基于人工蜂群寻优算法的WSN中继节点布局方案

doi:10.11959/j.issn.1000-0801.2016221

摘要/Abstract

摘要：

无线传感器网络(WSN)环境下，中继节点位置布局性能优劣是影响网络寿命的关键因素之一。目前，针对三维空间高密度WSN，缺少能够在硬件成本和连通性双重约束条件下提高网络寿命的中继节点位置布局方案。基于网格布局方式，提出了一种基于人工蜂群优化算法的中继节点布局方案(ABC-RNDS)。ABC-RNDS方案采用双层网络拓扑结构，首先使用最小生成树法构建骨干网络，再使用人工蜂群优化算法通过网络参数寻优和限制中继节点总数的方法实现网络寿命的延长。实验验证分析表明，在成本和连通性受约束的条件下，ABC-RNDS算法与传统方案相比能够显著提高网络寿命。

关键词: 无线传感器网络, 中继节点, 人工蜂群优化, 网络寿命

Abstract:

In wireless sensor network(WSN),the relay node deployment performance is one of the main factors to affect network life.Aiming at the WSN with 3-demension high density,there is no existing relay node deployment schemes enhancing the network life efficiently with the constraints of hardware cost and network connectivity.Hence,a novel deployment scheme called artificial bee colony(ABC)-based relay node deployment scheme(ABC-RNDS)was proposed based on network-grid deployment method.In ABC-RNDS,double-layer topology was adopted to build WSN,which used minimum spanning tree to construct the backbone nodes of WSN,and applied ABC to extend the network lifetime by optimizing network parameters and constraining the number of relay nodes.The simulation results show that,compared to the traditional deployment scheme,ABC-RNDS can improve network lifetime significantly under constrains of cost and connectivity.

Key words: wirelesssensornetwork, relaynode, artificialbeecolonyoptimization, networklifetime

刘开南,韩旭. 基于人工蜂群寻优算法的WSN中继节点布局方案[J]. 电信科学, 2016, 32(9): 61-67.

Kainan LIU,Xu HAN. Deployment scheme of WSN relay nodes based on artificial bee colony optimization[J]. Telecommunications Science, 2016, 32(9): 61-67.

图/表 8

图

表1

表2

图3

图4

表3

表4

参考文献 14

[1]	任丰原, 黄海宁, 林闯 . 无线传感器网络[J]. 软件学报, 2003,14(7): 1282-1291. REN F Y , HUANG H N , LIN C . Wireless sensor networks[J]. Journal of Software, 2003,14(7): 1282-1291.
[2]	王翥, 王祁, 魏德宝 ,等. 无线传感器网络中继节点布局算法的研究[J]. 物理学报, 2012,61(12): 95-104. WANG Z , WANG Q , WEI D B ,et al. Relay node placement and addition algorithms in wireless sensor networks[J]. Acta Physica Sinica, 2012,61(12): 95-104.
[3]	SONG W , HUANG R , XU M ,et al. Design and deployment of sensor network for real-time high-fidelity volcano monitoring[J]. IEEE Transactions on Parallel Distributed System, 2010,21(11): 1658-1674.
[4]	YANG I , KE X , HAN S T . Optimal node placement strategies of linear wireless sensor networks with sensor node uniformly distributed[C]// Chinese Control Conference,July 26-28,2013,Xi’an,China. New Jersey:IEEE Press, 2013: 7365-7370.
[5]	王翥, 吕翠翠, 王玲 . 无线传感器网络中继节点的布局算法[J]. 仪器技术与传感器, 2014(1): 85-88. WANG Z , LV C C , WANG L . Relay node placement algorithm in wireless sensor networks[J]. Instrument Technique and Sensor, 2014(1): 85-88.
[6]	魏德宝 . 无线传感器网络中继节点设置算法的研究[D]. 哈尔滨:哈尔滨工业大学, 2011. WEI D B . Research on relay node placement algorithm in wireless sensor networks[D]. Harbin:Harbin Institute of Technology, 2011.
[7]	AL-TURJMAN F , HASSANEIN H , IBNKAHLA M . Connectivity optimization for wireless sensor networks applied to forest monitoring[C]// IEEE International Conference on Communications,June 14-18,2009,Dresden,German. New Jersey:IEEE Press, 2009: 145-159.
[8]	AL-TURJMAN F , HASSANEIN H , IBNKAHLA M . Efficient deployment of wireless sensor networks targeting environment monitoring applications[J]. Computer Communication, 2013,36(2): 135-148.
[9]	MINI S , UDAGATA S K , SABAT S L . Sensor deployment and scheduling for target coverage problem in wireless sensor networks[J]. IEEE Sensor Journal, 2014,14(3): 636-644.
[10]	ABIDIN H , DIN N , YASSIN I ,et al. Sensor node placement in wireless sensor network using multi-objective territorial predator scent marking algorithm[J]. Arabian Journal Forence ＆Engineer, 2014,39(1): 6317-6325.
[11]	LEE S , YOUNIS M . Optimized relay placement to federate segments in wireless sensor networks[J]. IEEE Trans Select Area Commun, 2010,28(5): 742-752.
[12]	XU K , HASSANEIN H , TAKAHARA G ,et al. Relay node deployment strategies in heterogeneous wireless sensor networks[J]. IEEE Transactions on Mobile Computing, 2010,9(2): 145-159.
[13]	张先迪, 李正良 . 图论及其应用[M]. 北京: 高等教育出版社, 2005. ZHANG X D , LI Z L . Graph theory and application[M]. Beijing: High Education PressPress, 2005.
[14]	罗钧, 李研 . 具有混沌搜索策略的蜂群优化算法[J]. 控制与决策, 2010,25(12): 1913-1916. LUO J , LI Y . Artificial bee colony algorithm with chaotic-search strategy[J]. Control and Decision, 2010,25(12): 1913-1916.

