Multi-UAV-Enabled Load-Balance Mobile-Edge Computing for IoT Networks

Unmanned aerial vehicles (UAVs) have been widely used to provide enhanced information coverage as well as relay services for ground Internet-of-Things (IoT) networks. Considering the substantially limited processing capability, the IoT devices may not be able to tackle with heavy computing tasks. In...

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Bibliographic Details
Published in:IEEE internet of things journal 2020-08, Vol.7 (8), p.6898-6908
Main Authors: Yang, Lei, Yao, Haipeng, Wang, Jingjing, Jiang, Chunxiao, Benslimane, Abderrahim, Liu, Yunjie
Format: Article
Language:English
Subjects:
Algorithms
Computational modeling
Computer simulation
Edge computing
Evolutionary computation
Internet of Things
Load balance
Load balancing
Machine learning
Mobile computing
mobile-edge computing (MEC)
multi-UAV deployment
Nodes
Operations research
Quality of service
Relays
Scheduling
Task analysis
Task scheduling
Unmanned aerial vehicles
unmanned aerial vehicles (UAVs)
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Summary:Unmanned aerial vehicles (UAVs) have been widely used to provide enhanced information coverage as well as relay services for ground Internet-of-Things (IoT) networks. Considering the substantially limited processing capability, the IoT devices may not be able to tackle with heavy computing tasks. In this article, a multi-UAV-aided mobile-edge computing (MEC) system is constructed, where multiple UAVs act as MEC nodes in order to provide computing offloading services for ground IoT nodes which have limited local computing capabilities. For the sake of balancing the load for UAVs, the differential evolution (DE)-based multi-UAV deployment mechanism is proposed, where we model the access problem as a generalized assignment problem (GAP), which is then solved by a near-optimal solution algorithm. Based on this, we are capable of achieving the load balance of these drones while guaranteeing the coverage constraint and satisfying the quality of service (QoS) of IoT nodes. Furthermore, a deep reinforcement learning (DRL) algorithm is conceived for the task scheduling in a certain UAV, which improves the efficiency of the task execution in each UAV. Finally, sufficient simulation results show the feasibility and superiority of our proposed load-balance-oriented UAV deployment scheme as well as the task scheduling algorithm.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2020.2971645