TY - JOUR AU - Permpol, Songyut AU - Rujirakul, Kanokmon AU - So-In, Chakchai PY - 2016/09/01 Y2 - 2024/03/29 TI - Adaptive Membership Selection Criteria using Genetic Algorithms for Fuzzy Centroid Localizations in Wireless Sensor Network JF - Journal of Telecommunication, Electronic and Computer Engineering (JTEC) JA - JTEC VL - 8 IS - 6 SE - Articles DO - UR - https://jtec.utem.edu.my/jtec/article/view/1258 SP - 113-118 AB - This paper investigates the effect of fuzzy inputs, i.e., signal strength, of various known nodes, to fuzzy logic systems in order to derive a proper weight for Centroid, properly used to approximate the location in wireless sensor networks with its key advantage on simplicity but with precision trade-off. Due to a fluctuation behavior of location estimation precisions with respect to a diversity of various inputs, here, we propose the use of heuristic approach applying genetic algorithms with mutation and cross-over steps to adaptively seek the optimal solution – a proper number of membership functions for fuzzy logic systems in weighted Centroid – to achieve higher location estimation accuracy. The performance of our methodology is effectively confirmed by the intensive evaluation on a large scale simulation in various topologies and node densities against fixed membership function scenarios including a traditional Centroid ER -