Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm

Yang Chen; Xiaoguang Xu; Yong Wang

doi:10.3934/dcdss.2019059

Article Contents

2019, Volume 12, Issue 4&5: 887-900. Doi: 10.3934/dcdss.2019059 open access

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Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm

1.
Modern Education Technology Center, Anhui Polytechnic University, Anhui Wuhu, 241000, China
2.
School of Electrical Engineering, Anhui Polytechnic University, Anhui Wuhu, 241000, China
3.
Modern Education Technology Center, School of Computer and Information Engineering, Anhui Wuhu, 241000, China

* Corresponding author: Xiaoguang Xu
* Corresponding author: Xiaoguang Xu

Received: June 30, 2017

Revised: October 31, 2017

Published: March 2019

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Abstract

As a basic and fundamental problem in wireless sensor network (WSN), the network coverage greatly reflects the performance of information transmission in WSN. In order to achieve a good balance between target coverage and energy consumption, in this paper, we propose a novel wireless sensor network energy efficient coverage method based on genetic algorithm. Particularly, the goal of this work is cover a 2D sensing area via selecting a minimum number of sensors. Moreover, the deployed wireless sensors should be connected to let each sensor be connected a path to the base station. Afterwards, genetic algorithm is used to compute the minimum number of potential position to let all target be k-covered and all sensor nodes be m-connected, and each chromosome is set to be the number of potential positions. Finally, we provide a simulation to test the performance of the proposed method, and simulation results demonstrate that the proposed method can achieve high degree of target coverage without wasting extra energy.

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Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

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Figure 1. An example of node deployment scheme

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Figure 2. Initial node deployment for different schemes

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Figure 3. Coverage ratio for various number of sensors

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Figure 4. Maximum moved distance for various number of sensors

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Figure 5. Network lifetime with various number of sensors

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Figure 6. Network lifetime with various number of targets

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Table 1. Simulation settings

Parameter Value

Sensing field $50\times50$m$^2$

Coverage radius 5m

Number of targets 10-60

Initial population size 60

Mutation rate 3 %

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Table 2. Energy cost in this experiment

Working state Energy cost(mA)

Active 13.58

Transmitting 14.41

Receiving 9.37

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