DASS: Distributed Adaptive Sparse Sensing

TL;DR

This paper introduces DASS, an adaptive sparse sensing method for wireless sensor networks that learns signal models on-the-fly to optimize sampling, significantly reducing energy consumption while maintaining high data reconstruction quality.

Contribution

DASS is a novel adaptive sparse sensing approach that minimizes sensing energy by learning signal models dynamically without requiring inter-node communication.

Findings

Significant energy savings demonstrated on real datasets.

Outperforms traditional and fixed sparse sensing schemes.

Effective in scenarios with strong temporal or spatial correlations.

Abstract

Wireless sensor networks are often designed to perform two tasks: sensing a physical field and transmitting the data to end-users. A crucial aspect of the design of a WSN is the minimization of the overall energy consumption. Previous researchers aim at optimizing the energy spent for the communication, while mostly ignoring the energy cost due to sensing. Recently, it has been shown that considering the sensing energy cost can be beneficial for further improving the overall energy efficiency. More precisely, sparse sensing techniques were proposed to reduce the amount of collected samples and recover the missing data by using data statistics. While the majority of these techniques use fixed or random sampling patterns, we propose to adaptively learn the signal model from the measurements and use the model to schedule when and where to sample the physical field. The proposed method requires minimal on-board computation, no inter-node communications and still achieves appealing reconstruction performance. With experiments on real-world datasets, we demonstrate significant improvements over both traditional sensing schemes and the state-of-the-art sparse sensing schemes, particularly when the measured data is characterized by a strong intra-sensor (temporal) or inter-sensors (spatial) correlation.