Energy-efficient trajectory planning for a multi-UAV-assisted mobile edge computing system

Abstract

We study a mobile edge computing system assisted by multiple unmanned aerial vehicles (UAVs), where the UAVs act as edge servers to provide computing services for Internet of Things devices. Our goal is to minimize the energy consumption of this system by planning the trajectories of UAVs. This problem is difficult to address because when planning the trajectories, we need to consider not only the order of stop points (SPs), but also their deployment (including the number and locations) and the association between UAVs and SPs. To tackle this problem, we present an energy-efficient trajectory planning algorithm (TPA) which comprises three phases. In the first phase, a differential evolution algorithm with a variable population size is adopted to update the number and locations of SPs at the same time. In the second phase, the k-means clustering algorithm is employed to group the given SPs into a set of clusters, where the number of clusters is equal to that of UAVs and each cluster contains all SPs visited by the same UAV. In the third phase, to quickly generate the trajectories of UAVs, we propose a low-complexity greedy method to construct the order of SPs in each cluster. Compared with other algorithms, the effectiveness of TPA is verified on a set of instances at different scales.

A Novel Genetic Trajectory Planning Algorithm With Variable Population Size for Multi-UAV-Assisted Mobile Edge Computing System

“…The parameter settings of the proposed multi-UAV-assisted MEC system are presented in Table 1 [28]. We have used the same parameters for all compared algorithms used in this paper for a fair comparison.…”

“…We have used the same parameters for all compared algorithms used in this paper for a fair comparison. The parameter settings were kept the same as used in other articles such as: [28]. We have tested ten instances with up to 200 IoTDs to evaluate the performance of GTPA-VP.…”

“…Since most of the existing solutions are designed for different systems and problems, therefore, are not applicable to our proposed system. However, we have compared our algorithm with some existing approaches such as TPA [28], etc. In addition, we have compared each phase of the proposed GTPA-VP with an existing method to show its effectiveness.…”

“…In order to show the effectiveness of GTPA-VP, we designed algorithms called Kmeans-Greedy, GAC-Greedy, and TPA [28]. Kmeans-Greedy uses K-means algorithm [39] for clustering and a greedy algorithm for the order of SPs.…”

“…In the outer loop, they have adopted Dinkelbach method to find the optimal computation efficiency, while in the inner loop, they proposed a joint optimization algorithm for user association, resource allocation, and trajectory scheduling. Huang et al [28] studied a multi-UAV-assisted MEC system, where the UAVs act as edge servers to provide computing services for IoTDs. They presented an energy-efficient trajectory planning algorithm (TPA) to minimize the energy consumption of the system.…”