基于元胞蝙蝠算法的无线传感器网络节点定位研究

doi:10.11959/j.issn.1000-0801.2017311

Abstract

Abstract:

To further enhance the location precision of unknown nodes and solve the node location error in wireless sensor network,a location method based on cellular automata bat algorithm was presented.Mixed the idea of cellular automata and the bat algorithm and drawed into the cellular RCS niche technique and disaster mechanism,the algorithm could jump out of local optimum and increase the convergence speed.In order to verify the feasibility and efficiency,the proposed algorithm was verified through simulation of several benchmark functions.Then the algorithm implemented the CA-BA to node location prediction to increase the precision of the unknown node location.In the measured experiment,the results show that the proposed algorithm has higher accuracy compared to the improved PSO algorithm,which average localization error is less than 0.5m.

Key words: WSN, node localization, cellular automata, bat algorithm, accuracy

CLC Number:

TP393

Kailu MENG,Keqiang YUE,Junna SHANG. Wireless sensor network nodes localization method based on cellular automata bat algorithm[J]. Telecommunications Science, 2017, 33(11): 56-65.

Figures/Tables 11

测试函数	函数表达式	搜索空间	理论最优解
Quadric	$f_{1} (x) = \sum_{i = 1}^{D} \sum_{j = 1}^{i} (x j)^{2}$	-30,30]	0
Rosenbrock	$f_{2} (x) = \sum_{i = 1}^{D - 1} [100 (x_{i + 1} - x_{i}^{2})^{2} + (x_{i} - 1)^{2}]$	-2.048,2.048]	0
Schwefel	$f_{3} (x) = - \sum_{i = 1}^{n} (x_{i} \sin \sqrt{\| x_{i} \|})$	-100,100]	-418.982 9n
Ackley	$f_{4} (x) = 20 + e - 20 e^{- 0.2 \sqrt{\frac{\sum_{i = 1}^{n} x_{i}^{2}}{n}}} - e^{\sum_{i = 1}^{n} \cos 2 π x_{i}}$	-30,30]	0
Griewank	$f_{5} (x) = \frac{1}{4 000} \sum_{i = 1}^{D} x_{i}^{2} - \prod_{i = 1}^{D} \cos (\frac{x i}{\sqrt{i}}) + 1$	-600,600]	0
Rastrigin	$f_{6} (x) = \sum_{i = 1}^{D} [x_{i}^{2} - 10 \cos (2 π x_{i}) + 10]$	-5.12,5.12]	0

