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However, the dynamic nature and limited resources of edge networks bring challenges such as load imbalance and high latency while satisfying user requests. Service migration, the dynamic redeployment of service instances across distributed edge nodes, has become a key enabler for solving these challenges and optimizing edge network characteristics. Moreover, the low-latency nature of edge computing requires that service migration strategies must be in real time in order to ensure latency requirements. Thus, this paper presents a systematic survey of real-time service migration in edge networks. Specifically, we first introduce four network architectures and four basic models for real-time service migration. We then summarize four research motivations for real-time service migration and the real-time guarantee introduced during the implementation of migration strategies. To support these motivations, we present key techniques for solving the task of real-time service migration and how these algorithms and models facilitate the real-time performance of migration. We also explore latency-sensitive application scenarios, such as smart cities, smart homes, and smart manufacturing, where real-time service migration plays a critical role in sustaining performance and adaptability under dynamic conditions. Finally, we summarize the key challenges and outline promising future research directions for real-time service migration. This survey aims to provide a structured and in-depth theoretical foundation to guide future research on real-time service migration in edge networks.<\/jats:p>","DOI":"10.3390\/jsan14040079","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T10:13:51Z","timestamp":1754475231000},"page":"79","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Real-Time Service Migration in Edge Networks: A Survey"],"prefix":"10.3390","volume":"14","author":[{"given":"Yutong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China"},{"name":"Technology Innovation Center of Geoscience Knowledge and Intelligent Service, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China"},{"name":"Technology Innovation Center of Geoscience Knowledge and Intelligent Service, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1925-9518","authenticated-orcid":false,"given":"Yihong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China"},{"name":"Technology Innovation Center of Geoscience Knowledge and Intelligent Service, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhangbing","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China"},{"name":"Technology Innovation Center of Geoscience Knowledge and Intelligent Service, China University of Geosciences (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Prangon, N.F., and Wu, J. (2024). AI and computing horizons: Cloud and edge in the modern era. J. Sens. Actuator Netw., 13.","DOI":"10.3390\/jsan13040044"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1863","DOI":"10.30574\/ijsra.2024.11.1.0287","article-title":"Current state and prospects of edge computing within the Internet of Things (IoT) ecosystem","volume":"11","author":"Sodiya","year":"2024","journal-title":"Int. J. Sci. Res. Arch."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103568","DOI":"10.1016\/j.jnca.2022.103568","article-title":"Task offloading paradigm in mobile edge computing-current issues, adopted approaches, and future directions","volume":"212","author":"Akhlaqi","year":"2023","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1109\/COMST.2023.3244674","article-title":"Security and privacy on 6G network edge: A survey","volume":"25","author":"Mao","year":"2023","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Naseh, D., Shinde, S.S., and Tarchi, D. (2024). Network sliced Distributed Learning-as-a-Service for Internet of Vehicles applications in 6G non-terrestrial network scenarios. J. Sens. Actuator Netw., 13.","DOI":"10.3390\/jsan13010014"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3664200","article-title":"Adaptation in edge computing: A review on design principles and research challenges","volume":"19","author":"Golpayegani","year":"2024","journal-title":"ACM Trans. Auton. Adapt. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1109\/TCCN.2023.3346824","article-title":"Dynamic neural network-based resource management for mobile edge computing in 6G networks","volume":"10","author":"Ma","year":"2023","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_8","first-page":"1","article-title":"Real-Time Adaptive Orchestration of AI Microservices in Dynamic Edge Computing","volume":"3","author":"Ramamoorthi","year":"2023","journal-title":"J. Adv. Comput. