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Scheduling Optimization Based on Energy Prediction Using ARIMA Model in WSN

Scheduling Optimization Based on Energy Prediction Using ARIMA Model in WSN

Pooja Chaturvedi, Ajai Kumar Daniel
Copyright: © 2022 |Pages: 32
ISBN13: 9781668439210|ISBN10: 1668439212|ISBN13 Softcover: 9781668439227|EISBN13: 9781668439234
DOI: 10.4018/978-1-6684-3921-0.ch012
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MLA

Chaturvedi, Pooja, and Ajai Kumar Daniel. "Scheduling Optimization Based on Energy Prediction Using ARIMA Model in WSN." Information Security Practices for the Internet of Things, 5G, and Next-Generation Wireless Networks, edited by Biswa Mohan Sahoo and Suman Avdhesh Yadav, IGI Global, 2022, pp. 245-276. https://doi.org/10.4018/978-1-6684-3921-0.ch012

APA

Chaturvedi, P. & Daniel, A. K. (2022). Scheduling Optimization Based on Energy Prediction Using ARIMA Model in WSN. In B. Sahoo & S. Yadav (Eds.), Information Security Practices for the Internet of Things, 5G, and Next-Generation Wireless Networks (pp. 245-276). IGI Global. https://doi.org/10.4018/978-1-6684-3921-0.ch012

Chicago

Chaturvedi, Pooja, and Ajai Kumar Daniel. "Scheduling Optimization Based on Energy Prediction Using ARIMA Model in WSN." In Information Security Practices for the Internet of Things, 5G, and Next-Generation Wireless Networks, edited by Biswa Mohan Sahoo and Suman Avdhesh Yadav, 245-276. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-3921-0.ch012

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Abstract

Wireless sensor networks (WSNs) have attracted great attention because of their applicability in a variety of applications in day-to-day life such as structural monitoring, healthcare, surveillance, etc. Energy conservation is a challenging issue in the context of WSN as these networks are usually deployed in hazardous and remote applications where human intervention is not possible; hence, recharging or replacing the battery of sensor nodes is not feasible often. Apart from energy conservation, target coverage is also a major challenge. Scheduling the nodes to exist in active and sleep modes is an efficient mechanism to address the energy efficiency and coverage problem. The chapter proposes an ARIMA model-based energy consumption prediction approach such that the set cover scheduling may be optimized. The chapter compares the efficiency of several ARIMA-based models, and the results show that the ARIMA (0,1,2) model provides best results for the considered scenario in terms of energy consumption.

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