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Decomposition-based learning in drone-assisted wireless-powered mobile edge computing networks

Digital Communications and networks 2024年第6期10卷 1769-1781页

作者： Xiaoyi Zhou Liang Huang Tong Ye Weiqiang Sun State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong UniversityShanghai 200240China College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310058China

This paper investigates the multi-Unmanned Aerial Vehicle(UAV)-assisted wireless-powered Mobile Edge Computing(MEC)system,where UAVs provide computation and powering services to mobile *** aim to maximize the number of completed computation tasks by jointly optimizing the offloading decisions of all terminals and the trajectory planning of all *** action space of the system is extremely large and grows exponentially with the number of *** this case,single-agent learning will require an overlarge neural network,resulting in insufficient ***,the offloading decisions and trajectory planning are two subproblems performed by different executants,providing an opportunity for *** thus adopt the idea of decomposition and propose a 2-Tiered Multi-agent Soft Actor-Critic(2T-MSAC)algorithm,decomposing a single neural network into multiple small-scale *** the first tier,a single agent is used for offloading decisions,and an online pretrained model based on imitation learning is specially designed to accelerate the training process of this *** the second tier,UAVs utilize multiple agents to plan their *** agent exerts its influence on the parameter update of other agents through actions and rewards,thereby achieving joint *** results demonstrate that the proposed algorithm can be applied to scenarios with various location distributions of terminals,outperforming existing benchmarks that perform well only in specific *** particular,2T-MSAC increases the number of completed tasks by 45.5%in the scenario with uneven terminal ***,the pretrained model based on imitation learning reduces the convergence time of 2T-MSAC by 58.2%.

关键词： Mobile-edge computing Multi-agent reinforcement learning Offloading decision Trajectory planning Unmanned aerial vehicle Wireless power transfer

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Hotspotter: An Incentivized Crowdsensing System for WiFi and Cellular Network Coverage Visualization 29

Hotspotter: An Incentivized Crowdsensing System for WiFi and...

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29th IEEE Asia Pacific Conference on Communications, APCC 2024

作者： Ortiz, Sebastian Luis S. Enriquez, Justin Gabriel R. Tan, Wilson M. Festin, Cedric Angelo M. University of the Philippines - Diliman Networks and Distributed Systems Laboratory Department of Computer Science Metro Manila Philippines

ISBN: (纸本)9798331517816

Reliable internet access is a key enabler for economic growth. Although the Philippine government launched initiatives to improve connectivity, connection speeds remained below the global average, especially for mobile networks. This paper presents Hotspotter, a system that aims to aid implementers and stakeholders in quantifying and addressing network coverage issues. Hotspotter is an incentivized crowdsensing system that collects, maps, and visualizes WiFi and cellular data to pinpoint hotspots and dead zones for the effective deployment and relocation of WiFi access points. A mobile application for Android was developed to facilitate data collection on geolocation, nearby WiFi access points, and connected cellular networks. The user interface visualizes the aggregated data in a hexagon-grid map. Fieldwork was conducted in two sitios of Barangay San Lorenzo, Norzagaray, Bulacan to stress-test the Hotspotter system in a Geographically Isolated and Disadvantaged Area (GIDA). It was found that 2G and 4G had the widest coverage and strongest signals overall, with modal signal strengths of 4.0 and 3.0, respectively. Being at the cutting edge, 5G was not yet supported. In the end, the mobile application's passive sensing, collecting, and caching of data successfully operated even in the most isolated areas without an internet connection. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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6G-SECUREIDS: Blockchain-Enhanced Secure Knowledge Transfer for Distributed Intrusion Detection systems in Advanced networks 6

6G-SECUREIDS: Blockchain-Enhanced Secure Knowledge Transfer ...