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Va\u00f1o, R., Lacalle, I., Sowi\u0144ski, P., S-Juli\u00e1n, R., and Palau, C.E. (2023). Cloud-native workload orchestration at the edge: A deployment review and future directions. Sensors, 23.","DOI":"10.3390\/s23042215"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100667","DOI":"10.1016\/j.iot.2022.100667","article-title":"HunterPlus: AI based energy-efficient task scheduling for cloud\u2013fog computing environments","volume":"21","author":"Iftikhar","year":"2023","journal-title":"Internet Things"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Alotaibi, B. (2023). A survey on industrial Internet of Things security: Requirements, attacks, AI-based solutions, and edge computing opportunities. Sensors, 23.","DOI":"10.20944\/preprints202307.0771.v1"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3594718","article-title":"Resource management in mobile edge computing: A comprehensive survey","volume":"55","author":"Zhang","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"29689","DOI":"10.1109\/JIOT.2024.3406701","article-title":"Energy-Efficient Online Service Migration in Edge Networks","volume":"11","author":"Li","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3237","DOI":"10.1007\/s10586-023-04025-7","article-title":"Edge server placement problem in multi-access edge computing environment: Models, techniques, and applications","volume":"26","author":"Bahrami","year":"2023","journal-title":"Clust. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ray, K., Banerjee, A., and Narendra, N.C. (2020, January 12\u201314). Proactive microservice placement and migration for mobile edge computing. Proceedings of the 2020 IEEE\/ACM Symposium on Edge Computing (SEC), San Jose, CA, USA.","DOI":"10.1109\/SEC50012.2020.00010"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"14307","DOI":"10.1109\/JIOT.2023.3245611","article-title":"Management and orchestration of edge computing for IoT: A comprehensive survey","volume":"10","author":"Chiang","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MCOM.003.2100964","article-title":"FedFly: Toward migration in edge-based distributed federated learning","volume":"60","author":"Ullah","year":"2022","journal-title":"IEEE Commun. Mag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1016\/j.dcan.2022.05.004","article-title":"Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes","volume":"9","author":"Shen","year":"2023","journal-title":"Digit. Commun. Netw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1109\/TMC.2023.3246462","article-title":"Computation offloading in multi-cell networks with collaborative edge-cloud computing: A game theoretic approach","volume":"23","author":"Wu","year":"2023","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Peng, Y., Liu, L., Zhou, Y., Shi, J., and Li, J. (2019, January 9\u201313). Deep reinforcement learning-based dynamic service migration in vehicular networks. Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA.","DOI":"10.1109\/GLOBECOM38437.2019.9014294"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4505","DOI":"10.1109\/TNSM.2023.3274581","article-title":"An inference mechanism for proactive service migration at the edge","volume":"20","author":"Boulougaris","year":"2023","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1109\/TNSM.2021.3052808","article-title":"Mobility aware and dynamic migration of MEC services for the Internet of Vehicles","volume":"18","author":"Labriji","year":"2021","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6315","DOI":"10.1109\/TMC.2025.3540407","article-title":"Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems","volume":"24","author":"Chen","year":"2025","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"23497","DOI":"10.1109\/JIOT.2024.3385446","article-title":"Revenue-optimal contract design for content providers in IoT-edge caching","volume":"11","author":"Li","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8175","DOI":"10.1109\/TVT.2022.3175238","article-title":"Energy-delay minimization of task migration based on game theory in MEC-assisted vehicular networks","volume":"71","author":"Wang","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3468","DOI":"10.1109\/TNET.2024.3390416","article-title":"Federated learning with experience-driven model migration in heterogeneous edge networks","volume":"32","author":"Liu","year":"2024","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103058","DOI":"10.1016\/j.jnca.2021.103058","article-title":"Service migration in multi-access edge computing: A joint state adaptation and reinforcement learning mechanism","volume":"183","author":"Rui","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1109\/TSMC.2020.3042898","article-title":"Internet of things as system of systems: A review of methodologies, frameworks, platforms, and tools","volume":"51","author":"Fortino","year":"2020","journal-title":"IEEE Trans. Syst. Man, Cybern. