Dynamic Data Enhancing Battery Efficiency Through Collection Scheduling in IQRF Wireless Sensor Networks-Reference-Cited by-同舟云学术

Dynamic Data Enhancing Battery Efficiency Through Collection Scheduling in IQRF Wireless Sensor Networks

Published:2023-12-01 Issue:4 Volume:23 Page:3-9
ISSN:1338-3957
Container-title:Acta Electrotechnica et Informatica
language:en
Short-container-title:

Author:

Sebestyen Gergely¹,Kopjak József²

Affiliation:

1. Doctoral School of Applied Informatics and Applied Mathematics, Kandó Kálmán Faculty of Electrical Engineering , Óbuda University , Bécsi út 94-96, 1034 Budapest , Hungary

2. Kandó Kálmán Faculty of Electrical Engineering , Óbuda University , Bécsi út 94-96, 1034 Budapest , Hungary

Abstract

Abstract In this study, we explore innovative strategies for enhancing energy efficiency in Wireless Sensor Networks (WSNs), with a focus on the IQRF network. Our approach integrates dynamic sleep scheduling and data collection methods to optimize battery usage and extend the network’s operational lifespan. We introduce a battery life estimation model, taking into account various factors such as data collection frequency and network size. This model is instrumental in predicting battery longevity under different operational scenarios. Additionally, we develop a practical tool in the form of an API and an online calculator, aimed at assisting network designers in planning and maintaining energy-efficient WSNs. Our results, derived from a case study involving a CO2 sensor network, demonstrate the effectiveness of our methodologies in real-world applications. The study concludes that implementing dynamic data collection and sleep scheduling significantly enhances battery life, offering a valuable contribution to the sustainability and reliability of WSNs.

Publisher

Walter de Gruyter GmbH

Link

https://www.sciendo.com/pdf/10.2478/aei-2023-0016

Reference25 articles.

1. KHAN, J. A. ‒ QURESHI, H. K. ‒ IQBAL, A.: “Energy management in Wireless Sensor Networks: A survey,” Computers & Electrical Engineering, vol. 41, pp. 159–176, Jan. 2015, DOI: 10.1016/j.compeleceng.2014.06.009.

2. ANASTASI, G. ‒ CONTI, M. ‒ DI FRANCESCO, M. ‒ PASSARELLA, A.: “Energy conservation in wireless sensor networks: A survey,” Ad Hoc Networks, vol. 7, no. 3, pp. 537–568, May 2009, DOI: 10.1016/j.adhoc.2008.06.003.

3. DAS, I. ‒ SHAW, R. N. ‒ DAS, S.: “Analysis of Energy Consumption of Energy Models in Wireless Sensor Networks,” in Innovations in Electrical and Electronic Engineering, M. N. Favorskaya, S. Mekhilef, R. K. Pandey, and N. Singh, Eds., in Lecture Notes in Electrical Engineering. Singapore: Springer, 2021, pp. 755–764. DOI: 10.1007/978-981-15-4692-1_57.

4. GURUPRAKASH, B. ‒ BALASUBRAMANIAN, C. ‒ SUKUMAR, R: “An approach by adopting multi-objective clustering and data collection along with node sleep scheduling for energy efficient and delay aware WSN,” Peer-to-Peer Netw. Appl., vol. 13, no. 1, pp. 304–319, Jan. 2020, DOI: 10.1007/s12083-019-00779-3.

5. MA, J. ‒ LOU, W. ‒ WU, Y. ‒ LI, X.-Y. ‒ CHEN, G.: “Energy Efficient TDMA Sleep Scheduling in Wireless Sensor Networks,” in IEEE INFOCOM 2009, Apr. 2009, pp. 630–638. DOI: 10.1109/INFCOM.2009.5061970.