Signal Reconstruction from Rechargeable Wireless Sensor Networks using Sparse Random Projections

TL;DR

This paper introduces EAST and EAST+ algorithms for energy-aware signal reconstruction in rechargeable wireless sensor networks, optimizing sampling based on solar energy to improve accuracy and sensor lifetime.

Contribution

First to apply sparse approximation for energy-aware workload distribution in rechargeable WSNs, enhancing data accuracy and sensor on-time.

Findings

EAST improves approximation accuracy by ~50%

EAST+ computes optimal measurements using only energy budget

Experimental results validate energy-neutral operation and efficiency

Abstract

Due to non-homogeneous spread of sunlight, sensing nodes possess non-uniform energy budget in recharge- able Wireless Sensor Networks (WSNs). An energy-aware workload distribution strategy is therefore nec- essary to achieve good data accuracy subject to energy-neutral operation. Recently proposed signal approx- imation strategies assume uniform sampling and fail to ensure energy neutral operation in rechargeable wireless sensor networks. We propose EAST (Energy Aware Sparse approximation Technique), which ap- proximates a signal, by adapting sensor node sampling workload according to solar energy availability. To the best of our knowledge, we are the first to propose sparse approximation to model energy-aware workload distribution in rechargeable WSNs. Experimental results, using data from an outdoor WSN deployment suggest that EAST significantly improves the approximation accuracy offering approximately 50% higher sensor on-time. EAST requires the approximation error to be known beforehand to determine the number of measure- ments. However, it is not always possible to decide the accuracy a-priori. We improve EAST and propose EAST+, which, given only the energy budget of the nodes, computes the optimal number of measurements subject to the energy neutral operation.