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3233","DOI":"10.1109\/TNSM.2023.3242937","article-title":"Re-Scheduling IoT Services in Edge Networks","volume":"20","author":"Li","year":"2023","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"17035","DOI":"10.1109\/JIOT.2021.3135287","article-title":"Online Reconfiguration of Latency-Aware IoT Services in Edge Networks","volume":"9","author":"Li","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Choi, K., Soma, R., and Pedram, M. (2004, January 9\u201311). Dynamic voltage and frequency scaling based on workload decomposition. Proceedings of the 2004 International Symposium on Low Power Electronics and Design, New York, NY, USA. ISLPED \u201904.","DOI":"10.1145\/1013235.1013282"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"23511","DOI":"10.1109\/ACCESS.2018.2828102","article-title":"A Survey on Service Migration in Mobile Edge Computing","volume":"6","author":"Wang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1109\/TNET.2019.2916577","article-title":"Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process","volume":"27","author":"Wang","year":"2019","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"11341","DOI":"10.1109\/JIOT.2023.3332421","article-title":"Joint service migration and resource allocation in edge IoT system based on deep reinforcement learning","volume":"11","author":"Liu","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"9041","DOI":"10.1109\/TVT.2020.2999617","article-title":"A Joint Service Migration and Mobility Optimization Approach for Vehicular Edge Computing","volume":"69","author":"Yuan","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3347514","article-title":"A Dynamic Service Migration Mechanism in Edge Cognitive Computing","volume":"19","author":"Chen","year":"2019","journal-title":"ACM Trans. Internet Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3319","DOI":"10.1109\/TSG.2022.3156433","article-title":"Time-segmented multi-level reconfiguration in distribution network: A novel cloud-edge collaboration framework","volume":"13","author":"Gao","year":"2022","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5898","DOI":"10.1109\/TWC.2021.3070974","article-title":"Multi-cell mobile edge computing: Joint service migration and resource allocation","volume":"20","author":"Liang","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4898","DOI":"10.1109\/TMC.2022.3157312","article-title":"Reliable dynamic service chain scheduling in 5G networks","volume":"22","author":"Yang","year":"2022","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e2212","DOI":"10.1002\/nem.2212","article-title":"Container placement and migration strategies for cloud, fog, and edge data centers: A survey","volume":"32","author":"Kaur","year":"2022","journal-title":"Int. J. Netw. Manag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1007\/s10115-022-01822-1","article-title":"A jointly non-cooperative game-based offloading and dynamic service migration approach in mobile edge computing","volume":"65","author":"Li","year":"2023","journal-title":"Knowl. Inf. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"102872","DOI":"10.1016\/j.sysarc.2023.102872","article-title":"An automotive case study on the limits of approximation for object detection","volume":"138","author":"Caro","year":"2023","journal-title":"J. Syst. Archit."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bozkaya-Aras, E. (2025, January 24\u201327). Optimizing Service Migration in IoT Edge Networks: Digital Twin-Based Computation and Energy-Efficient Approach. Proceedings of the 2025 IEEE Wireless Communications and Networking Conference (WCNC), Milan, Italy.","DOI":"10.1109\/WCNC61545.2025.10978782"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7418","DOI":"10.1109\/TCOMM.2022.3206885","article-title":"Lyapunov optimization based mobile edge computing for Internet of Vehicles systems","volume":"70","author":"Jia","year":"2022","journal-title":"IEEE Trans. Commun."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/TMC.2021.3085527","article-title":"Mobility-aware computation offloading in edge computing using machine learning","volume":"22","author":"Maleki","year":"2021","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e3626","DOI":"10.1002\/ett.3626","article-title":"Mobile-aware dynamic resource management for edge computing","volume":"30","author":"Filiposka","year":"2019","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"72416","DOI":"10.1109\/ACCESS.2022.3189682","article-title":"Distributed location-aware task offloading in multi-UAVs enabled edge computing","volume":"10","author":"Liu","year":"2022","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1109\/TCC.2021.3063050","article-title":"Mobility and dependence-aware QoS monitoring in mobile edge computing","volume":"9","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Cloud Comput."