A dominance-based stepwise approach for sensor placement optimization |
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| Affiliation: | 1. Laboratoire de communication et d’intégration de la microĺectronique, École de Technologie Supérieure, University of Quebec, 1100 Notre-Dame West Street, Montreal, Quebec H3C 1K3, Canada;2. SADC2, Defence Research and Development Canada, 2459, boulevard Pie-XI Nord, Val-Bélair, Quebec G3J 1X5, Canada;3. Laboratoire d’imagerie, de vision et d’intelligence artificielle, École de Technologie Supérieure, University of Quebec, 1100 Notre-Dame West Street, Montreal, Quebec H3C 1K3, Canada;4. LACIME, École de Technologie Supérieure, University of Quebec, 1100 Notre-Dame West Street, Montreal, Quebec H3C 1K3, Canada;1. Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapore, West Bengal 721102, India;2. Department of Mathematics, Sidho Kanho Birsha University, Purulia, West Bengal 723101, India;1. Interdisciplinary Faculty of Science and Engineering, Shimane University, 1060 Nishikawatsu-cho, Matsue, Shimane 690-8504, Japan;2. Faculty of Engineering, Yamaguchi University, 2-16-1 Tokiwadai, Ube, Yamaguchi 755-8611, Japan;1. Department of Information Technologies, Işık University, Istanbul, Turkey;2. Informatics Institute, Middle East Technical University, Ankara, Turkey |
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| Abstract: | 
A Wireless Sensor Network (WSN) usually consists of numerous wireless devices deployed in a region of interest, each of which is capable of collecting and processing environmental information and communicating with neighboring devices. The problem of sensor placement becomes non trivial when we consider environmental factors such as terrain elevations. In this paper, we differentiate a stepwise optimization approach from a generic optimization approach, and show that the former is better suited for sensor placement optimization. Following a stepwise optimization approach, we propose a Crowd-Out Dominance Search (CODS), which makes use of terrain information and intersensor relationship information to facilitate the optimization. Finally, we investigate the effect of terrain irregularity on optimization algorithm performances, and show that the proposed method demonstrates better resistance to terrain complexity than other optimization methods. |
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| Keywords: | Wireless Sensor Networks Sensor placement Optimization Coverage Genetic Algorithm Simulated Annealing Evolutionary Algorithm Wireless communications |
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