Optimal LEACH Protocol with Improved Bat Algorithm in Wireless Sensor Networks

被引:22
作者
Cai, Xingjuan [1 ]
Sun, Youqiang [1 ]
Cui, Zhihua [1 ]
Zhang, Wensheng [2 ]
Chen, Jinjun [3 ]
机构
[1] Taiyuan Univ Sci & Technol, Complex Syst & Computat Intelligence Lab, Taiyuan 030024, Shanxi, Peoples R China
[2] Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing 100190, Peoples R China
[3] Univ Technol Sydney, Sydney, NSW 2007, Australia
来源
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS | 2019年 / 13卷 / 05期
基金
中国国家自然科学基金;
关键词
Low-energy adaptive clustering hierarchy (LEACH) protocol; Bat algorithm; Curve decline strategy; Triangle-flipping strategy; PARTICLE SWARM OPTIMIZATION; STRATEGY;
D O I
10.3837/tiis.2019.05.013
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A low-energy adaptive clustering hierarchy (LEACH) protocol is a low-power adaptive cluster routing protocol which was proposed by MIT's Chandrakasan for sensor networks. In the LEACH protocol, the selection mode of cluster-head nodes is a random selection of cycles, which may result in uneven distribution of nodal energy and reduce the lifetime of the entire network. Hence, we propose a new selection method to enhance the lifetime of network, in this selection function, the energy consumed between nodes in the clusters and the power consumed by the transfer between the cluster head and the base station are considered at the same time. Meanwhile, the improved FTBA algorithm integrating the curve strategy is proposed to enhance local and global search capabilities. Then we combine the improved BA with LEACH, and use the intelligent algorithm to select the cluster head. Experiment results show that the improved BA has stronger optimization ability than other optimization algorithms, which the method we proposed (FTBA-TC-LEACH) is superior than the LEACH and LEACH with standard BA (SBA-LEACH). The FTBA-TC-LEACH can obviously reduce network energy consumption and enhance the lifetime of wireless sensor networks (WSNs).
引用
收藏
页码:2469 / 2490
页数:22
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共 50 条
[21]  
Liang J. J, 2012, 201212 ZHENGZH U NAN
[22]  
Lindsey S., 2003, P AER C P, V3
[23]  
Manjeshwar A., 2002, P PAR DISTR PROC S P, P189
[24]  
Niu Wei-wei, 2011, Computer Engineering and Design, V32, P1869
[25]  
Okdem S, 2006, AHS 2006: First NASA/ESA Conference on Adaptive Hardware and Systems, Proceedings, P401
[26]   Wireless Sensor Networks: A Survey [J].
Potdar, Vidyasagar ;
Sharif, Atif ;
Chang, Elizabeth .
2009 INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS WORKSHOPS: WAINA, VOLS 1 AND 2, 2009, :636-641
[27]  
Ratnaweera A, 2004, IEEE T EVOLUT COMPUT, V8, P240, DOI [10.1109/TEVC.2004.826071, 10.1109/tevc.2004.826071]
[28]  
[任丰原 Ren Fengyuan], 2003, [软件学报, Journal of Software], V14, P1282
[29]   Bacterial foraging optimisation algorithm, particle swarm optimisation and genetic algorithm: a comparative study [J].
Sadeghiram, Soheila .
INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION, 2017, 10 (04) :275-282
[30]  
Shang Junna, 2015, Chinese Journal of Sensors and Actuators, V28, P1418, DOI 10.3969/j.issn.1004-1699.2015.09.025