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhao, Y., Wu, Q., Fan, Q., Zhang, C., and Li, Z. (2022, January 14\u201317). Asynchronous federated learning based mobility-aware caching in vehicular edge computing. Proceedings of the 2022 14th International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, China.","DOI":"10.1109\/WCSP55476.2022.10039430"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1109\/JSTSP.2022.3221271","article-title":"Mobility-aware cooperative caching in vehicular edge computing based on asynchronous federated and deep reinforcement learning","volume":"17","author":"Wu","year":"2022","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1109\/TMC.2021.3087198","article-title":"Energy-efficient service migration for multi-user heterogeneous dense cellular networks","volume":"22","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.1109\/COMST.2023.3273121","article-title":"Machine learning for service migration: A survey","volume":"25","author":"Toumi","year":"2023","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.1109\/TMC.2023.3239845","article-title":"Lightweight imitation learning for real-time cooperative service migration","volume":"23","author":"Ning","year":"2023","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"10519","DOI":"10.1109\/JIOT.2023.3241222","article-title":"Multiagent meta-reinforcement learning for optimized task scheduling in heterogeneous edge computing systems","volume":"10","author":"Niu","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ma, Y., Dai, M., Xia, Y., Liu, Z., Shao, S., Zhao, H., Li, G., Tang, Y., and Niu, X. (August, January 30). A Novel Approach to Predictive-Trajectory-Aware Service Migration in Edge Computing. Proceedings of the 2023 11th International Conference on Information Systems and Computing Technology (ISCTech), Qingdao, China.","DOI":"10.1109\/ISCTech60480.2023.00015"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Tocz\u00e9, K., and Nadjm-Tehrani, S. (2024). Energy-aware Distributed Microservice Request Placement at the Edge. arXiv.","DOI":"10.1145\/3676151.3719380"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"25286","DOI":"10.1109\/JIOT.2025.3558984","article-title":"Towards Collaborative and Latency-Aware Microservice Migration in Mobile Edge Computing","volume":"12","author":"Zeng","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhang, W., Hu, Y., Zhang, Y., and Raychaudhuri, D. (2016, January 12\u201315). Segue: Quality of service aware edge cloud service migration. Proceedings of the 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Luxembourg.","DOI":"10.1109\/CloudCom.2016.0061"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"6454","DOI":"10.1109\/TWC.2020.3003459","article-title":"Leveraging the power of prediction: Predictive service placement for latency-sensitive mobile edge computing","volume":"19","author":"Ma","year":"2020","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/TMC.2022.3225239","article-title":"Computing and communication cost-aware service migration enabled by transfer reinforcement learning for dynamic vehicular edge computing networks","volume":"23","author":"Peng","year":"2022","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1002\/spe.3014","article-title":"PDMA: Probabilistic service migration approach for delay-aware and mobility-aware mobile edge computing","volume":"52","author":"Xu","year":"2022","journal-title":"Softw. Pract. Exp."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"11115","DOI":"10.1109\/TII.2023.3244321","article-title":"Multi-criteria dynamic service migration for ultra-large-scale edge computing networks","volume":"19","author":"Chi","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Saha, S., Perumal, I., Abbas, M., Manimozhi, I., and Bhat, C.R. (2024). Contextual Information Based Scheduling for Service Migration in Mobile Edge Computing. Int. J. Comput. Commun. Control., 19.","DOI":"10.15837\/ijccc.2024.3.6143"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"124905","DOI":"10.1109\/ACCESS.2020.3007743","article-title":"Two-Stage Multi-Swarm Particle Swarm Optimizer for Unconstrained and Constrained Global Optimization","volume":"8","author":"Zhao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"4656","DOI":"10.1109\/TII.2018.2846549","article-title":"Adaptive Fog Configuration for the Industrial Internet of Things","volume":"14","author":"Chen","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"72985","DOI":"10.1109\/ACCESS.2020.2987574","article-title":"Online Scheduling Optimization for DAG-Based Requests Through Reinforcement Learning in Collaboration Edge Networks","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_67","unstructured":"Hacid, H., Kao, O., Mecella, M., Moha, N., and Paik, H.Y. Migration-Based Service Allocation Optimization in Dynamic IoT Networks. Proceedings of the Service-Oriented Computing."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1006\/game.1996.0044","article-title":"Potential Games","volume":"14","author":"Monderer","year":"1996","journal-title":"Games Econ. Behav."