Abstract
This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where multiple UAVs are used to serve mobile users. We aim to minimize the overall energy consumption of the system by planning the trajectories of UAVs. To plan the trajectories of UAVs, we need to consider the deployment of hovering points (HPs) of UAVs, their association with UAVs, and their order for each UAV. Therefore, the problem is very complicated, as it is non-convex, nonlinear, NP-hard, and mixed-integer. To solve the problem, this paper proposed an evolutionary trajectory planning algorithm (ETPA), which comprises four phases. In the first phase, a variable-length GA is adopted to update the deployments of HPs for UAVs. Accordingly, redundant HPs are removed by the remove operator. Subsequently, a differential evolution clustering algorithm is adopted to cluster HPs into different clusters without knowing the number of HPs in advance. Finally, a GA is proposed to construct the order of HPs for UAVs. The experimental results on a set of eight instances show that the proposed ETPA outperforms other compared algorithms in terms of the energy consumption of the system.
Highlights
(UAV)-assisted mobile edge computing (MEC) system, where cannot provide timely services during a natural disaster as the multiple UAVs are used to serve mobile users (MUs)
Eight instances show that the proposed evolutionary trajectory planning algorithm (ETPA) outperforms other Recently, due to the above-mentioned advantages, UAVs compared algorithms in terms of the energy consumption of the have been extensively used in various fields, such as wireless system
If MUs decide to offload to the UAVs, the data rate can be Where the objective function is the sum of hovering energy 195 given as rij[t] = Blog2
Summary
With the development of mobile communication systems, 25 a huge number of resource-intensive and latency-sensitive ap plications are emerging, such as virtual reality, online gaming, and so on. A new multi-UAV-assisted MEC system is proposed and Fig. 1: Collection Framework of multi-UAV-assisted MEC formulated to minimize the energy consumption of the system system by considering the deployment including the number and locations of hovering points (HPs), the number of UAVs, and their association with HPs, and the order otherwise. Ti[t] ≤ T max, ||qj[t + 1] − qj[t]||2 ≤ Sm2 ax, t = 0, ..., N, piue ≤ P max, Xmin ≤ Xj[t] ≤ Xmax, ∀j ∈ M, t ∈ Tj, Ymin ≤ Yj[t] ≤ Ymax, ∀j ∈ M, t ∈ Tj. If MUs decide to offload to the UAVs, the data rate can be Where the objective function is the sum of hovering energy 195 given as rij[t] = Blog. PROPOSED ALGORITHM which is constrained by piue ≤ P max
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