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Lifetime Maximization of Target-Covered WSN Using Computational Swarm Intelligence

Lifetime Maximization of Target-Covered WSN Using Computational Swarm Intelligence

Roselin Jones
Copyright: © 2019 |Pages: 43
ISBN13: 9781522573357|ISBN10: 1522573356|ISBN13 Softcover: 9781522590552|EISBN13: 9781522573364
DOI: 10.4018/978-1-5225-7335-7.ch018
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MLA

Jones, Roselin. "Lifetime Maximization of Target-Covered WSN Using Computational Swarm Intelligence." Handbook of Research on the IoT, Cloud Computing, and Wireless Network Optimization, edited by Surjit Singh and Rajeev Mohan Sharma, IGI Global, 2019, pp. 383-425. https://doi.org/10.4018/978-1-5225-7335-7.ch018

APA

Jones, R. (2019). Lifetime Maximization of Target-Covered WSN Using Computational Swarm Intelligence. In S. Singh & R. Mohan Sharma (Eds.), Handbook of Research on the IoT, Cloud Computing, and Wireless Network Optimization (pp. 383-425). IGI Global. https://doi.org/10.4018/978-1-5225-7335-7.ch018

Chicago

Jones, Roselin. "Lifetime Maximization of Target-Covered WSN Using Computational Swarm Intelligence." In Handbook of Research on the IoT, Cloud Computing, and Wireless Network Optimization, edited by Surjit Singh and Rajeev Mohan Sharma, 383-425. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7335-7.ch018

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Abstract

In target-covered WSN, all critical points (CPs) are to be monitored effectively. Even a single node failure may cause coverage hole reducing the lifetime of the network. The sensor has non-rechargeable battery, and hence, energy supervision is inevitable. To maximize the lifetime of the WSN with guaranteed coverage and effective battery utilization, the activities of the sensors are to be scheduled and also the sensors may be repositioned towards the critical points. This chapter proposes an energy-efficient coverage-based artificial bee colony optimization (EEC-ABC) approach that exploits the intelligent foraging behavior of honeybee swarms to solve EEC problem to maximize the lifetime of the WSN. It also adheres to quality of service metrics such as coverage, residual energy, and lifetime. Similarly, energy-balanced dynamic deployment (EB-DD) optimization approach is proposed to heal the coverage hole to maximize the lifetime of the WSN. It positions the self-deployable mobile sensors towards the CPs to balance their energy density and thus enhances the lifetime of the network.

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