Evolutionary approach on connectivity-based sensor network localization |
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| Affiliation: | 1. School of Automation, Beijing Institute of Technology, Beijing, China;2. Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong;1. College of Communications Engineering, PLA University of Science and Technology, Nanjing, Jiangsu 210007, China;2. Business School, Sichuan University, Chengdu 610064, Sichuan, China;1. School of Electrical and Electronic Engineering, Yonsei University, Sinchon-dong, Seodaemun-gu, Seoul 120-749, Republic of Korea;2. Department of Electrical Electronic and Control Engineering, Hankyong National University, Sukjong-dong, Ansung-si, Gyunggi-do 456-749, Republic of Korea;1. KERMIT, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure links 653, B-9000 Gent, Belgium;2. Laboratory of Hydrology and Water Management, Ghent University, Coupure links 653, B-9000 Gent, Belgium |
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| Abstract: | The sensor network localization based on connectivity can be modeled as a non-convex optimization problem. It can be argued that the actual problem should be represented as an optimization problem with both convex and non-convex constraints. A two-objective evolutionary algorithm is proposed which utilizes the result of all convex constraints to provide a starting point on the location of the unknown nodes and then searches for a solution to satisfy all the convex and non-convex constraints of the problem. The final solution can reach the most suitable configuration of the unknown nodes because all the information on the constraints (convex and non-convex) related to connectivity have been used. Compared with current models that only consider the nodes that have connections, this method considers not only the connection constraints, but also the disconnection constraints. As a MOEA (Multi-Objective Evolution Algorithm), PAES (Pareto Archived Evolution Strategy) is used to solve the problem. Simulation results have shown that better solution can be obtained through the use of this method when compared with those produced by other methods. |
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| Keywords: | Wireless sensor network Localization Connectivity Evolutionary algorithm Genetic algorithm Non-convex constraints |
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