A Survey on Mobile Edge Computing: The Communication Perspective

By Yuyi Mao, Changsheng You, Jun Zhang, Kaibin Huang, Khaled Ben Letaief. 2017

4,650 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, ML Systems & Hardware

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Citations in the corpus

Adaptive Federated Learning in Resource Constrained Edge Computing Systems (2018, background): “However, it is challenging to perform distributed machine learning on resource-constrained MEC systems.”
Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks (2019, background): “spectrum, only a portion of the UEs can be selected to access the radio channel and send their trained updates in each global aggregation [5, 11].”
Scheduling Policies for Federated Learning in Wireless Networks (2019, background): “…in a data center, to edge computing systems which largely deploy computational power to the network edges to meet the needs of applications that demand very high bandwidth and low latency, as well as supporting resource-constrained nodes reachable only over unreliable network connections [1]–[4].”
Adaptive Task Allocation for Asynchronous Federated Mobile Edge Learning (2019, methodology): “This paradigm of edge processing has been supported by the latest works in literature about Mobile Edge Computing (MEC) and Hierarchical-MEC (H-MEC) [3]–[6].”
In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning (2018, methodology): “Several pioneer works have been proposed and realize quite good results in their assuming settings based on convex optimization, game theory and so on [3] [4] .”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, neutral): “[47] A survey on mobile edge computing.”
Mobile Edge Intelligence and Computing for the Internet of Vehicles (2019, background): “different offloading models can be considered, namely, binary and partial offloading [31].”
The Roadmap to 6G - AI Empowered Wireless Networks (2019, background): “…stringent computation, bandwidth, storage, power and privacy constraints on individual devices, increasing research interests are moving towards leveraging the dispersed computing resources across the cloud, network edge and end-devices of 6G networks through the lens of mobile edge computing [14].”
Learning-Based Computation Offloading for IoT Devices With Energy Harvesting (2017, background): “With limited computation, energy and memory resources, Internet of Things (IoT) devices such as sensors, cameras and wearable devices have a computation bottleneck to limit the support for advanced applications such as interactive online gaming and facial recognition applications [1], [2].”
Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT (2019, background): “e, locating MEC servers at a wireless access point (AP) or base station), it can efficiently reduce the delay and save the computation resource at these devices by the way of computation task offloading [3] [4].”
Survey on Multi-Access Edge Computing for Internet of Things Realization (2018, background): “out in [32], the task models for partial offloading can be represented by task-call graphs with sequential, parallel, and general dependencies.”
Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning (2018, background): “In contrast to cloud/fog computing [3], [4], mobile-edge computing (MEC) is envisioned as a promising paradigm, which provides computing capabilities within the radio access networks (RANs) in close proximity to mobile users (MUs) [5], [6].”
Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks (2017, background): “Recently, several works have focused on exploiting the benefits of computation offloading in MEC network [24].”
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges (2021, background): “Recently, edge computing (EC) approaches [10, 88] have attempted to address the “latency vs.”
A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms (2019, background): “The authors of Reference [96] survey the state-of-the-art MEC studies, focusing on the joint management of communication and computation resources.”
Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems (2019, background): “manage radio and computing resources to execute tasks, which has been widely investigated recently, see surveys [4]–[6] and technical papers [7]–[9].”
Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing (2018, methodology): “Following the data-partition model [5], we assume that each task can be computed locally, i.”
Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services (2023, neutral): “2) Sustainability in Mobile AIGC Networks: In mobile AIGC networks, the pre-training, fine-tuning, and inference of generative AI models typically consume a substantial amount of computing and networking resources [26].”