Survey on Multi-Access Edge Computing for Internet of Things Realization
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Abstract (arXiv)
The Internet of Things (IoT) has recently advanced from an experimental technology to what will become the backbone of future customer value for both product and service sector businesses. This underscores the cardinal role of IoT on the journey towards the fifth generation (5G) of wireless communication systems. IoT technologies augmented with intelligent and big data analytics are expected to rapidly change the landscape of myriads of application domains ranging from health care to smart cities and industrial automations. The emergence of Multi-Access Edge Computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency access to radio network resources. IoT is identified as a key use case of MEC, given MEC's ability to provide cloud platform and gateway services at the network edge. MEC will inspire the development of myriads of applications and services with demand for ultra low latency and high Quality of Service (QoS) due to its dense geographical distribution and wide support for mobility. MEC is therefore an important enabler of IoT applications and services which require real-time operations. In this survey, we provide a holistic overview on the exploitation of MEC technology for the realization of IoT applications and their synergies. We further discuss the technical aspects of enabling MEC in IoT and provide some insight into various other integration technologies therein.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 May 2018.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 3; 2024: 5; 2025: 0; 2026: 1.
12 papers.
- 2023Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlookcited by 11
- 2024Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectoriescited by 9
- 2021A Review on Edge Analytics: Issues, Challenges, Opportunities, Promises, Future Directions, and Applicationscited by 5
- 2024Multiple Access in the Era of Distributed Computing and Edge Intelligencecited by 4
- 2023Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computingcited by 3
- 2021Reinforcement Learning-based Dynamic Service Placement in Vehicular Networkscited by 2
- 2023Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Servicescited by 2
- 2024Root Cause Localization for Microservice Systems in Cloud-edge Collaborative Environmentscited by 2
- 2021DRLD-SP: A Deep Reinforcement Learning-based Dynamic Service Placement in Edge-Enabled Internet of Vehiclescited by 1
- 2024Adaptive Client Selection with Personalization for Communication Efficient Federated Learningcited by 1
- 2024Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computingcited by 0
- 2026GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networkscited by 0
- 2026GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networks31 Jul 2026
- 2024Adaptive Client Selection with Personalization for Communication Efficient Federated Learning26 Nov 2024
- 2024Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing25 Sep 2024
- 2024Root Cause Localization for Microservice Systems in Cloud-edge Collaborative Environments19 Jun 2024
- 2024Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories22 Apr 2024
- 2024Multiple Access in the Era of Distributed Computing and Edge Intelligence26 Feb 2024
- 2023Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing16 Dec 2023
- 2023Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services4 Jul 2023
- 2023Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook3 Mar 2023
- 2021A Review on Edge Analytics: Issues, Challenges, Opportunities, Promises, Future Directions, and Applications1 Jul 2021
- 2021DRLD-SP: A Deep Reinforcement Learning-based Dynamic Service Placement in Edge-Enabled Internet of Vehicles11 Jun 2021
- 2021Reinforcement Learning-based Dynamic Service Placement in Vehicular Networks31 May 2021