IEEE Transactions on Mobile Computing最新文献_第3页

Traffic Load-Aware Resource Management Strategy for Underwater Wireless Sensor Networks 水下无线传感器网络的流量负载感知资源管理策略

IF 7.9 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-12 DOI: 10.1109/tmc.2024.3459896

Tong Zhang, Yu Gou, Jun Liu, Jun-Hong Cui

引用次数: 0

Pattern-sensitive Local Differential Privacy for Finite-Range Time-series Data in Mobile Crowdsensing 移动人群感应中有限范围时间序列数据的模式敏感型局部差分隐私保护

IF 7.9 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-11 DOI: 10.1109/tmc.2024.3445973

Zhetao Li, Xiyu Zeng, Yong Xiao, Chengxin Li, Wentai Wu, Haolin Liu

引用次数: 0

Design and Performance of Resonant Beam Communications—Part I: Quasi-Static Scenario 共振波束通信的设计与性能--第一部分：准静态场景

IF 7.9 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-11 DOI: 10.1109/tmc.2024.3458415

Dongxu Li, Yuanming Tian, Chuan Huang, Qingwen Liu, Shengli Zhou

引用次数: 0

Distributed Task Offloading and Resource Allocation for Latency Minimization in Mobile Edge Computing Networks 移动边缘计算网络中的分布式任务卸载和资源分配以实现延迟最小化

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-10 DOI: 10.1109/TMC.2024.3458185

Minwoo Kim;Jonggyu Jang;Youngchol Choi;Hyun Jong Yang

{"title":"Distributed Task Offloading and Resource Allocation for Latency Minimization in Mobile Edge Computing Networks","authors":"Minwoo Kim;Jonggyu Jang;Youngchol Choi;Hyun Jong Yang","doi":"10.1109/TMC.2024.3458185","DOIUrl":"10.1109/TMC.2024.3458185","url":null,"abstract":"The growth in artificial intelligence (AI) technology has attracted substantial interests in latency-aware task offloading of mobile edge computing (MEC)—namely, minimizing service latency. Additionally, the use of MEC systems poses an additional problem arising from limited battery resources of MDs. This paper tackles the pressing challenge of latency-aware distributed task offloading optimization, where user association (UA), resource allocation (RA), full-task offloading, and battery of mobile devices (MDs) are jointly considered. In existing studies, joint optimization of overall task offloading and UA is seldom considered due to the complexity of combinatorial optimization problems, and in cases where it is considered, linear objective functions such as power consumption are adopted. Revolutionizing the realm of MEC, our objective includes all major components contributing to users’ quality of experience, including latency and energy consumption. To achieve this, we first formulate an NP-hard combinatorial problem, where the objective function comprises three elements: communication latency, computation latency, and battery usage. We derive a closed-form RA solution of the problem; next, we provide a distributed pricing-based UA solution. We simulate the proposed algorithm for various resource-intensive tasks. Our numerical results show that the proposed method Pareto-dominates baseline methods. More specifically, the results demonstrate that the proposed method can outperform baseline methods by \u0000<italic>1.62 times shorter latency</i>\u0000 with \u0000<italic>41.2% less energy consumption</i>\u0000.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"15149-15166"},"PeriodicalIF":7.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Democratizing Federated WiFi-Based Human Activity Recognition Using Hypothesis Transfer 利用假设转移实现基于 WiFi 的联合人类活动识别的民主化

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-10 DOI: 10.1109/TMC.2024.3457788

Bing Li;Wei Cui;Le Zhang;Qi Yang;Min Wu;Joey Tianyi Zhou

{"title":"Democratizing Federated WiFi-Based Human Activity Recognition Using Hypothesis Transfer","authors":"Bing Li;Wei Cui;Le Zhang;Qi Yang;Min Wu;Joey Tianyi Zhou","doi":"10.1109/TMC.2024.3457788","DOIUrl":"10.1109/TMC.2024.3457788","url":null,"abstract":"Human activity recognition (HAR) is a crucial task in IoT systems with applications ranging from surveillance and intruder detection to home automation and more. Recently, non-invasive HAR utilizing WiFi signals has gained considerable attention due to advancements in ubiquitous WiFi technologies. However, recent studies have revealed significant privacy risks associated with WiFi signals, raising concerns about bio-information leakage. To address these concerns, the decentralized paradigm, particularly federated learning (FL), has emerged as a promising approach for training HAR models while preserving data privacy. Nevertheless, FL models may struggle in end-user environments due to substantial domain discrepancies between the source training data and the target end-user environment. This discrepancy arises from the sensitivity of WiFi signals to environmental changes, resulting in notable domain shifts. As a consequence, FL-based HAR approaches often face challenges when deployed in real-world WiFi environments. Albeit there are pioneer attempts on federated domain adaptation, they typically require non-trivial communication and computation cost, which is prohibitively expensive especially considering edge-based hardware equipment of end-user environment. In this paper, we propose a model to democratize the WiFi-based HAR system by enhancing recognition accuracy in unannotated end-user environments while prioritizing data privacy. Our model leverages the hypothesis transfer and a lightweight hypothesis ensemble to mitigate negative transfer. We prove a tighter theoretical upper bound compared to existing multi-source federated domain adaptation models. Extensive experiments shows our model improves the average accuracy by approximately 10 absolute percentage points in both cross-person and cross-environment settings comparing several state-of-the-art baselines.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"15132-15148"},"PeriodicalIF":7.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Joint Optimization of Device Placement and Model Partitioning for Cooperative DNN Inference in Heterogeneous Edge Computing 为异构边缘计算中的合作 DNN 推断联合优化设备布局和模型划分

IF 7.9 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-10 DOI: 10.1109/tmc.2024.3457793

Penglin Dai, Biao Han, Ke Li, Xincao Xu, Huanlai Xing, Kai Liu

引用次数: 0

PHCG: PLC Honeypoint Communication Generator for Industrial IoT PHCG：用于工业物联网的 PLC Honeypoint 通信发生器

IF 7.9 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-10 DOI: 10.1109/tmc.2024.3455564

Hao Liu, Yinghai Zhou, Binxing Fang, Yanbin Sun, Ning Hu, Zhihong Tian

引用次数: 0

Network-wide Data Collection Based on In-band Network Telemetry for Digital Twin Networks 基于数字孪生网络带内网络遥测的全网数据采集

IF 7.9 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-09 DOI: 10.1109/tmc.2024.3456584

Zhihao Wang, Dingde Jiang, Shahid Mumtaz

引用次数: 0

Budget-Constrained Digital Twin Synchronization and Its Application on Fidelity-Aware Queries in Edge Computing 受预算限制的数字双胞胎同步及其在边缘计算保真度感知查询中的应用

IF 7.9 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-06 DOI: 10.1109/tmc.2024.3455357

Yuchen Li, Weifa Liang, Zichuan Xu, Wenzheng Xu, Xiaohua Jia

引用次数: 0

Blockchain-Aided Digital Twin Offloading Mechanism in Space-Air-Ground Networks 天-空-地网络中的区块链辅助数字双胞胎卸载机制

IF 7.9 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-06 DOI: 10.1109/tmc.2024.3455417

Yongkang Gong, Haipeng Yao, Zehui Xiong, C. L. Philip Chen, Dusit Niyato

引用次数: 0