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Wireless Sensor Network Design for Energy-Efficient Monitoring

Wireless Sensor Network Design for Energy-Efficient Monitoring

Daniele Apiletti, Elena Baralis, Tania Cerquitelli
Copyright: © 2013 |Pages: 23
ISBN13: 9781466640382|ISBN10: 1466640383|EISBN13: 9781466640399
DOI: 10.4018/978-1-4666-4038-2.ch007
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MLA

Apiletti, Daniele, et al. "Wireless Sensor Network Design for Energy-Efficient Monitoring." Intelligent Technologies and Techniques for Pervasive Computing, edited by Kostas Kolomvatsos, et al., IGI Global, 2013, pp. 134-156. https://doi.org/10.4018/978-1-4666-4038-2.ch007

APA

Apiletti, D., Baralis, E., & Cerquitelli, T. (2013). Wireless Sensor Network Design for Energy-Efficient Monitoring. In K. Kolomvatsos, C. Anagnostopoulos, & S. Hadjiefthymiades (Eds.), Intelligent Technologies and Techniques for Pervasive Computing (pp. 134-156). IGI Global. https://doi.org/10.4018/978-1-4666-4038-2.ch007

Chicago

Apiletti, Daniele, Elena Baralis, and Tania Cerquitelli. "Wireless Sensor Network Design for Energy-Efficient Monitoring." In Intelligent Technologies and Techniques for Pervasive Computing, edited by Kostas Kolomvatsos, Christos Anagnostopoulos, and Stathes Hadjiefthymiades, 134-156. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-4038-2.ch007

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Abstract

Wireless sensors are small-scale mobile devices that can programmatically measure physical quantities, perform simple computations, store, receive, and transmit data. The lattice built by a set of cooperating sensors is called a sensor network. Since sensor networks provide a powerful infrastructure for large-scale monitoring applications, an important issue is the network design to achieve an optimal placement of the sensors to allow (1) energy-efficient monitoring and (2) gathering meaningful data. This chapter presents a novel approach to optimize sensing node placement (e.g., for new to-be-deployed networks) and efficiently acquire data from existing sensor networks. A historical data analysis task is performed to discover spatial and temporal correlations and identify sets of correlated sensors. Then, an algorithm based on a cost function considering both distance and communication cost selects the candidate sensors, leading to the optimized network design and acquisition. Candidate sensors can then be deployed and/or queried instead of the whole network, thus reducing the network cost and extending its lifetime in terms of energy consumption. Experiments, performed on a real wireless sensor network, demonstrate the adaptability and the effectiveness of the proposed approach in optimizing the sensor network design and the data acquisition.

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