Adaptive data collection approach based on sets similarity function for saving energy in periodic sensor networks

Hassan Harb, Abdallah Makhoul, Ali Jaber, Rami Tawil and Oussama Bazzi

International Journal of Information Technology and Management, 2016, vol. 15, issue 4, 346-363

Abstract: Disaster monitoring becomes a requirement for collecting and analysing data in order to offer a better disaster management situation. Periodic sensor networks (PSNs) are usually used in disaster monitoring and are characterised by the acquisition of sensor data from remote sensor nodes before being forwarded to the sink in a periodic basis. The major challenges in PSN are energy saving and collected data reduction in order to increase the sensor network lifetime and to ensure a long-time monitoring for disasters. In this paper, we propose an adaptive sampling approach for energy-efficient periodic data collection in sensor networks. Our proposed approach provides each sensor node the ability to identify redundancy between collected data over time, by using similarity functions, and allowing for sampling adaptive rate. Experiments on real sensors data show that our approach can be effectively used to conserve energy in the sensor network and to increase its lifetime, while still keeping a high quality of the collected data.

Keywords: disaster monitoring; periodic sensor networks; PSNs; adaptive sampling; data collection; energy saving; sampling rate; similarity functions; disaster management; emergency management; sensor data; remote nodes; sensor nodes; network lifetime; energy efficiency; energy consumption; energy conservation. (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=79603 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijitma:v:15:y:2016:i:4:p:346-363

Access Statistics for this article

More articles in International Journal of Information Technology and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().