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Deployment of Context-Aware Sensor in Wireless Sensor Network Based on the Variants of Genetic Algorithm

Deployment of Context-Aware Sensor in Wireless Sensor Network Based on the Variants of Genetic Algorithm

Puri Vishal, Ramesh Babu A.
Copyright: © 2018 |Volume: 8 |Issue: 2 |Pages: 24
ISSN: 1947-3087|EISSN: 1947-3079|EISBN13: 9781522544999|DOI: 10.4018/IJALR.2018070101
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MLA

Vishal, Puri, and Ramesh Babu A. "Deployment of Context-Aware Sensor in Wireless Sensor Network Based on the Variants of Genetic Algorithm." IJALR vol.8, no.2 2018: pp.1-24. http://doi.org/10.4018/IJALR.2018070101

APA

Vishal, P. & Ramesh Babu A. (2018). Deployment of Context-Aware Sensor in Wireless Sensor Network Based on the Variants of Genetic Algorithm. International Journal of Artificial Life Research (IJALR), 8(2), 1-24. http://doi.org/10.4018/IJALR.2018070101

Chicago

Vishal, Puri, and Ramesh Babu A. "Deployment of Context-Aware Sensor in Wireless Sensor Network Based on the Variants of Genetic Algorithm," International Journal of Artificial Life Research (IJALR) 8, no.2: 1-24. http://doi.org/10.4018/IJALR.2018070101

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Abstract

Wireless sensor networks (WSNs) are generally a group of spatially scattered and devoted sensors to record and monitor the physical environmental condition, and the collected data is grouped at a central location. In fact, the environmental conditions such as sound, humidity, temperature, wind, pollution levels, etc., can be clearly determined by WSNs. The principal objective of WSNs is to organize the whole sensor nodes in their related positions, thereby developing an effective network. In WSNs, target COVerage (TCOV) and Network CONnectivity (NCON) are the main concern of the sensor deployment problem. Many research works aspire the evolvement of smart context awareness algorithm for sensor deployment issues in WSN. Here the TCOV and NCON process are deployed as the minimization problem. This article makes an analysis of different GA variations in attaining the objective. The GA variations are as follows: self-adaptive genetic algorithm (SAGA), deterministic-adaptive genetic algorithm (DAGA), Individual- Adaptive Genetic Algorithm (IAGA). Finally, the methods are compared to one another in terms of connectivity and coverage performance.

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