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6th International Conference on Networking and Advanced systems, ICNAS 2023

作者： Sakraoui, Sabrina Derdour, Makhlouf Ahmim, Ahmed Badji Mokhtar Annaba University Networks and Systems Laboratory Department of Computer Science Annaba Algeria Oum El Bouaghi University Networks and Systems Laboratory Department of Computer Science Oum El Bouaghi Algeria Mohamed-Cherif Messaadia University Networks and Systems Laboratory Dpt. of Mathematics and Computer Science Souk Ahras Algeria

ISBN: (纸本)9798350319170

As the development of wireless networks advances towards the deployment of 6G technology, ensuring robust security measures becomes crucial. In this paper, we propose 6G-SECUREIDS, a novel intrusion detection system designed specifically for 6G wireless networks. The system leverages machine learning techniques and blockchain technology to detect and mitigate security threats, safeguard data privacy, and ensure efficient and optimized training. Our experimental results demonstrate the effectiveness and reliability of the system, with the clients achieving high accuracy scores across different model architectures. The promising results highlight the potential of 6G-SECUREIDS as a valuable security solution for future 6G networks, contributing to the overall protection and integrity of the wireless communication ecosystem. © 2023 IEEE.

关键词： Wireless networks

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Developing Mask R-CNN Framework for Real-Time Object Detection 6

Developing Mask R-CNN Framework for Real-Time Object Detecti...

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6th International Conference on Advanced Communication Technologies and Networking, CommNet 2023

作者： Oussama, Hmidani Mehdi, Ismaili Alaoui El Moulay Ismail University Computer Networks and Systems Laboratory Faculty of Science Meknes Morocco

ISBN: (纸本)9798350329391

In the field of computer vision, achieving real-time object detection through deep learning holds significant importance. Notable strides have been made in real-time object detection methods, particularly due to the rapid progress of deep convolutional neural networks (CNNs) compared to traditional approaches. It has been observed that existing real-time deep CNN-based object detectors face performance limitations, primarily stemming from the architecture of the underlying base network. This study introduces an improved framework for real-time object detection based on the Mask R-CNN model. To address the challenge of enhancing performance under stricter localization criteria, we replace the original Mask R-CNN's Region of Interest Align (RoIAlign) with spatial interpolation. Additionally, in the final phase of the Mask R-CNN framework, we utilize the depthwise separable convolution architecture from EfficientNet-B7 to construct a classifier for proposal categorization and to adjust bounding boxes for detected objects. Experimental findings on both the COCO dataset and the ImageNet dataset demonstrate that our proposed approach surpasses the original Mask R-CNN in terms of detection accuracy and inference speed. Categorically, our method outperforms the original Mask R-CNN framework by 51.5% on the COCO test set and 46.2% on the ImageNet test set. © 2023 IEEE.

关键词： Object detection

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EloMetrics: Advanced Outcome Prediction for Chess Matches with Elo Ratings and Logistic Regression

EloMetrics: Advanced Outcome Prediction for Chess Matches wi...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Reyes, Ma Julianna Re-An D.G. Dicreto, Eirnan Santos, Emmanuel Gabriel D. Limbag, Daniella Franxene P. Sampedro, Gabriel Avelino College of Computer Studies De La Salle University Manila Philippines Networks and Distributed Systems Laboratory University of the Philippines Diliman Quezon City Philippines

ISBN: (纸本)9798331510756

Chessis a complex game characterized by diverse strategies and time constraints, making quick decision-making essential for success. While Elo ratings are widely recognized as indicators of player skill, the predictability of match outcomes based solely on these ratings remains a challenge. The study aims to develop a model for accurately predicting the outcome of chess games using logistic regression, focusing on Elo rating differences and the number of moves in each game. The dataset includes over 20,000 games from ***, and only games with decisive outcomes (excluding draws) were used. The research categorizes Elo ratings into various classes and evaluates model performance across these ranges. The model achieves a predictive accuracy of 68.18%, demonstrating the significance of Elo ratings and move counts in determining game results. Performance metrics, including precision, recall, and Fl-score, further validate the model's effectiveness. The study concludes that while Elo ratings and move count are strong predictors of chess outcomes, further refinement is needed to improve performance at high-ranking skill levels. The model performs well for experts but loses accuracy in master-level games, reflecting higher skill-level complexities. The insights gained from this research contribute to a deeper understanding of predictive modeling in chess, sug-gesting potential avenues for further investigation into additional Influencing factors. © 2025 IEEE.

