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Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms

Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms

Arpit Tripathi, Pulkit Gupta, Aditya Trivedi, Rahul Kala
Copyright: © 2011 |Volume: 7 |Issue: 2 |Pages: 21
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781613507940|DOI: 10.4018/jiit.2011040104
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MLA

Tripathi, Arpit, et al. "Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms." IJIIT vol.7, no.2 2011: pp.63-83. http://doi.org/10.4018/jiit.2011040104

APA

Tripathi, A., Gupta, P., Trivedi, A., & Kala, R. (2011). Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms. International Journal of Intelligent Information Technologies (IJIIT), 7(2), 63-83. http://doi.org/10.4018/jiit.2011040104

Chicago

Tripathi, Arpit, et al. "Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms," International Journal of Intelligent Information Technologies (IJIIT) 7, no.2: 63-83. http://doi.org/10.4018/jiit.2011040104

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Abstract

The ease of use and re-configuration in a wireless network has played a key role in their widespread growth. The node deployment problem deals with an optimal placement strategy of the wireless nodes. This paper models a wireless sensor network, consisting of a number of nodes, and a unique sink to which all the information is transmitted using the shortest connecting path. Traditionally the systems have used Genetic Algorithms for optimal placement of the nodes that usually fail to give results in problems employing large numbers of nodes or higher areas to be covered. This paper proposes a hybrid Genetic Programming (GP) and Genetic Algorithm (GA) for solving the problem. While the GP optimizes the deployment structure, the GA is used for actual node placement as per the GP optimized structure. The GA serves as a slave and GP serves as master in this hierarchical implementation. The algorithm optimizes total coverage area, energy utilization, lifetime of the network, and the number of nodes deployed. Experimental results show that the algorithm could place the sensor nodes in a variety of scenarios. The placement was found to be better than random placement strategy as well as the Genetic Algorithm placement strategy.

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