Research on Emergency Communication Algorithm of Wireless Sensor Network Based on Chaos Mapping Osprey Optimization

Citation Author(s):
Songhao
Jia
Submitted by:
Songhao Jia
Last updated:
Fri, 07/12/2024 - 10:03
DOI:
10.21227/7v98-jz30
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Abstract 

Wireless Sensor Networks (WSNs) are self-organizing wireless networks comprising a large number of wireless sensors. Consequently, WSNs represent a potential method for providing communication in areas where the communication infrastructure has been severely damaged. However, the size of WSN nodes and the limited energy of the nodes result in a too-short life cycle for emergency communication networks.Aiming to address the issue of the cluster head node and the central node consuming excessive energy in the emergency communication network of wireless sensor networks, a wireless sensor network emergency communication algorithm based on chaotic mapping osprey optimization is proposed. Firstly, an optimization algorithm based on chaos theory is employed to select the virtual position of the initial population of the osprey optimization algorithm. This is achieved by simulating the randomness and unpredictability of chaotic systems. The actual position of the initial population is then calculated by the position mapping algorithm. Secondly, the optimal cluster head combination is selected through the osprey optimization algorithm and improved fitness function. Six factors, including the energy level of nodes, the distance between cluster heads, the distance between cluster heads and base stations, the distance between cluster heads and ordinary nodes, the variance of the distance between cluster heads and base stations, and the variance of the distance between cluster heads, are comprehensively considered in the selection process. Finally, the next hop node is selected by the fitness function of the FA-star algorithm to transmit the message. The simulation results show that the residual energy of CM-OOA algorithm after 1000 rounds of data transmission is 14% higher than the residual energy of CGWOA algorithm. It is 54% higher than the residual energy of PSO-C algorithm.

Instructions: 

this is a code about our paper.

Comments

thanks

Submitted by nupur parashar on Mon, 08/12/2024 - 23:05

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