Coverage-preserving Clustering Algorithm for Underwater Sensor Networks Based on the Sleeping Mechanism
DIAO Pengfei①② WANG Yanjiao③
①(College of Engineering and Technology, Northeast Forestry University, Harbin 150000, China) ②(College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China) ③(College of Information Engineering, Northeast Electric Power University, Jilin 132012, China)
Abstract:A new network deployment algorithm is proposed for the problem of low network lifetime and low network coverage of underwater sensor networks. Firstly, the node which has a higher network coverage redundancy should be asleep. Then the network coverage and energy consumption will be set as the objective functions. And the multi-objective optimization algorithm will be adopted to optimize it. At last, the TOPSIS is used to select the best solution from the Non-dominated solution set. If any node is dead, the sleeping nodes in the near dead node will be waken up to preserve the coverage. The results demonstrate that the proposed algorithm outperform the existing algorithms in terms of various performance metrics including energy consumption and the coverage.
AKYILDIZ I F, POMPILI D, and MELODIA T. Underwater acoustic sensor networks: research challenges[J]. Ad Hoc Networks, 2005, 3(3): 257-279. doi: 10.1016/j.adhoc.2005.01. 004.
GUO Zhongwen, LUO Hanjiang, and HONG Feng. Current progress and research issues in underwater sensor networks[J]. Journal of Computer Research and Development, 2010, 47(3): 377-389.
HONG Feng, ZHANG Yuliang, and YANG Bozhen. Review on time synchronization techniques in underwater acoustic sensor networks[J]. Acta Electronica Sinica, 2013, 41(5): 960-965. doi: 10.3969/j.issn.0372-2112.2013.05.020.
[4]
GUERRA F, CASARI P, and ZORZI M. World ocean simulation system (WOSS): A simulation tool for underwater networks with realistic propagation modeling[C]. ACM International Workshop on Underwater Networks, California, USA, 2009: 1-8. doi: 10.1145/1654130.1654134.
[5]
TAN H P, DIAMANT R, and SEAH W K G. A survey of techniques and challenges in underwater localization[J]. Ocean Engineering, 2011, 38(14): 1663-1676. doi: 10.1016/ j.oceaneng.2011.07.017
FU Xiuwen and LI Wenfeng. Evolutionary model of heterogeneous clustering wireless sensor networks based on local world theory[J]. Journal on Communications, 2015, 36(9): 204-214. doi: 10.11959/j.issn.1000-436x.2015157.
[8]
CHEN Zhi, LI Shuai, and YUE Wenjing. Memetic algorithm based multi-objective coverage optimization for wireless sensor networks[J]. Sensors, 2014, 14(11): 20500-20518. doi: 10.3390/s141120500.
JIN Shan and JIN Zhigang. Multi-objective sink nodes coverage algorithm based on quantum wolf pack evolution[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1178-1184. doi: 10.11999/JEIT160693.
[10]
OZDEMIR S, ATTEA B A, and KHALIL O A. Multi- objective evolutionary algorithm based on decomposition for energy efficient coverage in wireless sensor networks[J]. Wireless Personal Communications, 2013, 71(1): 195-215. doi: 10.1007/s11277-012-0811-3.
[11]
LATIF K, JAVAID N, AHMAD A, et al. On energy hole and coverage hole avoidance in underwater wireless sensor networks[J]. IEEE Sensors Journal, 2016, 16(11): 4431-4442. doi: 10.1109/JSEN.2016.2532389.
BI Xiaojun, DIAO Pengfei, and WANG Yanjiao. Multi- objective gravitational search algorithm based on decomposition[J]. Journal of Harbin Institute of Technology, 2015, 47(11): 69-75. doi: 10.11918/j.issn.0367-6234.2015.11. 012.
[17]
TAVANA M, LI Z, MOBIN M, et al. Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS[J]. Expert Systems with Applications, 2016, 50(5): 17-39. doi: 10.1016/j.eswa.2015. 11.007.
BI Xiaojun and DIAO Pengfei. Routing and clustering algorithm heterogeneous wireless sensor networks based on gravitational search algorithm[J]. Control and Decision, 2017, 32(3): 563-569. doi: 10.13195/j.kzyjc.2016.0111.