Abstract
When natural disasters strike, users in the disaster area may be isolated and unable to transmit disaster information to the outside due to the damage of communication facilities. Unmanned aerial vehicles can be exploited as mobile edge servers to provide emergency service for ground users due to its mobility and flexibility. In this paper, a robust UAV-aided wireless-powered mobile edge computing (MEC) system in post disaster areas is proposed, where the UAV provides charging and computing service for users in the disaster area. Considering the estimation error of users’ locations, our target is to maximize the energy acquisition of each user by jointly optimizing the computing offloading process and the UAV trajectory. Due to the strongly coupled connectionbetween optimization variables and the non-convex nature for trajectory optimization, the problem is difficult to solve. Furthermore, the semi-infinity of the users’ possible location makes the problem even more intractable. To tackle these difficulties, we ignore the estimation error of users’ location firstly, and propose an iterative algorithm by using Lagrange dual method and successive convex approximation (SCA) technology. Then, we propose a cutting-set method to deal with the uncertainty of users’ location. In this method, we degrade the influence of location uncertainty by alternating between optimization step and pessimization step. Finally, simulation results show that the proposed robust algorithm can effectively improve the user energy acquisition.
Highlights
Natural disasters, such as earthquake, flood, and typhoon, often cause huge and unpredictable losses to human lives and properties [1,2,3]
A unmanned aerial vehicles (UAVs) equipped with mobile edge computing (MEC) device and RF transmitter flies to the post-disaster area to provide computing and charging services for ground users
Considering that the distances dn[t] between the UAV and users are larger than the estimation error ∆qn, in the optimization process, we discretize the potential locations of the nth user into equal spacing grids-based worst-case locations with the resolution of π
Summary
Natural disasters, such as earthquake, flood, and typhoon, often cause huge and unpredictable losses to human lives and properties [1,2,3]. Motivated by the requirements of a UAV-enabled wireless platform in a post-disaster area, we consider a robust UAV-enabled wireless-powered MEC system in this paper. In this system, a UAV equipped with MEC device and RF transmitter flies to the post-disaster area to provide computing and charging services for ground users. We propose a UAV-enabled wireless-powered MEC system in a post-disaster area, while the imperfect location of users is considered. We propose a joint resource allocation and trajectory planning algorithm under known users’ location to solve the strong coupling between optimization variables. We propose a robust cutting-set method to degrade the influence of worst-case location of users on optimization.
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