scholarly journals Power-Efficient Deployment of a UAV for Emergency Indoor Wireless Coverage

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 73200-73209 ◽  
Author(s):  
Jian Cui ◽  
Hazim Shakhatreh ◽  
Bo Hu ◽  
Shanzhi Chen ◽  
Chuanan Wang
Keyword(s):  
Power Efficient ◽  
Indoor Wireless ◽  
Wireless Coverage

scholarly journals Power-Efficient Wireless Coverage Using Minimum Number of UAVs

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 223
Author(s):  
Ahmad Sawalmeh ◽  
Noor Shamsiah Othman ◽  
Guanxiong Liu ◽  
Abdallah Khreishah ◽  
Ali Alenezi ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Circle Packing ◽  
Three Dimensional ◽  
Base Stations ◽  
Transmit Power ◽  
Power Efficient ◽  
User Coverage ◽  
Minimum Number ◽  
Wireless Coverage ◽  
Placement Algorithms

Unmanned aerial vehicles (UAVs) can be deployed as backup aerial base stations due to cellular outage either during or post natural disaster. In this paper, an approach involving multi-UAV three-dimensional (3D) deployment with power-efficient planning was proposed with the objective of minimizing the number of UAVs used to provide wireless coverage to all outdoor and indoor users that minimizes the required UAV transmit power and satisfies users’ required data rate. More specifically, the proposed algorithm iteratively invoked a clustering algorithm and an efficient UAV 3D placement algorithm, which aimed for maximum wireless coverage using the minimum number of UAVs while minimizing the required UAV transmit power. Two scenarios where users are uniformly and non-uniformly distributed were considered. The proposed algorithm that employed a Particle Swarm Optimization (PSO)-based clustering algorithm resulted in a lower number of UAVs needed to serve all users compared with that when a K-means clustering algorithm was employed. Furthermore, the proposed algorithm that iteratively invoked a PSO-based clustering algorithm and PSO-based efficient UAV 3D placement algorithms reduced the execution time by a factor of ≈1/17 and ≈1/79, respectively, compared to that when the Genetic Algorithm (GA)-based and Artificial Bees Colony (ABC)-based efficient UAV 3D placement algorithms were employed. For the uniform distribution scenario, it was observed that the proposed algorithm required six UAVs to ensure 100% user coverage, whilst the benchmarker algorithm that utilized Circle Packing Theory (CPT) required five UAVs but at the expense of 67% of coverage density.

Sign in / Sign up

Export Citation Format

Share Document