Abstract
One of the most critical issues of Wireless Sensor Networks (WSNs) is the deployment of a limited number of sensors in order to achieve maximum coverage on a terrain. The optimal sensor deployment which enables one to minimize the consumed energy, communication time and manpower for the maintenance of the network has attracted interest with the increased number of studies conducted on the subject in the last decade. Most of the studies in the literature today are proposed for two dimensional (2D) surfaces; however, real world sensor deployments often arise on three dimensional (3D) environments. In this paper, a guided wavelet transform (WT) based deployment strategy (WTDS) for 3D terrains, in which the sensor movements are carried out within the mutation phase of the genetic algorithms (GAs) is proposed. The proposed algorithm aims to maximize the Quality of Coverage (QoC) of a WSN via deploying a limited number of sensors on a 3D surface by utilizing a probabilistic sensing model and the Bresenham's line of sight (LOS) algorithm. In addition, the method followed in this paper is novel to the literature and the performance of the proposed algorithm is compared with the Delaunay Triangulation (DT) method as well as a standard genetic algorithm based method and the results reveal that the proposed method is a more powerful and more successful method for sensor deployment on 3D terrains.
Highlights
Optimal sensor deployment on 3D terrains is the problem of placing the sensors at the most appropriate spots in order to maximize coverage of a wireless sensor network (WSN)
In order to maximize the quality of coverage within a given terrain and a constant number of sensors, numerous versions of genetic algorithms (GAs) are examined in order to find optimal sensor deployment schemes
In order to evaluate the performance of the proposed mutation method, it is compared with a simple deployment strategy (SDS)
Summary
Optimal sensor deployment on 3D terrains is the problem of placing the sensors at the most appropriate spots in order to maximize coverage of a wireless sensor network (WSN). There are many environmental challenges which affect the performance of a WSN; that is, the limited number of sensors, and dependency in the determination of the best location of each sensor on the terrain and sensor characteristics. Measure on a terrain, robust deployment strategies have to be taken into consideration, in the sense that, optimal sensor emplacement enables us to minimize the manpower, time and the number of sensors. Sensors are essentially battery operated and consume energy during the transmission, reception and sensing phases. In order to maximize the network lifetime, reliable methods for sensor deployment to reduce the energy consumption in WSN is a vital issue
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