LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Adaptive Sampling Approach Exploiting Spatio-Temporal Correlation and Residual Energy in Periodic Wireless Sensor Networks

Photo from wikipedia

Energy limitation is a major issue in wireless sensor networks where a high volume of redundant data is collected periodically and transmitted through the network. Therefore, efficient energy consumption is… Click to show full abstract

Energy limitation is a major issue in wireless sensor networks where a high volume of redundant data is collected periodically and transmitted through the network. Therefore, efficient energy consumption is the key solution to maximize the network lifetime. This paper proposes an adaptive sampling approach based on spatio-temporal correlation of collected data and on nodes residual energy. This approach aims to optimize sampling rates of sensor nodes while ensuring a high quality of the collected data. In addition, a data reconstruction method based on linear regression is adopted in the sink to reconstruct the missing samples due to the sampling rate reduction and adaptation compared to the case of a constant maximal sampling rate. We compared our approach with recently proposed adaptive sampling benchmark methods in different scenarios of data temporal correlation. Simulation results demonstrate the effectiveness of our proposed method in optimizing energy consumption by reducing the sampling rate while maintaining data quality. Our contribution can be applied to several fields, particularly, the field of water resources management.

Keywords: adaptive sampling; wireless sensor; energy; approach; temporal correlation

Journal Title: IEEE Access
Year Published: 2023

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.