Title: Adaptive data collection approach based on sets similarity function for saving energy in periodic sensor networks
Authors: Hassan Harb; Abdallah Makhoul; Ali Jaber; Rami Tawil; Oussama Bazzi
Addresses: FEMTO-ST Laboratory, DISC Department, University of Franche-Comté, Belfort, France ' FEMTO-ST Laboratory, DISC Department, University of Franche-Comté, Belfort, France ' Department of Computer Science, Lebanese University, Beirut, Lebanon ' Department of Computer Science, Lebanese University, Beirut, Lebanon ' Department of Physics and Electronics, Lebanese University, Beirut, Lebanon
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.
International Journal of Information Technology and Management, 2016 Vol.15 No.4, pp.346 - 363
Received: 06 Nov 2014
Accepted: 24 May 2015
Published online: 22 Sep 2016 *