IEEE Internet of Things Journal最新文献

High-Sample-Efficient Multiagent Reinforcement Learning for Navigation and Collision Avoidance of UAV Swarms in Multitask Environments 多任务环境中无人机群导航和避撞的高采样效率多代理强化学习

IF 8.2 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2024-11-07 DOI: 10.1109/JIOT.2024.3409169

Jiaming Cheng;Ni Li;Ban Wang;Shuhui Bu;Ming Zhou

{"title":"High-Sample-Efficient Multiagent Reinforcement Learning for Navigation and Collision Avoidance of UAV Swarms in Multitask Environments","authors":"Jiaming Cheng;Ni Li;Ban Wang;Shuhui Bu;Ming Zhou","doi":"10.1109/JIOT.2024.3409169","DOIUrl":"https://doi.org/10.1109/JIOT.2024.3409169","url":null,"abstract":"Multiagent reinforcement learning (MARL) algorithms have shown promise in the Internet of Things devices, such as unmanned aerial vehicle (UAV) swarms. However, the dynamic nature of large-scale swarm systems, with constantly changing numbers of agents and observed neighbors, poses challenges for MARL adaptation. Existing approaches struggle to extract meaningful features and require a substantial number of experience samples, resulting in low-sample efficiency and high-risk ratios. Moreover, these methods are effective in task-specific scenarios and fail to perform well in multitask settings. To overcome these challenges, this study proposes a high-sample efficient and scalable MARL approach for UAV swarms. The proposed approach incorporates a hypernetwork-based embedding attention (HEA) mechanism for the state representation of the policy network and a multiencoder gated transformer with a multilayer attention (MEGTrMA) mechanism for the value function. The HEA automatically generates weights for each agent to adapt to dynamic scenarios, enhancing representation ability and adaptability while reducing the cost of trial and error for improved learning efficiency. The MEGTrMA captures the contribution of each agent to the global observation, establishing long-term dependencies among them and facilitating stable policy learning in multitask scenarios. Simulation results demonstrate that the proposed method is scalable, generalizable, and high-sample efficient. Compared to learning from scratch, our method significantly reduces training time to less than one-fifth of the initial time by progressively increasing the number of UAVs and their corresponding neighbors. Additionally, the average number of collisions is reduced by an order of magnitude for large-scale UAV swarms.","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":null,"pages":null},"PeriodicalIF":8.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Internet of Things Journal Information for Authors IEEE 物联网期刊作者信息

IF 8.2 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2024-11-07 DOI: 10.1109/JIOT.2024.3480517

引用次数: 0

IEEE Internet of Things Journal Society Information IEEE 物联网期刊学会信息

IF 8.2 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2024-11-07 DOI: 10.1109/JIOT.2024.3480515

引用次数: 0

IEEE Internet of Things Journal Publication Information IEEE 物联网期刊出版信息

IF 8.2 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2024-11-07 DOI: 10.1109/JIOT.2024.3480511

引用次数: 0

See as You Desire: Scale-adaptive Face Super-Resolution for Varying Low Resolutions 随心所欲：针对不同低分辨率的规模自适应人脸超级分辨率

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2024-11-06 DOI: 10.1109/jiot.2024.3492716

Ling Li, Yan Zhang, Lin Yuan, Shuang Li, Xinbo Gao

引用次数: 0

Accelerating Multi-UAV Collaborative Sensing Data Collection: A Hybrid TDMA-NOMA-Cooperative Transmission in Cell-Free MIMO Networks 加速多无人机协作传感数据采集：无小区 MIMO 网络中的混合 TDMA-NOMA 协同传输

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2024-11-06 DOI: 10.1109/jiot.2024.3492709

Eunhyuk Park, Junbeom Kim, Seok-Hwan Park, Osvaldo Simeone, Shlomo Shamai

引用次数: 0

Payload-Adaptive Hybrid MAC Protocol for Sustainable Internet of Things Networks: Protocol Design and Adaptive Adjustment Mechanisms 可持续物联网网络的有效载荷自适应混合 MAC 协议：协议设计与自适应调整机制

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2024-11-06 DOI: 10.1109/jiot.2024.3491358

Xinyu Fan, Jie Hu, Kun Yang

引用次数: 0

A Dynamic Pricing-based Offloading and Resource Allocation Scheme With Data Security for Vehicle Platoon 基于定价的动态卸载和资源分配方案与车辆排的数据安全

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2024-11-06 DOI: 10.1109/jiot.2024.3492694

Yang Yang, Haiyang Yu, Yanan Zhao, Ming Chen, Jiewei Du, Yilong Ren

引用次数: 0

CD-BISHAC:Cross-Domain Scheme for Blockchain-Based Industrial Internet of Things Security Hybrid Access Control CD-BISHAC：基于区块链的工业物联网安全混合访问控制跨域方案

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2024-11-06 DOI: 10.1109/jiot.2024.3492279

Buzhen He, Tao Feng, Chunyan Liu, Chunhua Su

引用次数: 0

FedSC: A Sidechain-Enhanced Edge Computing Framework for 6G IoT Multiple Scenarios FedSC：面向 6G 物联网多种场景的侧链增强型边缘计算框架

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2024-11-06 DOI: 10.1109/jiot.2024.3492321

Zhou Fang, Shuping Dang, Yang Yang, Zhihui Ge, Xiangcheng Li, Zhenrong Zhang

引用次数: 0