Fractional lion optimization for cluster head-based routing protocol in wireless sensor network

被引:45
作者
Sirdeshpande, Nandakishor [1 ]
Udupi, Vishwanath [1 ]
机构
[1] Gogte Inst Technol, Karnatak Law Soc, Belgaum, Karnataka, India
来源
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2017年 / 354卷 / 11期
关键词
CHANNEL; ALGORITHM;
D O I
10.1016/j.jfranklin.2017.04.005
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Due to the hopeful application of gathering information from unreachable position, wireless sensor network creates an immense challenge for data routing to maximize the communication with more energy efficiency. In order to design the energy efficient routing, the optimization based clustering protocols are more preferred in wireless sensor network. In this paper, we have proposed competent optimization based algorithm called Fractional lion (FLION) clustering algorithm for creating the energy efficient routing path. Here, the proposed clustering algorithm is used to increase the energy and lifetime of the network nodes by selecting the rapid cluster head. In addition, we have proposed multi-objective FLION clustering algorithm to develop the new fitness function based on the five objectives like intra-cluster distance, inter-cluster distance, cluster head energy, normal nodes energy and delay. Here, the proposed fitness function is used to find the rapid cluster centroid for an efficient routing path. Finally, the performance of the proposed clustering algorithm is compared with the existing clustering algorithms such as low energy adaptive clustering hierarchy (LEACH), particle swarm optimization (PSO), artificial bee colony (ABC) and Fractional ABC clustering algorithm. The results proved that the lifetime of the wireless sensor nodes is maximized by the proposed FLION based multi-objective clustering algorithm as compared with existing protocols. (C) 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:4457 / 4480
页数:24
相关论文
共 25 条
[21]   Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman Filter [J].
Wei, Guiyi ;
Ling, Yun ;
Guo, Binfeng ;
Xiao, Bin ;
Vasilakos, Athanasios V. .
COMPUTER COMMUNICATIONS, 2011, 34 (06) :793-802
[22]   Characterisation of a time-variant wireless propagation channel for outdoor short-range sensor networks [J].
Wyne, S. ;
Santos, T. ;
Singh, A. P. ;
Tufvesson, F. ;
Molisch, A. F. .
IET COMMUNICATIONS, 2010, 4 (03) :253-264
[23]   Degree-energy-based local random routing strategies for sensor networks [J].
Yan, Fan ;
Yeung, Alan K. H. ;
Joseph, Andreas C. ;
Chen, Guanrong .
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2015, 20 (01) :250-262
[24]   Data Clustering Using Variants of Rapid Centroid Estimation [J].
Yuwono, Mitchell ;
Su, Steven W. ;
Moulton, Bruce D. ;
Nguyen, Hung T. .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2014, 18 (03) :366-377
[25]  
Zhang W, 2012, IFIP ADV INF COMM TE, V369, P326