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}
Pub Date : 2025-12-30DOI: 10.1109/tsc.2025.3649198
Xiangbo Tian, Shi Ying, Tiangang Li, Ting Zhang, Yong Wang
{"title":"DALAD: Unsupervised Detection of Global and Local Anomalies in Microservice Systems","authors":"Xiangbo Tian, Shi Ying, Tiangang Li, Ting Zhang, Yong Wang","doi":"10.1109/tsc.2025.3649198","DOIUrl":"https://doi.org/10.1109/tsc.2025.3649198","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"16 1","pages":"1-13"},"PeriodicalIF":8.1,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894665","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 : 2025-12-29DOI: 10.1109/tsc.2025.3649194
Ming-Can Geng, Wei-Neng Chen, Jun Zhang
{"title":"A Knee Point-Driven Set-Based Swarm Optimizer for Computing Tasks Allocation Oriented to Marginal Utility in Fog Computing","authors":"Ming-Can Geng, Wei-Neng Chen, Jun Zhang","doi":"10.1109/tsc.2025.3649194","DOIUrl":"https://doi.org/10.1109/tsc.2025.3649194","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"54 1","pages":"1-14"},"PeriodicalIF":8.1,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894666","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 : 2025-12-25DOI: 10.1109/tsc.2025.3648444
Zihan Huang, Tong Li, Yong Li
{"title":"SMAUG: Semantic-Enhanced Mobile App Usage Data Generation With LLM","authors":"Zihan Huang, Tong Li, Yong Li","doi":"10.1109/tsc.2025.3648444","DOIUrl":"https://doi.org/10.1109/tsc.2025.3648444","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"48 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145829974","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}
{"title":"AC-BaaS: An Asynchronous Cross-Blockchain as a Service for the Internet of Things","authors":"Lingxiao Yang, Xuewen Dong, Zhiguo Wan, Sheng Gao, Wei Tong, Yong Yu, Yulong Shen","doi":"10.1109/tsc.2025.3647639","DOIUrl":"https://doi.org/10.1109/tsc.2025.3647639","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"363 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823139","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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