Power optimization in UAV-based wireless power transmission and collaborative MEC IoT networks

Internet of Things (IoT) devices will generate massive fragmented data that requires real-time computing and intelligent analysis, which makes it facing the challenge of insufficient computing resources and limited battery energy. With the assistance of mobile edge computing (MEC), IoT devices can offload computing tasks to the MEC server to reduce their computing pressure. Cooperation between devices and wireless power transmission technology (WPT) can be used to solve the problem of limited battery energy. Moreover, the use of unmanned aerial vehicles (UAVs) in the WPT-MEC system can improve signal coverage and energy collection efficiency. In this paper, we propose an UAV-based wireless power transmission and collaborative MEC scheme for IoT networks, in which IoT devices utilize different subcarriers to transmit the information. We aim to minimize UAV’s transmitting power via optimizing the transmitting power of IoT devices with the constraints of delay and offloading task size. The performance of the proposed scheme is verified by simulation results.

[1]  Xu Chen,et al.  In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning , 2018, IEEE Network.

[2]  Lóránt Farkas,et al.  Multi-user computation offloading as Multiple Knapsack Problem for 5G Mobile Edge Computing , 2016, 2016 European Conference on Networks and Communications (EuCNC).

[3]  Geoffrey Ye Li,et al.  Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems , 2019, IEEE Internet of Things Journal.

[4]  Nguyen Ti Ti,et al.  Joint Resource Allocation, Computation Offloading, and Path Planning for UAV Based Hierarchical Fog-Cloud Mobile Systems , 2018, 2018 IEEE Seventh International Conference on Communications and Electronics (ICCE).

[5]  Hao Zhang,et al.  Cooperative Integration of RF Energy Harvesting and Dedicated WPT for Wireless Sensor Networks , 2019, IEEE Microwave and Wireless Components Letters.

[6]  Mohamed Kamoun,et al.  Joint resource allocation and offloading strategies in cloud enabled cellular networks , 2015, 2015 IEEE International Conference on Communications (ICC).

[7]  H. Vincent Poor,et al.  Fundamentals of Wireless Information and Power Transfer: From RF Energy Harvester Models to Signal and System Designs , 2018, IEEE Journal on Selected Areas in Communications.

[8]  Chee Yen Leow,et al.  An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges , 2018, IEEE Internet of Things Journal.

[9]  Mu Zhou,et al.  An Information-Theoretic View of WLAN Localization Error Bound in GPS-Denied Environment , 2019, IEEE Transactions on Vehicular Technology.

[10]  Khaled Ben Letaief,et al.  Power-Delay Tradeoff in Multi-User Mobile-Edge Computing Systems , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[11]  Songtao Guo,et al.  Energy-Efficient Cooperative Resource Allocation in Wireless Powered Mobile Edge Computing , 2019, IEEE Internet of Things Journal.

[12]  Kai-Kit Wong,et al.  Wireless Powered Cooperation-Assisted Mobile Edge Computing , 2018, IEEE Transactions on Wireless Communications.

[13]  Chengwen Xing,et al.  Green UAV communications for 6G: A survey , 2021 .

[14]  Hao Song,et al.  Data-Driven Deep Learning for Signal Classification in Industrial Cognitive Radio Networks , 2021, IEEE Transactions on Industrial Informatics.

[15]  Weidang Lu,et al.  Energy Efficiency Optimization in SWIPT Enabled WSNs for Smart Agriculture , 2021, IEEE Transactions on Industrial Informatics.

[16]  Hao Song,et al.  Intelligent Signal Classification in Industrial Distributed Wireless Sensor Networks-Based IIoT , 2020 .