基于模拟退火算法和改进灰狼优化器的异构无线传感器网络路由协议

doi:10.11959/j.issn.2096-3750.2021.00211

Chinese Journal on Internet of Things ›› 2021, Vol. 5 ›› Issue (2): 97-106.doi: 10.11959/j.issn.2096-3750.2021.00211

• Theory and Technology • Previous Articles Next Articles

Heterogeneous wireless sensor network routing protocol based on simulated annealing algorithm and modified grey wolf optimizer

Xiaoqiang ZHAO¹^,², Shaoya REN¹^,², Yongzhi ZHAI¹^,², Heng QUAN¹^,², Ting YANG¹^,²

¹ School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
² Shaanxi Key Laboratory of Information Communication Network and Security, Xi’an 710121, China

Revised:2020-10-15 Online:2021-06-30 Published:2021-06-01
Supported by:
The National Natural Science Foundation of China(U1965102);The Shaanxi Innovative Talent Promotion Plan-Science and Technology Innovation Team(2019TD-28);The Science and Technology Projects of Xi’an(201806117YF05NC13-2);The Industrialization Cultivation Project of Shaanxi Provincial Department of Education(18JF029);The Shaanxi International Cooperation Project(2018KW-025)

Abstract

Abstract:

It’s one of the main goals of the heterogeneous wireless sensor network (HWSN) to extend the network lifecycle by reasonably utilizing the heterogeneity of node energy.Therefore, according to the heterogeneity of node energy, a routing protocol (SA-MGWO) for HWSN based on simulated annealing (SA) algorithm and modified grey wolf optimizer (GWO) was proposed.Firstly, the appropriate initial clusters were selected by de?ning different ?tness functions for heterogeneous energy nodes.Secondly, The ?tness values of nodes were calculated and treated as initial weights in the GWO.At the same time, the weights were updated dynamically according to the distance between the wolves and their prey and coefficient vectors to improve the GWO’s optimization ability.Finally, simulated annealing algorithm was used to ensure the selection of optimal cluster set in heterogeneous networks.Compared with stable election protocol (SEP), distribute energy efficient clustering (DEEC), modified stable election protocol (M-SEP), and fitness value based improved grey wolf optimizer (FIGWO) protocols, the experimental results indicate that the network lifecycle of the SA-MGWO protocol improves by 53.1%, 31.9%, 46.5% and 27.0% respectively.

Key words: heterogeneous wireless sensor network, simulated annealing algorithm, grey wolf optimizer, network lifecycle

CLC Number:

Xiaoqiang ZHAO, Shaoya REN, Yongzhi ZHAI, Heng QUAN, Ting YANG. Heterogeneous wireless sensor network routing protocol based on simulated annealing algorithm and modified grey wolf optimizer[J]. Chinese Journal on Internet of Things, 2021, 5(2): 97-106.

Figures/Tables 9

实验参数	场景
网络区域	100 m ×100 m
网络中节点数目	100
基站位置	{50 m,50 m}
发送和接收数据报长度	4 000 bit
普通节点初始能量	0.5J
高级节点初始能量超过普通节点初始能量的倍数	1
高级节点占节点总数比例	1:10
最优簇首选取比例	1:10
单位比特发送能耗E_elec	50nJ bit
单位比特数据融合能耗E_DA	50nJ bit
自由空间模型下功率放大器损耗系数ε_fs	10 pJ/(bit?m ²)
多径衰减模型下功率放大器损耗系数ε_mp	0.0013 pJ/(bit?m ⁴)
适应度函数的权重	$a_{11} = \frac{1}{3}, a_{12} = \frac{1}{6}, a_{2} = \frac{1}{2}, a_{3} = 0.2$
目标优化函数的权重	$a_{41} = a_{42} = a_{43} = \frac{1}{3}$

References 15

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