An Energy Efficient and Reliable Multipath Transmission Strategy for Mobile Wireless Sensor Networks

Author:

Sun Changlong1,Wang Zhengzong2,Lu Dongwan3,Cao Li2ORCID,Yue Yinggao2ORCID,Ding Haihua2ORCID,Hu Zhongyi3

Affiliation:

1. School of Economics and Management, Wenzhou University of Technology, Wenzhou 325035, China

2. School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China

3. Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China

Abstract

Multipath data transmission is a key problem that needs to be solved urgently in wireless sensor networks. In this paper, sensor node failure, link failure, energy exhaustion, and external interference affect the stability and reliability of network data transmission. A multipath transmission strategy for wireless sensor networks based on improved shuffled frog leaping algorithm is proposed. A mathematical model of multipath transmission in wireless sensor networks is established. In the shuffled frog leaping algorithm, combined with the transition probability in the particle swarm optimization algorithm, random individuals in the subgroup are introduced to assist the search when updating the frog individual position, which improves the algorithm's ability to jump out of the local optimum and improves the quality of the optimization algorithm solution. The model is applied to multipath transmission in wireless sensor networks. Then, the shuffled frog leaping algorithm is used to update, divide, and reorganize the sensor nodes to select the optimal node to establish the optimal transmission path and improve the stability and reliability of the network. Simulation experiments show that the algorithm in this paper can ensure the reliability of data transmission, reduce the network packet loss rate and network energy consumption, and reduce the average delay of data transmission.

Funder

Science and Technology Program of Zhejiang Province

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

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