynamic Priority Scheduling Mechanism (FDPSM) is proposed, where the frame dynamic priority levels are set up and frames are scheduled accordingly. The target is to maximize the success rate and minimize the WCRT for in-vehicle networks. We evaluate the proposed mechanism from aspects such as traffic forwarding time, size of windows, number of hops, and the network load, together with success rate and the simulation results demonstrate the advantages of the FDPSM for in-vehicle TSN networks. © 2025 IEEE. All rights reserved,

关键词： Integer programming

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Imperceptible Face Forgery Attack via Adversarial Semantic Mask

arXiv

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arXiv 2024年

作者： Liu, Decheng Su, Qixuan Peng, Chunlei Wang, Nannan Gao, Xinbo State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xidian University Shaanxi Xi’an710071 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Xidian University Shaanxi Xi’an710071 China Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China

With the great development of generative model techniques, face forgery detection draws more and more attention in the related field. Researchers find that existing face forgery models are still vulnerable to adversarial examples with generated pixel perturbations in the global image. These generated adversarial samples still can't achieve satisfactory performance because of the high detectability. To address these problems, we propose an Adversarial Semantic Mask Attack framework (ASMA) which can generate adversarial examples with good transferability and invisibility. Specifically, we propose a novel adversarial semantic mask generative model, which can constrain generated perturbations in local semantic regions for good stealthiness. The designed adaptive semantic mask selection strategy can effectively leverage the class activation values of different semantic regions, and further ensure better attack transferability and stealthiness. Extensive experiments on the public face forgery dataset prove the proposed method achieves superior performance compared with several representative adversarial attack methods. The code is publicly available at https://***/clawerO-O/ASMA. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Comparative E-Voting Security Evaluation: Multi-Modal Authentication Approaches

Comparative E-Voting Security Evaluation: Multi-Modal Authen...

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Blockchain Computing and Applications (BCCA), International Conference on

作者： Elsi Ahmadieh Nour El Madhoun Department Communications and Computer Networks Engineering Faculty of Technology Lebanese University Beirut Lebanon ISEP LISITE Laboratory France CNRS LIP6 Sorbonne Université Paris France

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

The decentralized, distributed ledger, a fundamental aspect of blockchain technology, plays a pivotal role in various sectors, including electronic voting (e-voting) systems. When considering e-voting, the significance of blockchain technology in boosting the security, transparency, and integrity of the entire process is evident. This ranges from the initial steps of user registration and authentication to the final stages of voting and result validation. Unlike conventional voting systems, which are plagued by issues such as fraud, tampering, and a lack of transparency in the result tallying and viewing processes, not to mention the delays, blockchain-enabled e-voting mitigates these challenges within government frameworks. It ensures a secure, tamper-proof, and transparent procedure, where each vote is recorded as an immutable transaction, thereby enhancing the process’s reliability. This paper explores the integration of voice recognition and fingerprinting using blockchain technology for enhancing the security of e-voting systems, comparing alternative authentication methods, and emphasizing the need for a multimodal approach to achieve better security and reliability of the voting systems.

关键词： Multi-factor authentication Government Speech recognition Fingerprint recognition Blockchains Registers Fraud Security Reliability Electronic voting

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Constructing Quadratic and Cubic Negabent Functions over Finite Fields

Constructing Quadratic and Cubic Negabent Functions over Fin...

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IEEE International Symposium on Information Theory

作者： Fei Guo Zilong Wang Guang Gong State Key Laboratory of Integrated Service Networks Xidian University Xi’an China Department of Electrical and Computer Engineering University of Waterloo Waterloo Ontario Canada

Bent functions have flat absolute Walsh-Hadamard spectra and negabent functions have flat absolute nega-Hadamard spectra. Those properties are wide applications in cryptography for constructing cryptographically strong functions and error correcting codes for better performance. In this paper, we present a new construction of quadratic and cubic negabent functions over finite fields. Those functions can be represented as the sum of the three components: one is the trace function of the monomial term λx 3 or it multiplying by the trace function of x; the second, the sum of all the quadratic monomial functions except for one; and the third, the product of two linear functions where the parameter λ and variable x belong to an arbitrary binary finite field of 2 n elements for n odd.

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Sentiment Analysis to manage Health Crisis in Smart Cities 18

Sentiment Analysis to manage Health Crisis in Smart Cities

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18th IEEE International Conference on computer Science and Information Technologies, CSIT 2023

作者： Bouhissi, Houda El Itmacene, Ouardia Oubekkou, Milissa Pavlova, Olga Hovorushchenko, Tetiana Shestakevych, Tetiana University of Bejaia LMED Laboratory Faculty of Exact Sciences Bejaia06000 Algeria University of Bejaia Faculty of Exact Sciences Bejaia06000 Algeria Khmelnytskyi National University Department of Computer Engineering & Information Systems Khmelnytskyi Ukraine Lviv Polytechnic National University Information Systems and Networks Department Lviv Ukraine

ISBN: (纸本)9798350360462

Sentiment analysis is a branch of natural language processing that analyzes textual data and provides insights into users' opinions. In the context of smart cities, sentiment analysis seeks to seamlessly integrate technological innovations into people's daily lives, fostering sustainability and enhancing overall quality of life while monitoring citizen satisfaction levels. This approach allows for more precise analysis and facilitates the implementation of strategies geared towards improving services and the general quality of life. The aim of this paper is to propose an approach for managing healthcare crises in smart cities. The proposal relies on a hybrid approach that combines LSTM for tweet classification with EHO for optimizing classification parameters. Experimental results have demonstrated the effectiveness of this proposed technique. © 2023 IEEE.

关键词： Smart city

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