Abstract
In this paper, the dynamic deployment of a single UAV as an aerial base station in providing wireless coverage for mobile outdoor and indoor users is studied. The problem of finding the efficient UAV trajectory is formulated with the objective to minimize the required UAV transmit power that satisfies the users' minimum data rate. The proposed solution to the problem considers the users' movement in a search and rescue (SAR) operation. More specifically, the outdoor rescue team members are considered to move in a group with the reference point group mobility (RPGM) model. Whilst, the indoor rescue team members are considered to move individually and in a group with random waypoint and RPGM models, respectively. The efficient UAV trajectory is developed using two approaches, namely, heuristic and optimal approaches. The employment of the heuristic approach, namely particle swarm optimization (PSO) and genetics algorithm (GA), to find the efficient UAV trajectory reduced the execution time by a factor of ∼eq 1/60 and ∼eq 1/9 compared to that when using the optimal approach of brute-force search space algorithm. Furthermore, the use of PSO algorithm reduced the execution time by a factor of ∼eq 1/7 compared to that when the GA algorithm is invoked.The performance of the dynamic UAV deployment also outperformed the static UAV deployment in terms of the required transmit power. More specifically, the dynamic UAV deployment required less total transmit power by a factor of about 1/2 compared to the static UAV deployment, in providing wireless coverage for rescue team to perform SAR operation within a rectangular sub-region.
Original language | English (US) |
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Article number | 8819951 |
Pages (from-to) | 126376-126390 |
Number of pages | 15 |
Journal | IEEE Access |
Volume | 7 |
DOIs | |
State | Published - 2019 |
All Science Journal Classification (ASJC) codes
- General Computer Science
- General Materials Science
- General Engineering
- Electrical and Electronic Engineering
Keywords
- Genetic algorithm
- Particle swarm optimization
- Random waypoint
- Reference point group mobility model
- Unmanned aerial vehicles