mlsanityData to 7 Oct 2026

Mobile Edge Computing: A Survey on Architecture and Computation Offloading

arXiv 1702.05309 ↗First posted 17 Feb 2017cs.IT cs.NI math.ITcited by 52

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Abstract (arXiv)

Technological evolution of mobile user equipments (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. Suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud (CC). Nevertheless, this option introduces significant execution delay consisting in delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such delay is inconvenient and make the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: i) decision on computation offloading, ii) allocation of computing resource within the MEC, and iii) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Feb 2017.

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Citing papers per year: 2021: 6; 2022: 15; 2023: 8; 2024: 5; 2025: 7; 2026: 11.

52 papers.

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  1. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  2. 2023Green Edge AI: A Contemporary Surveycited by 24
  3. 2021Communication-Efficient ADMM-based Federated Learningcited by 14
  4. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  5. 2022Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoTcited by 12
  6. 2024Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approachcited by 12
  7. 2022Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computingcited by 10
  8. 2022FedGiA: An Efficient Hybrid Algorithm for Federated Learningcited by 8
  9. 2021Character-level HyperNetworks for Hate Speech Detectioncited by 7
  10. 2023Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approachcited by 6
  11. 2024GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networkscited by 6
  12. 2022Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networkscited by 5
38 more
  1. 2022Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisionscited by 4
  2. 2023The Case for Hierarchical Deep Learning Inference at the Network Edgecited by 4
  3. 2021Offloading Algorithms for Maximizing Inference Accuracy on Edge Device Under a Time Constraintcited by 3
  4. 2022Edge-centric Optimization of Multi-modal ML-driven eHealth Applicationscited by 3
  5. 2024Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Modelcited by 3
  6. 2021Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learningcited by 2
  7. 2021Federated Ensemble Model-based Reinforcement Learning in Edge Computingcited by 2
  8. 2022Heterogeneous Federated Learning on a Graphcited by 2
  9. 2023Multi-objective Deep Reinforcement Learning for Mobile Edge Computingcited by 2
  10. 2023Reliable and Efficient Data Collection in UAV-based IoT Networkscited by 2
  11. 2025Efficient Distributed Retrieval-Augmented Generation for Enhancing Language Model Performancecited by 2
  12. 2022On Jointly Optimizing Partial Offloading and SFC Mapping: A Cooperative Dual-agent Deep Reinforcement Learning Approachcited by 1
  13. 2023On the Regret of Online Edge Service Hostingcited by 1
  14. 2023Dynamic Partial Computation Offloading for the Metaverse in In-Network Computingcited by 1
  15. 2025Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architecturescited by 1
  16. 2025Cooperative Inference for Real-Time 3D Human Pose Estimation in Multi-Device Edge Networkscited by 1
  17. 2026Multi-Turn Reasoning LLMs for Task Offloading in Mobile Edge Computingcited by 1
  18. 2021Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoTcited by 0
  19. 2022Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Applicationcited by 0
  20. 2022Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Datasetcited by 0
  21. 2022FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrationscited by 0
  22. 2022Hybrid HMM Decoder For Convolutional Codes By Joint Trellis-Like Structure and Channel Priorcited by 0
  23. 2022Beyond 5G Networks: Integration of Communication, Computing, Caching, and Controlcited by 0
  24. 2023Scheduling Inference Workloads on Distributed Edge Clusters with Reinforcement Learningcited by 0
  25. 2024Computation Offloading for Multi-server Multi-access Edge Vehicular Networks: A DDQN-based Methodcited by 0
  26. 2024DeepAir: A Multi-Agent Deep Reinforcement Learning Based Scheme for an Unknown User Location Problemcited by 0
  27. 2025A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralizationcited by 0
  28. 2025UrbanInsight: A Distributed Edge Computing Framework with LLM-Powered Data Filtering for Smart City Digital Twinscited by 0
  29. 2025A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Managementcited by 0
  30. 2025IslandRun: Privacy-Aware Multi-Objective Orchestration for Distributed AI Inferencecited by 0
  31. 2026Probe-then-Commit Multi-Objective Bandits: Theoretical Benefits of Limited Multi-Arm Feedbackcited by 0
  32. 2026Accuracy-Delay Trade-Off in LLM Offloading via Token-Level Uncertaintycited by 0
  33. 2026Entropy-Aware Task Offloading in Mobile Edge Computingcited by 0
  34. 2026Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directionscited by 0
  35. 2026Lightweight Quantum Agent for Edge Systems: Joint PQC and NOMA Resource Allocationcited by 0
  36. 2026Joint Optimization of Trajectory Control, Resource Allocation, and Task Offloading for Multi-UAV-Assisted IoVcited by 0
  37. 2026AI-IoT-Robotics Integration: Survey of Frameworks, Emerging Trends, and the Path Toward Connected Roboticscited by 0
  38. 2026Learning-Based Collaborative MEC for LLM Inference with Soft-Deadline Awareness via Transformer-Enhanced PPOcited by 0

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