A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks

TL;DR

This paper introduces a novel spatial-temporal correlation-based data reduction method for sensor networks that reduces energy consumption by up to 60% while maintaining data quality through adaptive sampling and reconstruction.

Contribution

It presents a new data reduction scheme leveraging spatial-temporal correlations and a back-end reconstruction algorithm, improving energy efficiency and handling non-stationary data effectively.

Findings

Reduces energy consumption by up to 60%.

Effectively handles non-stationary data.

Outperforms recent adaptive sampling methods.

Abstract

In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme, that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. This strategy reduces the overall sampling/transmission rates while preserving the quality of the data. Moreover, a back-end reconstruction algorithm is deployed on the workstation (Sink). This algorithm can reproduce the data that have not been sampled by finding the spatial and temporal correlation among the reported data set, and filling the 'non-sampled' parts with predictions. We have used real sensor data of a network that was deployed at the Grand-St-Bernard pass located between Switzerland and Italy. We tested our approach using the previously mentioned data-set and compared it to a recent adaptive sampling based data reduction approach. The obtained results show that our proposed method consumes up to 60% less energy and can handle non-stationary data more effectively.