关键词： Logistic regression

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Int-BEB: Enhanced Binary Exponential Backoff using Q-learning 5

Int-BEB: Enhanced Binary Exponential Backoff using Q-learnin...

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5th International Conference on Pattern Analysis and Intelligent systems, PAIS 2023

作者： Zerguine, N. Ferhat Abbas Setif 1 University Networks and Distributed Systems Laboratory Department of Computer Science Setif Algeria

ISBN: (纸本)9798350381450

Because of their development and success, MANETs are now preferred alternatives in many fields of study and application. The IEEE 802.11 standard is the basis of most wireless technologies. To treat collisions in wireless networks, IEEE 802.11 Distributed Coordination Function (DCF) MAC layer employs Binary Exponential Backoff (BEB) algorithm, that is helpful in decreasing the potential of collision, but comes at the expense of several network performance metrics, such as medium access delay, bandwidth management, power use, and fairness. A Deep Learning technique called Deep Reinforcement Learning (DRL) allows an agent to communicate with the environment in order to accomplish a goal. In the present paper, we suggest Q-learning (QL), one of the DRL approaches to improve the Binary Exponential Backoff (BEB) in MANETs. The intelligently designed Int-BEB aims for better CW selection;this selection strikes to reduce collisions as much as possible and to give more chances to nodes that make collisions to access the channel by taking into consideration their residual energy. In-depth simulations are performed to evaluate the performance of the proposed mechanism. The performance of Int-BEB is evaluated in diverse networks with several criteria such as packet successful transmission, packet loss and energy. © 2023 IEEE.

关键词： Reinforcement learning

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Near-Far Field Beamforming for Holographic Multiple-Input Multiple-Output

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Journal of Communications and Information networks 2023年第2期8卷 99-110页

作者： Yutong Zhang Boya Di Hongliang Zhang Lingyang Song State Key Laboratory of Advanced Optical Communication Systems and Networks School of ElectronicsPeking UniversityBeijing 100871China School of Electronic and Computer Engineering Peking University Shenzhen Graduate SchoolShenzhen 518055China

Recently,holographic multiple-input multiple-output(HMIMO)has motivated its potential use to support high-capacity data transmission with spatially quasi-continuous *** a practical instance of HMIMO,reconfigurable refractive surfaces(RRSs)equipped with numerous metamaterial elements are utilized as antennas by refracting incident signals from signal *** this paper,we investigate a multi-user communication system with an RRS deployed as the base station(BS)’s transmit *** mitigate the high overhead of accurate channel state information(CSI)acquisition,the codebook design and beam training are employed to perform *** the large scale of RRS,users are likely to be randomly distributed in both the near and far fields around the BS,which is unknown in *** considering radiation characteristics in both fields,a near-far field codebook is designed to be applicable to all users,regardless of their *** reduce overhead,a multi-user beam training is proposed to serve all users simultaneously by enhancing each codeword capable of covering multiple *** a general case that includes users in both fields,simulation results indicate that,without prior knowledge of user distribution,the proposed scheme outperforms state-of-the-art ones in terms of sum rate and overhead.

关键词： near-far field codebook design RRSs multi-user beam training

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Intelligent indoor metasurface robotics

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National Science Review 2023年第8期10卷 20-32页

作者： Hanting Zhao Shengguo Hu Hongrui Zhang Zhuo Wang Hao Dong Philipp del Hougne Tie Jun Cui Lianlin Li State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Center on Frontiers of Computing Studies School of Computer Science Peking University The French National Centre for Scientific Research(CNRS) University of Rennes State Key Laboratory of Millimeter Waves Southeast University

