TY - GEN
T1 - An Estimation of Distribution Algorithm Based Load-Balanced Clustering of Wireless Sensor Networks
AU - Jiao, Dongbin
AU - Ke, Liangjun
AU - Yang, Weibo
AU - Li, Jing
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/8
Y1 - 2017/8/8
N2 - The load-balanced clustering is a most significant problem for WSNs with unequal load of the sensor nodes but it is known to be an NP-hard problem. This paper introduces a new model for the problem in which the objective function is to maximize the overall minimum lifetime of the cluster heads. To solve this model, we propose a novel estimation of distribution algorithm based dynamic clustering approach (EDA-MADCA). In EDA-MADCA, a new vector encoding is introduced for representing a complete clustering solution, and a probability matrix model is constructed to guide the individual search. In addition, EDA-MADCA merges the EDA based exploration and the local search based exploitation within the memetic algorithm (MA) framework. A minimum-lifetime-based local search (MLLS) strategy is presented to avoid invalid search and enhance the local exploitation of the EDA. Experiment results demonstrate that EDA-MADCA can prolong network lifetime, it outperforms the existing DECA algorithm in terms of various performance metrics.
AB - The load-balanced clustering is a most significant problem for WSNs with unequal load of the sensor nodes but it is known to be an NP-hard problem. This paper introduces a new model for the problem in which the objective function is to maximize the overall minimum lifetime of the cluster heads. To solve this model, we propose a novel estimation of distribution algorithm based dynamic clustering approach (EDA-MADCA). In EDA-MADCA, a new vector encoding is introduced for representing a complete clustering solution, and a probability matrix model is constructed to guide the individual search. In addition, EDA-MADCA merges the EDA based exploration and the local search based exploitation within the memetic algorithm (MA) framework. A minimum-lifetime-based local search (MLLS) strategy is presented to avoid invalid search and enhance the local exploitation of the EDA. Experiment results demonstrate that EDA-MADCA can prolong network lifetime, it outperforms the existing DECA algorithm in terms of various performance metrics.
KW - Energy efficiency
KW - Estimation of distribution algorithm
KW - Load-balanced clustering
KW - Memetic algorithm
KW - Wireless sensor networks
UR - https://www.scopus.com/pages/publications/85034631739
U2 - 10.1109/CSE-EUC.2017.35
DO - 10.1109/CSE-EUC.2017.35
M3 - 会议稿件
AN - SCOPUS:85034631739
T3 - Proceedings - 2017 IEEE International Conference on Computational Science and Engineering and IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, CSE and EUC 2017
SP - 151
EP - 158
BT - Proceedings - 2017 IEEE International Conference on Computational Science and Engineering and IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, CSE and EUC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Computational Science and Engineering and 15th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, CSE and EUC 2017
Y2 - 21 July 2017 through 24 July 2017
ER -