Affiliation:
1. State Grid Jilin Province Electric Power Company Limited Information Communication Company, Changchun 130000, China
2. School of Telecommunications Engineering, Xidian University, Xi’an 710071, China
Abstract
The rapid development of wireless network technology has led to the coexistence of various heterogeneous wireless networks (HWNs). To ensure that users enjoy normal and diversified services, research on vertical switching technology has become an inevitable trend. However, most current vertical switching algorithms only consider static situations or single services, which are not suitable for power grid scenarios. This paper studies the vertical switching problem of wireless heterogeneous networks for unmanned aerial vehicles (UAVs) performing inspection tasks in power grid scenarios. In this model, a UAV for power grid inspection needs to plan its flight trajectory, avoid obstacles, and find the optimal trajectory to reach each inspection point. Throughout the UAV inspection process, we must ensure the quality of communication services for the UAV. The UAV dynamically selects different networks for access at different locations, presenting a dynamic network selection and vertical switching problem. This paper proposes a method that combines trajectory planning and network selection, which first utilizes the A-star algorithm to obtain suitable trajectories, and then evaluates and judges networks based on the Fuzzy Analytic Hierarchy Process (FAHP) to determine the most appropriate network. It is worth noting that this paper considers three service requirements and seven network attributes under three types of heterogeneous wireless networks. Numerical results show that this method can better meet the requirements of UAV inspection tasks and reduce the number of switches, thus addressing the issue of terminal vertical switches in power grid scenarios.
Funder
State Grid Jilin Electric Power Company
Reference42 articles.
1. Design of Terminal Communication Access Architecture for Smart Power Distribution and Utilization Based on Integration of Multiple Technologies;Li;Dianli Xitong Zidonghua/Automation Electr. Power Syst.,2018
2. NOMA-Assisted Secure Offloading for Vehicular Edge Computing Networks with Asynchronous Deep Reinforcement Learning;Ju;IEEE Trans. Intell. Transp. Syst.,2024
3. Reliability–Security Tradeoff Analysis in mmWave Ad Hoc–based CPS;Ju;ACM Trans. Sen. Netw.,2024
4. Ju, Y., Gao, Z., Wang, H., Liu, L., Pei, Q., Dong, M., Mumtaz, S., and Leung, V.C.M. (2024). Energy-Efficient Cooperative Secure Communications in mmWave Vehicular Networks Using Deep Recurrent Reinforcement Learning. IEEE Trans. Intell. Transp. Syst., 1–16.
5. Heterogeneous Dynamic Graph Convolutional Networks for Enhanced Spatiotemporal Flood Forecasting by Remote Sensing;Jiang;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2024