ArticleSensors (Basel, Switzerland)2019
Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Learning-Guided Meta-Heuristic Approach for Task Offloading in Four-Tier IoT Networks: A Hybrid UCB-ACO Algorithm.Biomimetics (Basel, Switzerland) · 2026Article
- Multi-Function Computation over a Directed Acyclic Network.Entropy (Basel, Switzerland) · 2025Article
- Edge Server Selection with Round-Robin-Based Task Processing in Multiserver Mobile Edge Computing.Sensors (Basel, Switzerland) · 2025Article
- Energy efficient task scheduling for heterogeneous multicore processors in edge computing.Scientific reports · 2025Article
- Deep Reinforcement Learning-Enabled Computation Offloading: A Novel Framework to Energy Optimization and Security-Aware in Vehicular Edge-Cloud Computing Networks.Sensors (Basel, Switzerland) · 2025Article
- Research on Cloud-Edge-End Collaborative Computing Offloading Strategy in the Internet of Vehicles Based on the M-TSA Algorithm.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning-Based Dynamic Computation Task Offloading for Mobile Edge Computing Networks.Sensors (Basel, Switzerland) · 2022Article
- Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing.Sensors (Basel, Switzerland) · 2021Article
- Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server. Considering different real-time computation tasks at different WDs, every task is decided to be processed locally at its WD or to be offloaded to and processed at one of the edge servers or the cloud server. In this paper, we investigate low-complexity computation offloading policies to guarantee quality of service of the MEC network and to minimize WDs' energy consumption. Specifically, both a linear programing relaxation-based (LR-based) algorithm and a distributed deep learning-based offloading (DDLO) algorithm are independently studied for MEC networks. We further propose a heterogeneous DDLO to achieve better convergence performance than DDLO. Extensive numerical results show that the DDLO algorithms guarantee better performance than the LR-based algorithm. Furthermore, the DDLO algorithm generates an offloading decision in less than 1 millisecond, which is several orders faster than the LR-based algorithm.
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Registered trials
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