References 15

[1]	PENG L J , LI W W . The Improvement of 3D wireless sensor network nodes locolization[C]// 26th IEEE Chinese Control and Decision Conference,May 31-June 2,2014,Changsha,China. New Jersey:IEEE Press, 2016: 4873-4878.
[2]	方震, 赵湛, 郭鹏 ,等. 基于 RSSI 测距分析[J]. 传感技术学报, 2007(11): 2526-2530.
	FANG Z , ZHAO Z , GUO P ,et al. Analysis of distance measurement based on RSSI[J]. Chinese Journal of Sensors and Actuators, 2007,20(11): 2526-2530.
[3]	焦磊, 邢建平, 张军 ,等. 一种非视距环境下具有鲁棒特性TOA 无线传感网络定位算法[J]. 传感技术学报, 2007,20(7): 1625-1629.
	JIAO L , XING J P , ZHANG J ,et al. A new NLOS TOA-based wireless sensor network localization algorithm with robust character[J]. Chinese Journal of sensors actuators, 2007,20(7): 1625-1629.
[4]	刘长平, 叶春明 . 具有混沌搜索策略的蝙蝠优化算法及性能仿真[J]. 系统仿真学报, 2013,25(6): 1183-1188,1195.
	LIU C P , YE C M . Bat algorithm with chaotic search strategy and analysis of its property[J]. Journal of System Simulation, 2013,25(6): 1183-1188,1195.
[5]	赖锦辉 . 基于蝙蝠优化算法的无线传感器网络节点定位研究[J]. 计算机测量与控制, 2014,22(8): 2709-2712.
	LAI J H . Research on nodes localization method for wireless sensor networks based on bat optimization algorithm[J]. Computer Measurement ＆ Control, 2014,22(8): 2709-2712.
[6]	尚俊娜, 刘春菊, 岳克强 ,等. 多智能体蝙蝠算法在无线传感器中的应用[J]. 传感技术学报, 2015,28(9): 1418-1424.
	SHANG J N , LIU C J , YUE K Q ,et al. The multi-agent bat algorithm applied to wireless sensor network[J]. Chinese Journal of Sensors and Actuators, 2015,25(9): 1418-1424.
[7]	朱大林, 詹腾, 张屹 ,等. 多策略差分进化的元胞多目标粒子群算法[J]. 电子学报, 2014,42(9): 1831-1838.
	ZHU D L , ZHAN T , ZHANG Y ,et al. Cellular multi-objective particle swarm algorithm based on multi-strategy differential evolution[J]. Acta Electronica Sinica, 2014,42(9): 1831-1838.
[8]	张屹, 万兴余, 郑小东 ,等. 基于正交设计的元胞多目标遗传算法[J]. 电子学报, 2016,44(1): 87-94.
	ZHANG Y , WAN X Y , ZHENG X D ,et al. Cellular genetic algorithm for multiobjective optimization based on orthogonal design[J]. Acta Electronica Sinica, 2016,44(1): 87-94.
[9]	YANG X S . A new metaheuristic bat-inspired algorithm[J]. Nature Inspired Cooperative Strategies for Optimization, 2010(284): 65-74.
[10]	石杨 . 元胞粒子群优化算法及其在柔性作业车间调度中的应用[D]. 武汉:华中科技大学, 2010: 8-10.
	SHI Y . A thesis submitted in partial fulfillment of the requirements for the degree of master of engineering[D]. Wuhan:Huazhong University of Science and Technology, 2010: 8-10.
[11]	张俞 . 元胞遗传算法的研究[D]. 江西:南昌航空大学, 2009: 37-41.
	ZHANG Y . Research for cellular genetic algorithm[D]. Jiangxi:Nanchang Hangkong University, 2009: 37-41.
[12]	李新鹏, 张超勇, 高亮 ,等. 基于元胞粒子群算法的数控切削参数优化[J]. 计算机工程与应用, 2014,50(2): 252-257.
	LI X P , ZHANG C Y , GAO L ,et al. NC cutting parameter optimization based on cellular particle swarm optimization algorithm[J]. Computer Engineering and Applications, 2014,50(2): 252-257.
[13]	鲁宇明, 陈殊, 黎明 ,等. 自适应调整选择压力的灾变元胞遗传算法[J]. 系统仿真学报, 2013,25(3): 436-444.
	LU Y M , CHEN S , LI M ,et al. Self-adaptive cellular genetic algorithms with disaster based on selection pressure[J]. Journal of System Simulation, 2013,25(3): 436-444.
[14]	詹杰, 刘宏立, 刘述钢 ,等. 基于RSSI的动态权重定位算法研究[J]. 电子学报, 2011,39(1): 82-88.
	ZHAN J , LIU H L , LIU S G ,et al. The study of dynamic degree weighted centroid localization algorithm based on RSSI[J]. Acta Electronic Sinica, 2011,39(1): 82-88.
[15]	尚俊娜, 盛林, 程涛 ,等. 基于 LQI 权重和改进粒子群算法的室内定位方法[J]. 传感技术学报, 2017,30(2): 284-290.
	SHANG J N , SHENG L , CHENG T ,et al. The indoor localization based on LQI weight and improved particle swarm optimization algorithm[J]. Chinese Journal of Sensors and Actuators, 2017,30(2): 284-290.

Metrics

Recommended 0

No Suggested Reading articles found!

函数f ₁～ f₆	BA		CBA		MA-BA		CA-BA
函数f ₁～ f₆	平均最小适应度值	成功率	平均最小适应度值	成功率	平均最小适应度值	成功率	平均最小适应度值	成功率
Quadric	0.339 1	0	0.232 0	0	1.100 4×10^?39	40%	7.120 4×10^?43	80%
Rosenbrock	24.789 7	0	6.521 0	21%	0.099 84	47%	0.078 24	70%
Schwehel	?9.77×10²	50%	?1.16×10³	63%	?1.573 9×10³	100%	?2.315 9×10³	100%
Ackley	1.879 6	0	1.245 5	13%	5.968 1×10^?12	77%	5.267 5×10^?13	97%
Griewank	0.003 9	43%	5.540 6×10^?5	46%	8.278 5×10^?17	98%	0	100%
Rastrigin	32.328 3	0	13.929 4	10%	2.023 1	67%	1.691 4	87%

