Extended Genetic Algorithm for Unmanned Aerial Vehicle Collaboration in Three-Layer Mobile Edge Computing

Abstract

In recent years, low-latency tasks such as real-time communication, virtual reality, and augmented reality have higher latency requirements. Since users have relatively weak computing power, the past practice was to offload low-latency tasks to base stations (BSs) with more powerful computing power for execution, which can reduce computing latency in real-time communication. Duetothe inflexible deployment of BSs and the uneven distribution of computing resources, users often find it difficult to meet the needs of low-latency tasks in places that lack BSs, so unmanned aerial vehicles (UAVs) are needed to alleviate this problem. UAVs can serve as communication relays between users and BSs and provide computing services directly to users. However, when there are few BSs, UAVs may experience load imbalance, that is, some UAVs are overloaded while other UAVs are idle. To solve this problem, we propose a UAV collaborative three-layer mobile edge computing (MEC) system model and describe the problem as a mixed integer nonlinear programming (MINLP) problem. Then, we propose an extended genetic algorithm for UAV collaboration in three-layer MEC task allocation and optimization (EGAC-3MEC) where the three-layer structure includes users, UAVs, and BSs. The proposed EGAC- 3MEC achieves reasonable allocation of tasks and resources through offloading and resource allocation decisions, reduces system delay and energy consumption as much as possible, and prioritizes the delay of high-priority users. Experimental results show that the algorithm in this paper converges quickly when various parameters change, achieves better performance than the baseline algorithms when the user task size is different, and moreover, the necessity of UAV collaboration is proven.