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"4800","DOI":"10.1109\/TII.2019.2951206","article-title":"A Code-Oriented Partitioning Computation Offloading Strategy for Multiple Users and Multiple Mobile Edge Computing Servers","volume":"16","author":"Ding","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"4791","DOI":"10.1109\/JIOT.2018.2869226","article-title":"Joint Optimization of Energy Consumption and Latency in Mobile Edge Computing for Internet of Things","volume":"6","author":"Cui","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1109\/TSC.2019.2962128","article-title":"Towards Service Composition Aware Virtual Machine Migration Approach in the Cloud","volume":"13","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1109\/TSC.2018.2868356","article-title":"Towards Green Service Composition Approach in the Cloud","volume":"14","author":"Wang","year":"2021","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Kientopf, K., Raza, S., Lansing, S., and G\u00fcne\u015f, M. (2017, January 8\u201313). Service management platform to support service migrations for IoT smart city applications. Proceedings of the 2017 IEEE 28th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Montreal, QC, Canada.","DOI":"10.1109\/PIMRC.2017.8292690"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"4084","DOI":"10.1109\/JIOT.2019.2959124","article-title":"Trust-Oriented IoT Service Placement for Smart Cities in Edge Computing","volume":"7","author":"Xu","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_75","first-page":"236","article-title":"Cloud Migration Techniques for Enhancing Critical Public Services: Mobile Cloud-Based Big Healthcare Data Processing in Smart Cities","volume":"8","author":"Ganesan","year":"2021","journal-title":"J. Sci. Eng. Res."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MCC.2016.44","article-title":"Migrating Smart City Applications to the Cloud","volume":"3","author":"Schleicher","year":"2016","journal-title":"IEEE Cloud Comput."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/MWC.001.1900298","article-title":"Toward Reinforcement-Learning-Based Service Deployment of 5G Mobile Edge Computing with Request-Aware Scheduling","volume":"27","author":"Zhai","year":"2020","journal-title":"IEEE Wirel. Commun."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Cao, J., Xu, L., Abdallah, R., and Shi, W. (2017, January 5\u20138). EdgeOS_H: A home operating system for internet of everything. Proceedings of the 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, GA, USA.","DOI":"10.1109\/ICDCS.2017.325"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1109\/TCE.2012.6311320","article-title":"An intelligent self-adjusting sensor for smart home services based on ZigBee communications","volume":"58","author":"Byun","year":"2012","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Zavalyshyn, I., Duarte, N.O., and Santos, N. (2017, January 12\u201314). HomePad: A privacy-aware smart hub for home environments. Proceedings of the 2018 IEEE\/ACM Symposium on Edge Computing (SEC), Seattle, WA, USA.","DOI":"10.1109\/SEC.2018.00012"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1109\/MCOM.2018.1700303","article-title":"Big data reduction for a smart city\u2019s critical infrastructural health monitoring","volume":"56","author":"Wang","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"5441","DOI":"10.48084\/etasr.3394","article-title":"Towards a New Model to Secure IoT-based Smart Home Mobile Agents using Blockchain Technology","volume":"10","author":"Sabir","year":"2020","journal-title":"Eng. Technol. Appl. Sci. Res."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s10209-017-0533-0","article-title":"Smart home services as the next mainstream of the ICT industry: Determinants of the adoption of smart home services","volume":"17","author":"Park","year":"2018","journal-title":"Univers. Access Inf. Soc."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"4225","DOI":"10.1109\/TII.2019.2899679","article-title":"A Hybrid Computing Solution and Resource Scheduling Strategy for Edge Computing in Smart Manufacturing","volume":"15","author":"Li","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"4276","DOI":"10.1109\/TII.2019.2908210","article-title":"Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network","volume":"15","author":"Lin","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/MNET.001.1800510","article-title":"AI-Enhanced Offloading in Edge Computing: When Machine Learning Meets Industrial IoT","volume":"33","author":"Sun","year":"2019","journal-title":"IEEE Netw."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1109\/MCOM.2018.1701231","article-title":"Edge Computing in IoT-Based Manufacturing","volume":"56","author":"Chen","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"e3710","DOI":"10.1002\/ett.3710","article-title":"Edge computing in smart health care systems: Review, challenges, and research directions","volume":"33","author":"Hartmann","year":"2022","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Manogaran, G., Shakeel, P.M., Fouad, H., Nam, Y., Baskar, S., Chilamkurti, N., and Sundarasekar, R. (2019). Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System. Sensors, 19.","DOI":"10.3390\/s19133030"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.jpdc.2018.08.011","article-title":"Edge-of-things computing framework for cost-effective provisioning of healthcare data","volume":"123","author":"Alam","year":"2019","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Samie, F., Tsoutsouras, V., Bauer, L., Xydis, S., Soudris, D., and Henkel, J. (2016, January 12\u201314). Computation offloading and resource allocation for low-power IoT edge devices. Proceedings of the 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), Reston, VA, USA.","DOI":"10.1109\/WF-IoT.2016.7845499"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/JSAC.2020.3020645","article-title":"Mobile Edge Computing Enabled 5G Health Monitoring for Internet of Medical Things: A Decentralized Game Theoretic Approach","volume":"39","author":"Ning","year":"2021","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"8058","DOI":"10.1109\/TII.2022.3172489","article-title":"Intelligent Edge Load Migration in SDN-IIoT for Smart Healthcare","volume":"18","author":"Babbar","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Oueida, S., Kotb, Y., Aloqaily, M., Jararweh, Y., and Baker, T. (2018). An Edge Computing Based Smart Healthcare Framework for Resource Management. Sensors, 18.","DOI":"10.3390\/s18124307"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"11887","DOI":"10.1109\/ACCESS.2017.2707439","article-title":"Mobile Cloud-Based Big Healthcare Data Processing in Smart Cities","volume":"5","author":"Islam","year":"2017","journal-title":"IEEE Access"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1007\/s12083-019-0716-y","article-title":"Vulnerability assessment as a service for fog-centric ICT ecosystems: A healthcare use case","volume":"12","author":"Nikoloudakis","year":"2019","journal-title":"Peer-to-Peer Netw. Appl."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Chaudhry, J., Saleem, K., Islam, R., Selamat, A., Ahmad, M., and Valli, C. (2017, January 9\u201312). AZSPM: Autonomic Zero-Knowledge Security Provisioning Model for Medical Control Systems in Fog Computing Environments. Proceedings of the 2017 IEEE 42nd Conference on Local Computer Networks Workshops (LCN Workshops), Singapore.","DOI":"10.1109\/LCN.Workshops.2017.73"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1109\/JSAC.2022.3229425","article-title":"Modems: Optimizing edge computing migrations for user mobility","volume":"41","author":"Kim","year":"2022","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Yuan, J., Shen, Y., Xu, R., Wei, X., and Liu, D. (2024). Elevating Security in Migration: An Enhanced Trusted Execution Environment-Based Generic Virtual Remote Attestation Scheme. Information, 15.","DOI":"10.3390\/info15080470"},{"key":"ref_100","unstructured":"Pahl, C., El Ioini, N., and Le, V.T. (2019, January 22\u201325). Blockchain based service continuity in mobile edge computing. Proceedings of the 2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS), Granada, Spain."},{"key":"ref_101","first-page":"1169","article-title":"Mobility-aware and privacy-protecting QoS optimization in mobile edge networks","volume":"23","author":"Jin","year":"2022","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_102","first-page":"478","article-title":"Privacy-aware forecasting of quality of service in mobile edge computing","volume":"16","author":"Jin","year":"2021","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1007\/s11235-023-01094-2","article-title":"Energy-efficient secure dynamic service migration for edge-based 3-D networks","volume":"85","author":"Zheng","year":"2024","journal-title":"Telecommun. Syst."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"109881","DOI":"10.1016\/j.comnet.2023.109881","article-title":"Trusted edge and cross-domain privacy enhancement model under multi-blockchain","volume":"234","author":"Minmin","year":"2023","journal-title":"Comput. Netw."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1049\/cje.2021.00.269","article-title":"An edge-cloud collaborative cross-domain identity-based authentication protocol with privacy protection","volume":"31","author":"Sun","year":"2022","journal-title":"Chin. J. Electron."},{"key":"ref_106","unstructured":"Kalalas, C., Mulinka, P., Belmonte, G.C., Fornell, M., Dalgitsis, M., Vera, F.P., S\u00e1nchez, J.S., Villares, C.V., Sedar, R., and Datsika, E. (2025). AI-Driven Vehicle Condition Monitoring with Cell-Aware Edge Service Migration. arXiv."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"4436","DOI":"10.1109\/TSC.2024.3407581","article-title":"Vrccs-ac: Reinforcement learning for service migration in vehicular edge computing systems","volume":"17","author":"Gao","year":"2024","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"3415","DOI":"10.1109\/TNSM.2021.3086721","article-title":"Deep reinforcement learning-based content migration for edge content delivery networks with vehicular nodes","volume":"18","author":"Malektaji","year":"2021","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Brandherm, F., Gedeon, J., Abboud, O., and M\u00fchlh\u00e4user, M. (2022, January 5\u20138). BigMEC: Scalable service migration for mobile edge computing. Proceedings of the 2022 IEEE\/ACM 7th Symposium on Edge Computing (SEC), Seattle, WA, USA.","DOI":"10.1109\/SEC54971.2022.00018"},{"key":"ref_110","first-page":"1485","article-title":"AI-enabled secure microservices in edge computing: Opportunities and challenges","volume":"16","author":"Moustafa","year":"2022","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Zhang, M., Cao, J., Sahni, Y., Chen, Q., Jiang, S., and Wu, T. (2022, January 15\u201318). Eaas: A service-oriented edge computing framework towards distributed intelligence. Proceedings of the 2022 IEEE International Conference on Service-Oriented System Engineering (SOSE), Newark, CA, USA.","DOI":"10.1109\/SOSE55356.2022.00026"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"2892","DOI":"10.1109\/COMST.2023.3316615","article-title":"Combining federated learning and edge computing toward ubiquitous intelligence in 6G network: Challenges, recent advances, and future directions","volume":"25","author":"Duan","year":"2023","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"107","DOI":"10.23919\/JCIN.2022.9815195","article-title":"6G-enabled edge AI for metaverse: Challenges, methods, and future research directions","volume":"7","author":"Chang","year":"2022","journal-title":"J. Commun. Inf. Netw."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Dritsas, E., Ramantas, K., and Verikoukis, C. (2024, January 21\u201323). A Mobility-Aware Reinforcement Learning Proactive Solution for State Data Migration in Edge Computing. Proceedings of the 2024 IEEE 29th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Athens, Greece.","DOI":"10.1109\/CAMAD62243.2024.10943072"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"11937","DOI":"10.1109\/TMC.2024.3404125","article-title":"Federated deep reinforcement learning for prediction-based network slice mobility in 6G mobile networks","volume":"23","author":"Ming","year":"2024","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/JSAC.2021.3126076","article-title":"Edge artificial intelligence for 6G: Vision, enabling technologies, and applications","volume":"40","author":"Letaief","year":"2021","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"4090","DOI":"10.1109\/TNSM.2024.3385484","article-title":"Realizing the Carbon-Aware Service Provision in ICT System","volume":"21","author":"Sun","year":"2024","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/JSAC.2023.3310047","article-title":"Traffic prediction-assisted federated deep reinforcement learning for service migration in digital twins-enabled MEC networks","volume":"41","author":"Chen","year":"2023","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"74011","DOI":"10.1109\/ACCESS.2024.3404222","article-title":"Enhancing sustainable edge computing offloading via renewable prediction for energy harvesting","volume":"12","author":"Alhartomi","year":"2024","journal-title":"IEEE Access"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"e4899","DOI":"10.1002\/dac.4899","article-title":"Energy aware cloud-edge service placement approaches in the Internet of Things communications","volume":"35","author":"Heng","year":"2022","journal-title":"Int. J. Commun. Syst."},{"key":"ref_121","first-page":"1","article-title":"Integrating Edge and Cloud Computing for Efficient Big Data Processing in IoT Environments: Enhancing Smart City Applications with Fog Computing","volume":"14","author":"Belkacem","year":"2024","journal-title":"Stud. Knowl. Discov. Intell. Syst. Distrib. Anal."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.comcom.2023.06.001","article-title":"Mobility-aware edge server placement for mobile edge computing","volume":"208","author":"Chen","year":"2023","journal-title":"Comput. Commun."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MNET.2025.3533581","article-title":"Cloud-Edge-End Collaborative Inference in Mobile Networks: Challenges and Solutions","volume":"39","author":"Zheng","year":"2025","journal-title":"IEEE Network"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1109\/TPDS.2020.3035044","article-title":"Cuttlefish: Neural configuration adaptation for video analysis in live augmented reality","volume":"32","author":"Chen","year":"2020","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Tayeb, H., Bramas, B., Faverge, M., and Guermouche, A. (2024, January 27\u201331). Dynamic Tasks Scheduling with Multiple Priorities on Heterogeneous Computing Systems. Proceedings of the 2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), San Francisco, CA, USA.","DOI":"10.1109\/IPDPSW63119.2024.00014"}],"container-title":["Journal of Sensor and Actuator Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2224-2708\/14\/4\/79\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:20:30Z","timestamp":1760034030000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2224-2708\/14\/4\/79"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,1]]},"references-count":125,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["jsan14040079"],"URL":"https:\/\/doi.org\/10.3390\/jsan14040079","relation":{},"ISSN":["2224-2708"],"issn-type":[{"value":"2224-2708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,1]]}}}