Optimizing Multi-UAV Deployment in 3D Space to Minimize Task Completion Time in UAV-Enabled Mobile Edge Computing Systems

Mobile Edge Computing (MEC) is an emerging paradigm that enables computation and storage resources to be brought closer to the end-users in a wireless network. Unmanned Aerial Vehicles (UAVs) have been introduced in MEC systems to enhance network connectivity, coverage, and capacity. In this paper, we propose a novel approach to optimize the deployment of multiple UAVs in a three-dimensional space to minimize the task completion time in UAV-enabled MEC systems.

We formulate the problem as a mixed-integer linear program (MILP), where the objective is to minimize the task completion time subject to resource constraints and safety regulations. The MILP considers the UAVs' positions, velocities, and orientations, as well as the task locations and requirements. We also consider the communication delay between the UAVs and the MEC servers and the energy consumption of the UAVs.

To solve the MILP, we propose a two-stage algorithm that first generates an initial solution using a heuristic algorithm and then refines it using a local search algorithm. The heuristic algorithm generates a set of candidate solutions by selecting the UAVs' positions randomly and then optimizing the orientations and velocities to minimize the task completion time. The local search algorithm improves the solutions by iteratively moving the UAVs to nearby locations and checking if the task completion time is reduced.

We evaluate the proposed approach using a simulation environment that mimics a real-world scenario. The results show that the proposed approach can significantly reduce the task completion time compared to the baseline approaches. Moreover, the proposed approach can adapt to the changes in the task requirements and the network conditions.

In conclusion, the proposed approach can optimize the deployment of multiple UAVs in a three-dimensional space to minimize the task completion time in UAV-enabled MEC systems. The approach can be used in various applications, such as disaster response, surveillance, and precision agriculture.