{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T11:28:51Z","timestamp":1768217331583,"version":"3.49.0"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Sci."],"abstract":"<jats:p>Automated healthcare IoT systems demand secure, low-latency, and energy-efficient computation\u2014capabilities well-supported by fog computing. Effective selection of fog nodes is critical for maximizing the performance of fog-based IoT platforms. This paper introduces a Secure Proximal Policy Optimization (Secure PPO) algorithm for trust-aware fog node selection, considering latency, energy consumption, processing power, and a trust flag for each node. Secure PPO enforces a trust constraint while optimizing latency and energy via PPO's clipped objective, ensuring stable and reliable learning. Simulation results demonstrate that Secure PPO achieves substantial improvements over A2C and Deep Q-Networks (DQN). Specifically, Secure PPO reduces inference latency by 24.36 and 37.57%, lowers convergence time by 55.56 and 66.67%, and decreases energy consumption by 11.90 and 20.04% compared to A2C and DQN, respectively. Additionally, Secure PPO improves accuracy by 9.42 and 18.88% over A2C and DQN. The framework maintains sub-millisecond inference time and ensures secure, reliable fog-based execution of automated healthcare tasks, substantially enhancing patient safety and operational efficiency within healthcare IoT environments.<\/jats:p>","DOI":"10.3389\/fcomp.2025.1723498","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T08:17:58Z","timestamp":1768205878000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Latency and trust constrained fog node selection using deep reinforcement learning"],"prefix":"10.3389","volume":"7","author":[{"given":"Anju","family":"Babu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G. Josemin","family":"Bala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"B1","unstructured":"Optimisation of volunteer node selection for scalable and trustworthy fog environments\n          \n          \n            \n              Alkhalaf\n              A.\n            \n            \n              Hussain\n              F. K.\n            \n          \n          Sydney, NSW\n          IEEE\n          Proc. IEEE Int. Conf. e-Business Engineering (ICEBE)\n          \n          2023"},{"key":"B2","doi-asserted-by":"publisher","first-page":"10299","DOI":"10.1007\/s10586-024-04518-z","article-title":"Reinforcement learning-based task offloading of IoT applications in fog computing: algorithms and optimization techniques","volume":"27","author":"Allaoui","year":"2024","journal-title":"Cluster Comput"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e70097","DOI":"10.1002\/cpe.70097","article-title":"Service placement in fog computing using a combination of reinforcement learning and improved gray wolf optimization method","volume":"37","author":"Ashkani","year":"2025","journal-title":"Concurr. Comput. Pract. 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Comput"},{"key":"B7","doi-asserted-by":"publisher","first-page":"3176","DOI":"10.1109\/TCYB.2020.2989309","article-title":"An ant colony optimization-based multiobjective service replica placement strategy for fog computing","volume":"51","author":"Huang","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"B8","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.dcan.2018.10.008","article-title":"Enabling intelligence in fog computing to achieve energy and latency reduction","volume":"5","author":"La","year":"2019","journal-title":"Digital Commun. Netw."},{"key":"B9","doi-asserted-by":"publisher","first-page":"27073","DOI":"10.1007\/s11227-024-06439-5","article-title":"Multi-objective application placement in fog computing using graph neural network-based reinforcement learning","volume":"80","author":"Lera","year":"2024","journal-title":"J. Supercomput."},{"key":"B10","doi-asserted-by":"publisher","first-page":"3641","DOI":"10.1109\/JIOT.2018.2889511","article-title":"Availability-aware service placement policy in fog computing based on graph partitions","volume":"6","author":"Lera","year":"2018","journal-title":"IEEE Inter. Things J"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.1007\/s11063-021-10708-2","article-title":"Solving the multi-objective problem of IoT service placement in fog computing using cuckoo search algorithm","volume":"54","author":"Liu","year":"2022","journal-title":"Neural Process Lett"},{"key":"B12","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/s10586-025-05588-3","article-title":"FAMASO: fog-adaptive multi-agent scheduling optimization","volume":"28","author":"Nagabushnam","year":"2025","journal-title":"Cluster Comput"},{"key":"B13","first-page":"466","article-title":"\u201cReinforcement learning for value-based placement of fog services,\u201d","volume-title":"Proceedings of the IFIP\/IEEE International Symposium on Integrated Network Management","author":"Poltronieri","year":"2021"},{"key":"B14","doi-asserted-by":"crossref","DOI":"10.1109\/ICRITO48877.2020.9197885","article-title":"A survey of optimization algorithms for fog computing service placement","volume-title":"Proc. Int. Conf. Reliability, Infocom Technologies and Optimization (ICRITO)","author":"Raghavendra","year":"2020"},{"key":"B15","doi-asserted-by":"publisher","first-page":"141","DOI":"10.23919\/ICN.2020.0020","article-title":"Deep reinforcement learning-based computation offloading and resource allocation for low-latency fog radio access networks","volume":"1","author":"Rahman","year":"2020","journal-title":"Intell. Converg. Netw."},{"key":"B16","unstructured":"A secure task-offloading framework for cooperative fog computing environment\n          \n          \n            \n              Roshan\n              R.\n            \n            \n              Matam\n              R.\n            \n            \n              Mukherjee\n              M.\n            \n            \n              Mauri\n              J.\n            \n            \n              Tripathy\n              S.\n            \n          \n          Taipei, Taiwan\n          IEEE\n          Proc. IEEE Global Communications Conference (GLOBECOM)\n          \n          2020"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3391196","article-title":"An overview of the service placement problem in fog and edge computing","volume":"53","author":"Salaht","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"B18","doi-asserted-by":"publisher","first-page":"2438","DOI":"10.1002\/spe.2986","article-title":"Toward an autonomic approach for internet of things service placement using gray wolf optimization in the fog computing environment","volume":"51","author":"Salimian","year":"2021","journal-title":"Softw. Pract. Exp."},{"key":"B19","doi-asserted-by":"publisher","first-page":"101112","DOI":"10.1016\/j.iot.2024.101112","article-title":"Intelligent service placement algorithm based on DDQN and prioritized experience replay in IoT\u2013fog computing environment","volume":"25","author":"Sharma","year":"2024","journal-title":"Intern. 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Sci."},{"key":"B23","doi-asserted-by":"publisher","first-page":"7948","DOI":"10.1016\/j.jksuci.2022.07.012","article-title":"A dynamic planning model for deploying service function chains in fog\u2013cloud computing","volume":"34","author":"Zhang","year":"2022","journal-title":"J. King Saud Univ. Comput. Inform. Sci."}],"container-title":["Frontiers in Computer Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcomp.2025.1723498\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T08:17:59Z","timestamp":1768205879000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcomp.2025.1723498\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,12]]},"references-count":23,"alternative-id":["10.3389\/fcomp.2025.1723498"],"URL":"https:\/\/doi.org\/10.3389\/fcomp.2025.1723498","relation":{},"ISSN":["2624-9898"],"issn-type":[{"value":"2624-9898","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,12]]},"article-number":"1723498"}}