Intelligent indoor robotics is expected to rapidly gain importance in crucial areas of our modern society such as at-home health care and factories. Yet, existing mobile robots are limited in their ability to perceive and respond to dynamically evolving complex indoor environments because of their inherently limited sensing and computing resources that are, moreover, traded off against their cruise time and payload. To address these formidable challenges, here we propose intelligent indoor metasurface robotics(I2MR),where all sensing and computing are relegated to a centralized robotic brain endowed with microwave perception; and I2MR's limbs(motorized vehicles, airborne drones, etc.) merely execute the wirelessly received instructions from the brain. The key aspect of our concept is the centralized use of a computation-enabled programmable metasurface that can flexibly mold microwave propagation in the indoor wireless environment, including a sensing and localization modality based on configurational diversity and a communication modality to establish a preferential high-capacity wireless link between the I2MR's brain and limbs. The metasurface-enhanced microwave perception is capable of realizing low-latency and high-resolution three-dimensional imaging of humans, even around corners and behind thick concrete walls, which is the basis for action decisions of the I2MR's brain. I2MR is thus endowed with real-time and full-context awareness of its operating indoor environment. We implement, experimentally, a proof-of-principle demonstration at ～2.4 GHz, in which I2MR provides health-care assistance to a human inhabitant. The presented strategy opens a new avenue for the conception of smart and wirelessly networked indoor robotics.

关键词： intelligent metasurfaces intelligent robotics edge devices IoT 6G

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Optimizing Polynomial-Time Solutions to a Network Weighted Vertex Cover Game

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 512-523页

作者： Jie Chen Kaiyi Luo Changbing Tang Zhao Zhang Xiang Li the Adaptive Networks and Control Laboratory Department of Electronic EngineeringFudan UniversityShanghai 200433China the College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua 321004 Yuxi Middle School Taizhou 317399China IEEE the College of Physics and Electronics Information Engineering Zhejiang Normal UniversityJinhua 321004China the College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua 321004China the Adaptive Networks and Control Laboratory Department of Electronic EngineeringFudan UniversityShanghai 200433 the Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous SystemsTongji UniversityShanghai 201210China

Weighted vertex cover(WVC)is one of the most important combinatorial optimization *** this paper,we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted *** first model the WVC problem as a general game on weighted *** the framework of a game,we newly define several cover states to describe the WVC ***,we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums(SNEs)of the ***,we propose a game-based asynchronous algorithm(GAA),which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial ***,we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms,termed the improved game-based asynchronous algorithm(IGAA),in which we prove that it can obtain a better solution to the WVC problem than using a the ***,numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.

关键词： Game-based asynchronous algorithm(GAA) game optimization polynomial time strict Nash equilibrium(SNE) weighted vertex cover(WVC)

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Electromagnetic metamaterial agent

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Light(Science & Applications) 2025年第1期14卷 118-130页

作者： Shengguo Hu Mingyi Li Jiawen Xu Hongrui Zhang Shanghang Zhang Tie Jun Cui Philipp del Hougne Lianlin Li State Key Laboratory of Advanced Optical Communication Systems and Networks School of ElectronicsPeking UniversityBeijing100871China National Key Laboratory for Multimedia Information Processing School of Computer SciencePeking UniversityBeijing100871China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China Pazhou Laboratory(Huangpu) GuangzhouGuangdong510555China Univ Rennes CNRSIETR-UMR 6164F-35000RennesFrance

Metamaterials have revolutionized wave control;in the last two decades,they evolved from passive devices via programmable devices to sensor-endowed self-adaptive devices realizing a user-specified *** deep-learning techniques play an increasingly important role in metamaterial inverse design,measurement post-processing and end-to-end optimization,their role is ultimately still limited to approximating specific mathematical relations;the metamaterial is still limited to serving as proxy of a human operator,realizing a predefined ***,we propose and experimentally prototype a paradigm shift toward a metamaterial agent(coined metaAgent)endowed with reasoning and cognitive capabilities enabling the autonomous planning and successful execution of diverse long-horizon tasks,including electromagnetic(EM)field manipulations and interactions with robots and *** recently released foundation models,metaAgent reasons in high-level natural language,acting upon diverse prompts from an evolving complex ***,metaAgent's cerebrum performs high-level task planning in natural language via a multi-agent discussion mechanism,where agents are domain experts in sensing,planning,grounding,and *** response to live environmental feedback within a real-world setting emulating an ambient-assisted living context(including human requests in natural language),our metaAgent prototype self-organizes a hierarchy of EM manipulation tasks in conjunction with commanding a *** masters foundational EM manipulation skills related to wireless communications and sensing,and it memorizes and learns from past experience based on human feedback.

关键词： agent prompt reasoning

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