Signal Reconstruction from Rechargeable Wireless Sensor Networks using Sparse Random Projections

Due to non-homogeneous spread of sunlight, sensing nodes possess non-uniform energy budget in rechargeable Wireless Sensor Networks (WSNs). An energy-aware workload distribution strategy is therefore necessary to achieve good data accuracy subject to energy-neutral operation. Our previously proposed Energy Aware Sparse approximation Technique (EAST) can approximate a signal, by adapting sensor node sampling workload according to solar energy availability. However, the major shortcoming of EAST is that it does not guarantee an optimal sensing strategy. In other words EAST offers energy neutral operation, however it does not offer the best utilization of sensor node energy, which compromises the reconstruction accuracy. In order to overcome this shortcoming, we propose EAST which, maximizes the reconstruction accuracy subject to energy neutral operations. We also propose a distributed algorithm for EAST, which offers accurate signal reconstruction with limited node to-base communications.