Axis 2 – Monitoring and Prediction of Complex Systems:

Energy-saving data collection in wireless sensor networks


The use of wireless sensor networks is attracting much attention worldwide in a multitude of domains like environment, industry, medicine… It enables for instance a continuous monitoring and analysis of industrial processes, so as to control and maintain their best operating performance. But, these networks nowadays imply staying afloat with the data deluge delivered by the sensors while taking care of the subsequent increase in energy consumption. This calls for increased attention to build efficient data collection, management, processing, and stewardship strategies.

Currently deployed sensor networks typically gather data and transmit them periodically to the end user. Instead, a novel approach by EIPHI researchers, published in the Feb. 2018 issue of IEEE Transactions on Industrial informatics, proposes an adaptive statistical data collection that finally entails each sensor node to adjust its sampling rate (instead of being periodic) to the variation of its environment. Both simulation and experimentations on compliant wireless electronic motes were performed, proving that this adaptive method effectively reduces the number of acquired samples up to 80%, with significant energy savings and high accurate data collection compared to existing fixed-rate approaches.


Hassan Harb and Abdallah Makhoul, “Energy-Efficient Sensor Data Collection Approach for Industrial Process Monitoring”, IEEE Transactions on Industrial Informatics 14, 2, 661-672 (Feb. 2018). DOI: 10.1109/TII.2017.2776082