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

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.