Joint Mobility, Communication and Computation Optimization for UAVs in Air-Ground Cooperative Networks

Abstract

Xuemin (2021) Joint mobility, communication and computation optimization for UAVs in air-ground cooperative networks. IEEE Transactions on Vehicular Technology.

Weighted Energy-Efficiency Maximization for a UAV-Assisted Multiplatoon Mobile-Edge Computing System

“…The simulation results showed that the proposed method is significantly better than the benchmark methods for the above indicators. Some of the simulation parameters refer to previous works [4] and [13]. The detailed simulation parameters are provided in the next…”

“…It is widely assumed that in different time slots [4], [13], the vehicles movement can be simplified to a uniform acceleration movement. Hence, its motion equation can be expressed as follows:…”

“…Furthermore, considering the intelligent autonomous driving assistance of a platoon, there is a contradiction between the requirements of the computation-intensive, delay-sensitive tasks and limited computation resources of vehicles. Applications ranging from advanced driving assistance services such as multi-source heterogeneous data fusion, perception of the surrounding traffic conditions, and driving decision-making to in-car entertainment services like virtual reality video games urgently need assistance from external computing platforms [2]- [4]. Therefore, advanced communication and auxiliary computing technology will greatly promote the realization of intelligent autonomous transport systems.…”

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

“…The authors in [184] proposed a game theory based solution for offloading the UAVs task while ensuring a tradeoff between the delay, cost, and energy consumption. Recently, Zhou et al [185] proposed a method to jointly optimize the mobility, communication, and computation for a UAV in MEC-assisted air-ground cooperative network to maximize the UAV's energy efficiency. The work in [186] aimed at minimizing the execution time required for UAVs to complete the offloaded tasks by optimizing the UAV's 3D location.…”

A Dynamic Clustering Mechanism With Load-Balancing for Flying Ad Hoc Networks

“…Mindful of the significant envisioned role of UAV-FANET in future generation mobile networks and beyond, such as seamless and flexible provision of emergency communication coverage and information services [9], we find in the literature, barely few approaches that attempted to efficiently integrate clustering and optimization techniques to improve routing efficiency in FANETs. In one of such approaches, L. Ye et al [3] propose a mechanism able to reduce endto-end delay and increased throughput, but at the expense of high communication overheads.…”