{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T07:45:35Z","timestamp":1766735135301,"version":"3.48.0"},"reference-count":21,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Communications"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The sixth\u2010generation (6G) mobile network is expected to trigger an unprecedented surge in data traffic. unmanned aerial vehicle (UAV)\u2010assisted mobile edge computing (MEC) has emerged as a promising paradigm for emergency communications, yet dynamic user mobility and heterogeneous task demands pose severe challenges. Existing reinforcement learning (RL) methods (such as proximal policy optimization (PPO)), while effective in continuous control, often suffer from unstable convergence and neglect task\u2010priority differentiation, leading to excessive delays for critical tasks. To address these issues, we propose a novel KM\u2010PPO framework that integrates interference\u2010aware k\u2010means clustering with PPO. The clustering stage aggregates users by spatial distribution and task urgency, yielding an interference\u2010suppressed initial UAV deployment. The trajectory optimization stage employs PPO with clipped probability ratios, which serve to constrain policy updates and maintain stability, and a priority\u2010sensitive reward function, enabling UAVs to adaptively adjust motion trajectories under dynamic conditions. Compared with the baseline algorithms of k\u2010means integrated with twin delayed deep deterministic policy gradient (KM\u2010TD3) and k\u2010means integrated with soft actor\u2010critic (KM\u2010SAC), our method reduces average system latency by 15.9% and 24.6%, respectively, while achieving an 86.7% success rate for high\u2010priority tasks. These results demonstrate that KM\u2010PPO not only ensures stable convergence in the environments but also guarantees quality of service (QoS) for mission\u2010critical tasks, highlighting viability for UAV MEC deployments.<\/jats:p>","DOI":"10.1049\/cmu2.70126","type":"journal-article","created":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T07:42:26Z","timestamp":1766734946000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DRL\u2010Based Joint Clustering and Trajectory Optimization for UAV\u2010Assisted Emergency Networks"],"prefix":"10.1049","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3144-2542","authenticated-orcid":false,"given":"Ruirui","family":"Xu","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence Putian University  Putian China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6599-1261","authenticated-orcid":false,"given":"Yuanmo","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence Putian University  Putian China"}]},{"given":"Jian","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Communications China Mobile Fujian Co., Ltd. Putian Branch  Putian China"}]},{"given":"Huayong","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence Putian University  Putian China"},{"name":"Faculty of Engineering Universiti Malaya  Kuala Lumpur Malaysia"}]},{"given":"Zhongyue","family":"Lei","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence Putian University  Putian China"}]}],"member":"265","published-online":{"date-parts":[[2025,12,25]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2023.100885"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2022.3221557"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3059691"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3270332"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3073208"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3381668"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phycom.2024.102536"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/aerospace10030208"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2023.3339853"},{"issue":"01","key":"e_1_2_10_11_1","article-title":"Cooperative Path Planning of Multiple UAVs by Using Max\u2010min Ant Colony Optimization Along With Cauchy Mutant Operator","volume":"20","author":"Zain A. 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