A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
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Abstract (arXiv)
As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures which bring network functions and contents to the network edge are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks including definition, architecture and advantages. Next, a comprehensive survey of issues on computing, caching and communication techniques at the network edge is presented respectively. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks such as cloud technology, SDN/NFV and smart devices are discussed. Finally, open research challenges and future directions are presented as well.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Mar 2017.
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Citing papers per year: 2021: 4; 2022: 2; 2023: 1; 2024: 3; 2025: 1; 2026: 1.
12 papers.
- 2021Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learningcited by 5
- 2023A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniquescited by 4
- 2022A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks with Transient Datacited by 3
- 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 3
- 2024Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperationcited by 2
- 2024Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learningcited by 1
- 2021Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computingcited by 0
- 2021Learning-based decentralized offloading decision making in an adversarial environmentcited by 0
- 2021Learning from Images: Proactive Caching with Parallel Convolutional Neural Networkscited by 0
- 2022Beyond 5G Networks: Integration of Communication, Computing, Caching, and Controlcited by 0
- 2025MetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse Systemscited by 0
- 2026When Simultaneous Localization and Mapping Meets Wireless Communications: A Surveycited by 0
- 2026When Simultaneous Localization and Mapping Meets Wireless Communications: A Survey28 Jan 2026
- 2025MetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse Systems24 Aug 2025
- 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence7 Jul 2024
- 2024Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning1 Jul 2024
- 2024Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation22 Jan 2024
- 2023A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques16 Feb 2023
- 2022Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control26 Dec 2022
- 2022A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks with Transient Data16 Mar 2022
- 2021Learning from Images: Proactive Caching with Parallel Convolutional Neural Networks15 Aug 2021
- 2021Learning-based decentralized offloading decision making in an adversarial environment26 Apr 2021
- 2021Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computing15 Apr 2021
- 2021Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learning14 Jan 2021