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Reseach Article

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

by P. Leela Rani, G.A. Sathish Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 26
Year of Publication: 2021
Authors: P. Leela Rani, G.A. Sathish Kumar
10.5120/ijca2021921645

P. Leela Rani, G.A. Sathish Kumar . Adaptive Five-State based Sensor Scheduling for Energy Conservation in Wireless Sensor Networks. International Journal of Computer Applications. 183, 26 ( Sep 2021), 23-27. DOI=10.5120/ijca2021921645

@article{ 10.5120/ijca2021921645,
author = { P. Leela Rani, 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 = { Sep 2021 },
volume = { 183 },
number = { 26 },
month = { Sep },
year = { 2021 },
issn = { 0975-8887 },
pages = { 23-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number26/32092-2021921645/ },
doi = { 10.5120/ijca2021921645 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:17:58.331012+05:30
%A P. Leela Rani
%A G.A. Sathish Kumar
%T Adaptive Five-State based Sensor Scheduling for Energy Conservation in Wireless Sensor Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 26
%P 23-27
%D 2021
%I Foundation of Computer Science (FCS), NY, 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
  1. Mohamed Elshrkawey, Samiha M. Elsherif, M. Elsayed Wahed, An Enhancement Approach for Reducing the Energy Consumption in Wireless Sensor Networks, Journal of King Saud University - Computer and Information Sciences, Volume 30, Issue 2, 2018, Pages 259-267, ISSN 1319-1578, https://doi.org/10.1016/j.jksuci.2017.04.002.
  2. Kozłowski, A., Sosnowski, J. Energy Efficiency Trade-Off Between Duty-Cycling and Wake-Up Radio Techniques in IoT Networks. WirelessPers Commun 107, 1951–1971(2019). https://doi.org/10.1007/s11277-019-06368-0.
  3. Wan, R., Xiong, N. & Loc, N.T. An energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks. Hum. Cent. Comput. Inf. Sci. 8, 18 (2018). https://doi.org/10.1186/s13673-018-0141-x.
  4. Abtin Keshavarzian, Huang Lee, and Lakshmi Venkatraman. 2006. Wakeup scheduling in wireless sensor networks. In Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing (MobiHoc '06). Association for Computing Machinery, New York, NY, USA,322–333. https://doi.org/10.1145/1132905.1132941
  5. Chao Gui and Prasant Mohapatra. 2004. Power conservation and quality of surveillance in target tracking sensor networks. In Proceedings of the 10th annual international conference on Mobile computing and networking (MobiCom '04). Association for Computing Machinery, New York, NY, USA, 129–143. https://doi.org/10.1145/1023720.1023734.
  6. A. Singh and T. P. Sharma, "A survey on area coverage in wireless sensor networks," 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014, pp. 829-836, doi: 10.1109/ICCICCT.2014.6993073.
  7. Mihaela Cardei, Jie Wu, Energy-efficient coverage problems in wireless ad-hoc sensor networks, Computer Communications, Volume 29, Issue 4, 2006, Pages 413-420, ISSN 0140-3664, https://doi.org/10.1016/j.comcom.2004.12.025.
  8. Huang, CF., Tseng, YC. The Coverage Problem in a Wireless Sensor Network. Mobile Netw Appl 10, 519–528 (2005). https://doi.org/10.1007/s11036-005-1564-y.
  9. Wang, Xiaorui & Xing, Guoliang & Zhang, Yuanfang & Lu, Chenyang & Pless, Robert & Gill, Christopher. (2003). Integrated Coverage and Connectivity Configuration in Wireless Sensor Networks. SenSys'03: Proceedings of the First International Conference on Embedded Networked Sensor Systems. 1. 28-39. 10.1145/958491.958496.
  10. Woosun An, Satnam Singh, Krishna R. Pattipati, David L. Kleinman, Swapna S. Gokhale: Dynamic Scheduling of Multiple Hidden Markov Model-Based Sensors. J. Adv. Inf. Fusion 3(1): 33-49 (2008).
  11. N. Essaddi, M. Hamdi and N. Boudriga, "Efficient coverage criterion for accurate target tracking using cooperative wireless sensor networks," 2009 International Conference on Ultra Modern Telecommunications & Workshops, 2009, pp. 1-8, doi: 10.1109/ICUMT.2009.5345383.
Index Terms

Computer Science
Information Sciences

Keywords

Target tracking wireless sensor networks sleep scheduling