参数	取值
n_c	110
J_a/J	50×e^-7
ε₁/(J·bit^-1)	50×e^-9
ε₂/(J·(bit·m^-2)^-1)	10×e^-12
γ	4.8
β/(J·bit^-1)	50×e^-9
L/bit	512
E_i/J	15
T/个	100
R	100
A	10
r/m	100

网络规模	优化参数数目	种群大小	迭代次数
20	190	400	200
30	445	600	200
40	780	800	200
50	1 205	1 000	200
60	1 780	1 200	200

参数				实验次数/次					平均值
参数	1	2	3	4	5	6	7	8	平均值
W(G)	20.007 0	20.422 4	22.046 2	20.043 3	19.489 9	20.668 1	20.014 5	20.421 5	20.389 1
μ(G)	10.515 5	11.174 9	11.221 2	11.703 5	11.704 7	12.018 0	13.775 5	11.184 4	11.662 2
E_p	0.567 9	0.634 0	0.638 8	0.690 3	0.690 5	0.725 3	0.942 0	0.635 0	0.690 5
T_R	4.274 5	3.576 1	3.529 7	3.065 9	3.064 8	2.781 4	1.418 0	3.566 5	3.159 6
λ₂	0.597 8	0.595 8	0.583 3	0.596 8	0.598 5	0.598 1	0.599 5	0.597 3	0.595 9

参数				实验次数/次					平均值
参数	1	2	3	4	5	6	7	8	平均值
W(G)	149.298 0	146.547 8	144.895 1	151.623 6	148.246 8	146.998 2	147.852 2	149.748 4	148.151 3
μ(G)	1.870 0	1.493 5	1.267 2	2.188 3	1.726 1	1.555 2	1.672 1	1.931 6	1.713 0
E_p	0.032 3	0.019 5	0.012 2	0.043 6	0.027 3	0.021 6	0.025 5	0.034 4	0.027 0
T_R	88.888 6	95.168 0	99.188 0	83.949 4	91.230 5	94.105 2	92.127 9	87.906 3	91.570 5
λ₂	0.554 2	0.535 2	0.548 6	0.567 1	0.572 4	0.559 6	0.537 1	0.551 9	0.553 3

[1]	丁铖, 陈锦荣, 曹小冬, 王翊. 基于服务质量的层次化结构资源分配算法[J]. 电信科学, 2022, 38(1): 102-111.
[2]	李勇燕,吴坚平. 基于IPSO-MC融合算法的无线传感器节点定位[J]. 电信科学, 2020, 36(3): 11-18.
[3]	张红,沈士根,吴小军,曹奇英. 基于元胞自动机和静态贝叶斯博弈的WSN恶意程序传染模型[J]. 电信科学, 2019, 35(6): 60-69.
[4]	陈瑞凤,倪明,徐春婕,胡昊. 面向铁路环境监测的无线传感器网络通信性能分析[J]. 电信科学, 2019, 35(5): 70-77.
[5]	周颖,杨丽花,杨龙祥,倪梦. 基于卡尔曼预测与压缩感知的WSN中高能效数据收集方法[J]. 电信科学, 2019, 35(1): 74-80.
[6]	曹世华,胡克用,王李冬,王琦晖. 基于分布式色调识别的无线传感器网络k连通性路径检测[J]. 电信科学, 2018, 34(2): 65-73.
[7]	周海平,沈士根,黄龙军,冯晟. 基于博弈论的无线传感器网络恶意程序传播模型[J]. 电信科学, 2018, 34(11): 67-76.
[8]	孟得月,裴二荣,黄佑林. 异质节点分离CRSN中MAC协议设计与性能分析[J]. 电信科学, 2017, 33(8): 107-119.
[9]	刘建华,李明禄,李大志,李鲁群. 工业无线传感云中面向移动机器人的自适应信任演化机制[J]. 电信科学, 2017, 33(6): 86-96.
[10]	戴国勇,施伟元,应可珍,陈庆章,毛科技. 基于移动节点二次部署的WSN栅栏新型强化方法[J]. 电信科学, 2017, 33(6): 97-104.
[11]	王骐,肖正安,王怀兴. 无线传感器网络中基于“k-覆盖问题”的多项式时间算法[J]. 电信科学, 2017, 33(12): 91-98.
[12]	孟凯露,岳克强,尚俊娜. 基于元胞蝙蝠算法的无线传感器网络节点定位研究[J]. 电信科学, 2017, 33(11): 56-65.
[13]	邹永艳,张衍志. WSN中视觉辅助方案结合TI CC2431 ZPS平台的跟踪定位方法[J]. 电信科学, 2016, 32(9): 68-74.
[14]	张衍志,叶小琴. WSN中基于周期性超宽带距离信息的女巫攻击检测[J]. 电信科学, 2016, 32(8): 110-117.
[15]	刘述木,杨建,黎远松. WSN中基于自适应预测聚类的多组群目标的跟踪方法[J]. 电信科学, 2016, 32(7): 68-75.