未知节点标号	实际位置/m	MA-BA		CA-BA
未知节点标号	实际位置/m	预测值/m	AVE/m	预测值/m	AVE/m
1	（6.104 4，4.334 5）	（6.296 8，4.450 7）	0.224 8	（6.185 3，4.343 9）	0.081 4
2	（1.049 1，0.870 4）	（1.187 3，0.952 3）	0.160 6	（1.037 8，0.855 5）	0.018 7
3	（9.499 7，1.784 5）	（9.345 6，1.968 5）	0.240 0	（9.507 8，1.803 9）	0.021 0
4	（3.765 0，2.711 1）	（3.891 3，2.931 4）	0.253 9	（3.805 2，2.739 1）	0.049 0
5	（8.176 7，4.902 0）	（8.313 5，5.172 9）	0.303 5	（8.146 1，4.833 2）	0.075 3
6	（23.042 9，23.027 5）	23.179 5，23.322 5）	0.325 1	（23.029 9，23.315 9）	0.288 7
7	（26.662 8，3.286 0）	（26.815 7，3.495 7）	0.259 5	（26.682 8，3.296 0）	0.022 4
8	（26.0103，25.2551）	26.318 5，25.354 4）	0.323 8	（26.257 9，25.287 9）	0.249 8
9	（23.480 0，15.824 7）	23.290 1，15.687 1）	0.234 5	（23.416 0，15.907 3）	0.104 5
10	（28.758 0，4.574 6）	（28.587 3，4.407 9）	0.2386	（28.752 5，4.591 5）	0.017 8

算法	8个锚节点
改进PSO	0.013 0
CA-BA	0.001 1

Wireless sensor network nodes localization method based on cellular automata bat algorithm

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 11

References 15

Related Articles 15

Metrics

Recommended 0

[1]	Lu HAN, Weiyu CHEN, Fei ZHANG, Jianfeng HE, Huaizhen SU. Big data classification method of non relational distributed submission information under differentiated requirements [J]. Telecommunications Science, 2023, 39(6): 114-121.
[2]	Lijuan YE, Yiting WANG, Licheng ZHU. Cellular automata model based power network attack prediction technology [J]. Telecommunications Science, 2023, 39(4): 173-179.
[3]	Xinghua JIA, Peng LIU, Wangdong QI, Shengheng LIU, Yongming HUANG, Jialu LI, Jia XU. Technical perspective and simulation platform for 5G integrated communication and high precision localization [J]. Telecommunications Science, 2022, 38(8): 75-85.
[4]	Ming AI, Yunjing HOU, Runze ZHOU, Mao CAI. Location services and technologies of 5G-Advanced network [J]. Telecommunications Science, 2022, 38(6): 120-130.
[5]	Bin REN, Zhenyu ZHANG, Rongyi FANG, Xiaotao REN, Jianxiang LI, Yunjing HOU, Zhe YU, Shaohui SUN. High-accuracy positioning for 5G-Advanced wireless systems [J]. Telecommunications Science, 2022, 38(3): 65-73.
[6]	Zihao LIU, Xiaojun JIA, Sulan ZHANG, Zhiling XU, Jun ZHANG. Vibe++ background segmentation method combining MeanShift clustering analysis and convolutional neural network [J]. Telecommunications Science, 2021, 37(3): 133-145.
[7]	Haiqing YU,Wei DING,Jie XU. Accuracy analysis on access hyper-point real-time detection algorithms in high-speed network [J]. Telecommunications Science, 2020, 36(4): 74-82.
[8]	ZHANG Hong,SHEN Shigen,WU Xiaojun,CAO Qiying. WSN malware infection model based on cellular automaton and static Bayesian game [J]. Telecommunications Science, 2019, 35(6): 60-69.
[9]	Shumu LIU,Jian YANG,Yuansong LI. Multi-group target tracking method based on adaptive predictive clustering in WSN [J]. Telecommunications Science, 2016, 32(7): 68-75.
[10]	Xiaoyu WAN,Pan HU,Zhengqiang WANG. Spectrum prediction algorithm in ISM band based on two-dimensional LMBP neural network [J]. Telecommunications Science, 2016, 32(3): 53-59.
[11]	Chong WANG,Zeng ZENG. An energy conservation method for locating wireless sensor network nodes in smart grid [J]. Telecommunications Science, 2016, 32(3): 183-186.
[12]	Mengdan ZHANG,Guangyue LU,Honggang WANG,Jiming LIU. Wireless indoor localization technology based on fingerprint algorithm [J]. Telecommunications Science, 2016, 32(10): 77-86.
[13]	Ershen WANG,Qing ZHANG,Hong LEI,Tao PANG. Evaluation method of satellite navigation signal-in-space user range error [J]. Telecommunications Science, 2016, 32(10): 56-62.
[14]	Dameng Wu,Jiangbo Qian,Yefang Chen,Yihong Dong. Query Processing Algorithm Based on Neighbor Information in Delay Tolerant Network [J]. Telecommunications Science, 2014, 30(4): 100-108.
[15]	Qin Yu,Zhihui Xiao,Ning An. AODV-ECA: Energy-Efficient AODV Routing Protocol Using Cellular Automata in Wireless Sensor Network [J]. Telecommunications Science, 2013, 29(6): 105-109.