Optimal Wireless Sensor Networks Deployment in 3-Dimensional Terrains using Hybrid Population Based Algorithm
| dc.contributor.advisor | Negash, Yalemzewud (PhD) | |
| dc.contributor.author | Enyew, Yiwab | |
| dc.date.accessioned | 2018-06-27T05:51:32Z | |
| dc.date.accessioned | 2023-11-28T14:09:07Z | |
| dc.date.available | 2018-06-27T05:51:32Z | |
| dc.date.available | 2023-11-28T14:09:07Z | |
| dc.date.issued | 2014-02 | |
| dc.description.abstract | Wireless sensor networks (WSNs) are composed of cooperating sensor nodes that can perceive the environment to monitor physical phenomena and events of interest. Sensor deployment is a fundamental issue in a WSNs to maximize coverage and quality of service with limited number of sensor nodes. In order to maximize area coverage, sensors need to be placed in a position such that the sensing capability of the network reach at high quality. Coverage is one of the main problems in WSNs deployment. Previous research works on sensor deployment mainly focused on Two Dimensional (2D) plane or in Three Dimensional (3D) volume coverage. But now, these studies on sensor deployment extended to 3D surfaces or terrain, to achieve the highest overall sensing quality. In our thesis, we worked to develop an optimal WSNs deployment on 3D surfaces to maximize area coverage under constrained number of nodes. Researchers have used different methods and algorithms to make sensor deployment. Populationbased optimization algorithms find near-optimal solutions to the difficult optimization inspired by natural probabilistic evolution. In our research work, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are selected to form Hybrid Algorithm (HA) to find optimal locations of sensors based on a fitness function. We have selected the two algorithms to exploit the best features of the algorithms in combination. We have used two typical surfaces, rough and smooth, to compare the results of the GA, PSO and HA in the optimal deployment of sensors. The fitness function used in the algorithms is calculated based on coverage of all sensors in the region of interest (ROI). A simulating program for both surface types and all the three algorithms has been developed using MATLAB. In all the three PSO, GA and HA evaluations, we found that the HA has exceeded PSO with a percentage of 26.12% and GA with 1.58% on rough surface. Similarly when we also compared the results of these algorithms on smooth surface, HA has exceeded PSO with a percentage of 22.24% and GA with 3.42%. Next to the HA, GA has a very good performance than PSO. Key words: Particle Swarm Optimization, Genetic Algorithm, WSNs, 3-D terrain, Sensor Deployment. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/3807 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Particle Swarm Optimization | en_US |
| dc.subject | Genetic Algorithm | en_US |
| dc.subject | WSNs | en_US |
| dc.subject | 3-D terrain | en_US |
| dc.subject | Sensor Deployment | en_US |
| dc.title | Optimal Wireless Sensor Networks Deployment in 3-Dimensional Terrains using Hybrid Population Based Algorithm | en_US |
| dc.type | Thesis | en_US |