期刊
IEEE INTERNET OF THINGS JOURNAL
卷 7, 期 8, 页码 6898-6908出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.2971645
关键词
Task analysis; Internet of Things; Relays; Quality of service; Edge computing; Computational modeling; Scheduling; Load balance; mobile-edge computing (MEC); multi-UAV deployment; task scheduling; unmanned aerial vehicles (UAVs)
资金
- National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data
- National Key Research and Development Plan [018YFB1800805]
- Future Intelligent Networking and Intelligent Transportation Joint Laboratory (BUPT-CTTIC)
- Shuimu Tsinghua Scholar Program
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.
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