Pub Date : 2026-01-13DOI: 10.1109/tsc.2026.3653815
Bin Jiang, Yongxiang Kuang, Houbing Herbert Song
{"title":"Privacy-Preserving Services for Internet of Medical Things: Architecture, Techniques, and Challenges","authors":"Bin Jiang, Yongxiang Kuang, Houbing Herbert Song","doi":"10.1109/tsc.2026.3653815","DOIUrl":"https://doi.org/10.1109/tsc.2026.3653815","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"94 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961762","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}
Pub Date : 2026-01-13DOI: 10.1109/tsc.2026.3653423
Dongho Ham, Yeongjin Kim, Jeongho Kwak
{"title":"Optimal Computation Load Balancing for Integrated Service Caching and Offloading Systems in Hierarchical Cloud Architecture","authors":"Dongho Ham, Yeongjin Kim, Jeongho Kwak","doi":"10.1109/tsc.2026.3653423","DOIUrl":"https://doi.org/10.1109/tsc.2026.3653423","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"77 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961763","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}
Pub Date : 2026-01-12DOI: 10.1109/TSC.2026.3651622
Qianqian Wu;Qiang Liu;Ying He;Zefan Wu
Data collection and distributed task execution in Internet of Things (IoT) networks require efficient coordination among autonomous agents to handle the growing volume of sensing and computational demands. Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) present promising candidates for these operations due to their complementary capabilities and mobility advantages. However, effective cooperation between these heterogeneous agents faces significant challenges including communication limitations, energy constraints, and suboptimal task allocation efficiency. In this paper, we aim to maximize data collection capacity, task completion rates, while minimizing energy consumption across all UAVs. We propose U2GNet, a novel UGV-assisted framework for UAV that enables efficient task offloading and resource allocation in dynamic environments by leveraging Deep Reinforcement Learning (DRL) enhanced with Heterogeneous Graph Attention Networks (HGAT). The framework employs HGAT to process local observations and information shared by neighboring agents, while Gated Recurrent Units (GRU) address partial observability by integrating historical information, and DRL optimizes the decision-making process. Simulation results demonstrate that U2GNet improves the average data collection rate and task completion rate by 16.90% and 10.81% respectively compared to the baseline HGN approach.
{"title":"UGV-Assisted Task Allocation for UAVs: A Heterogeneous Graph Reinforcement Learning Approach","authors":"Qianqian Wu;Qiang Liu;Ying He;Zefan Wu","doi":"10.1109/TSC.2026.3651622","DOIUrl":"10.1109/TSC.2026.3651622","url":null,"abstract":"Data collection and distributed task execution in Internet of Things (IoT) networks require efficient coordination among autonomous agents to handle the growing volume of sensing and computational demands. Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) present promising candidates for these operations due to their complementary capabilities and mobility advantages. However, effective cooperation between these heterogeneous agents faces significant challenges including communication limitations, energy constraints, and suboptimal task allocation efficiency. In this paper, we aim to maximize data collection capacity, task completion rates, while minimizing energy consumption across all UAVs. We propose U2GNet, a novel UGV-assisted framework for UAV that enables efficient task offloading and resource allocation in dynamic environments by leveraging Deep Reinforcement Learning (DRL) enhanced with Heterogeneous Graph Attention Networks (HGAT). The framework employs HGAT to process local observations and information shared by neighboring agents, while Gated Recurrent Units (GRU) address partial observability by integrating historical information, and DRL optimizes the decision-making process. Simulation results demonstrate that U2GNet improves the average data collection rate and task completion rate by 16.90% and 10.81% respectively compared to the baseline HGN approach.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 1","pages":"752-765"},"PeriodicalIF":5.8,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145955193","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}
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