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Adaptive Five-State based Sensor Scheduling for Energy Conservation in Wireless Sensor Networks

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Authors:
P. Leela Rani, G.A. Sathish Kumar
10.5120/ijca2021921645

Leela P Rani and Sathish G A Kumar. Adaptive Five-State based Sensor Scheduling for Energy Conservation in Wireless Sensor Networks. International Journal of Computer Applications 183(26):23-27, September 2021. BibTeX

@article{10.5120/ijca2021921645,
	author = {P. Leela Rani and G.A. Sathish Kumar},
	title = {Adaptive Five-State based Sensor Scheduling for Energy Conservation in Wireless Sensor Networks},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2021},
	volume = {183},
	number = {26},
	month = {Sep},
	year = {2021},
	issn = {0975-8887},
	pages = {23-27},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume183/number26/32092-2021921645},
	doi = {10.5120/ijca2021921645},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Target tracking is the trending topics of research in Wireless Sensor Networks. It deals with detecting and estimating the consecutive positions of single or multiple targets during their course of movement in the observed area. Reducing the expenditure of energy, while offering high precision in tracking, is a tricky problem, as sensor nodes are restricted in terms of energy. The sensor nodes may be made to sleep to conserve energy. Nevertheless, sleep scheduling amplifies the likelihood of target loss while tracking, when the sensor nodes that are supposed to be active, are asleep. Consequently, there is a tradeoff between target coverage and efficiency in terms of energy. In this paper, we propose an adaptive five-state based sensor scheduling that balances this tradeoff. Simulation results illustrate that this scheme leverages between energy conservation and coverage efficiently.

References

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Keywords

Target tracking, wireless sensor networks, sleep scheduling