Node Clustering in Wireless Sensor Networks with Heuristic Optimization Methods
碩士 === 國立高雄應用科技大學 === 電子工程系碩士班 === 104 === This thesis is a comparative study using metaheuristic algorithms to solve the clustering problem in Wireless Sensor Networks (WSNs). By using a distance based energy model, we evaluate the problem in term of the transmission range of sensors along with th...
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ndltd-TW-104KUAS03930252017-04-16T04:35:11Z http://ndltd.ncl.edu.tw/handle/71961211709279870368 Node Clustering in Wireless Sensor Networks with Heuristic Optimization Methods 啟發式最佳化方法應用於無線感測網路之節點分群 Le Quang Duy 黎光維 碩士 國立高雄應用科技大學 電子工程系碩士班 104 This thesis is a comparative study using metaheuristic algorithms to solve the clustering problem in Wireless Sensor Networks (WSNs). By using a distance based energy model, we evaluate the problem in term of the transmission range of sensors along with the position of the base station. Four heuristic optimization methods are chosen due to the different characteristic in exploration and exploitation or selection and mutation process. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE) and Grey Wolf Optimizer (GWO) are implemented, experimented in terms of comparing the performances that using the same energy model. The metaheuristic algorithms are proved to be an excellent solution due to the outperform performance compare to LEACH. In both cases, Differential Evolution (DE) seems to be the best candidate with the first place in three of four experiments. Through implementing and doing research we had more understanding about the reason why and how heuristic optimization methods works so well through analyzing the behavior of each agent then modelling with mathematical equations. We hope that our work can be a useful reference for other researchers when applying heuristic optimization methods in clustering problem with similar energy model. Chin-Shiuh Shieh 謝欽旭 2016 學位論文 ; thesis 83 en_US |
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碩士 === 國立高雄應用科技大學 === 電子工程系碩士班 === 104 === This thesis is a comparative study using metaheuristic algorithms to solve the clustering problem in Wireless Sensor Networks (WSNs). By using a distance based energy model, we evaluate the problem in term of the transmission range of sensors along with the position of the base station.
Four heuristic optimization methods are chosen due to the different characteristic in exploration and exploitation or selection and mutation process. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE) and Grey Wolf Optimizer (GWO) are implemented, experimented in terms of comparing the performances that using the same energy model.
The metaheuristic algorithms are proved to be an excellent solution due to the outperform performance compare to LEACH. In both cases, Differential Evolution (DE) seems to be the best candidate with the first place in three of four experiments. Through implementing and doing research we had more understanding about the reason why and how heuristic optimization methods works so well through analyzing the behavior of each agent then modelling with mathematical equations. We hope that our work can be a useful reference for other researchers when applying heuristic optimization methods in clustering problem with similar energy model.
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Chin-Shiuh Shieh |
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Chin-Shiuh Shieh Le Quang Duy 黎光維 |
author |
Le Quang Duy 黎光維 |
spellingShingle |
Le Quang Duy 黎光維 Node Clustering in Wireless Sensor Networks with Heuristic Optimization Methods |
author_sort |
Le Quang Duy |
title |
Node Clustering in Wireless Sensor Networks with Heuristic Optimization Methods |
title_short |
Node Clustering in Wireless Sensor Networks with Heuristic Optimization Methods |
title_full |
Node Clustering in Wireless Sensor Networks with Heuristic Optimization Methods |
title_fullStr |
Node Clustering in Wireless Sensor Networks with Heuristic Optimization Methods |
title_full_unstemmed |
Node Clustering in Wireless Sensor Networks with Heuristic Optimization Methods |
title_sort |
node clustering in wireless sensor networks with heuristic optimization methods |
publishDate |
2016 |
url |
http://ndltd.ncl.edu.tw/handle/71961211709279870368 |
work_keys_str_mv |
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