Computational efficiency maximization for UAV-assisted MEC network with energy harvesting in disaster scenarios

Abstract

Wireless networks are expected to provide unlimited connectivity to an increasing number of heterogeneous devices. Future wireless networks (sixth-generation (6G)) will accomplish this in three-dimensional (3D) space by combining terrestrial and aerial networks. However, effective resource optimization and standardization in future wireless networks are challenging because of massive resource-constrained devices, diverse quality-of-service (QoS) requirements, and a high density of heterogeneous devices. Recently, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks are considered a potential candidate to provide effective and efficient solutions for disaster management in terms of disaster monitoring, forecasting, in-time response, and situation awareness. However, the limited size of end-user devices comes with the limitation of battery lives and computational capacities. Therefore, offloading, energy consumption and computational efficiency are significant challenges for uninterrupted communication in UAV-assisted MEC networks. In this thesis, we consider a UAV-assisted MEC network with energy harvesting (EH). To achieve this, we mathematically formulate a mixed integer non-linear programming problem to maximize the computational efficiency of UAV-assisted MEC networks with EH under disaster situations. A power splitting architecture splits the source power for communication and EH. We jointly optimize user association, the transmission power of UE, task offloading time, and UAV’s optimal location. To solve this optimization problem, we divide it into three stages. In the first stage, we adopt k-means clustering to determine the optimal locations of the UAVs. In the second stage, we determine user association. In the third stage, we determine the optimal power of UE and offloading time using the optimal UAV location from the first stage and the user association indicator from the second stage, followed by linearization and the use of interior-point method to solve the resulting linear optimization problem. Simulation results for offloading, no-offloading, offloading with EH, and no-offloading no-EH scenarios are presented with a varying number of UAVs and UEs. The results show the proposed EH solution’s effectiveness in offloading scenarios compared to no-offloading scenarios in terms of computational efficiency, bits computed, and energy consumption