Learning-based Smart Sensing for Energy-Sustainable WSN
Sushmita Ghosh, Swades K. De, Shouri Chatterjee, Marius Portmann · 2021
Wireless sensors networks (WSNs) are gaining enormous attention for monitoring physical conditions in various application. WSNs equipped with power-hungry senors often suffer from energy sustainability. Hence, an efficient smart sensing approach is required to enhance the energy sustainability of such WSNs. A wireless node equipped with a sensor monitoring the variation of a particular parameter in time often exhibits high temporal correlation that can be studied to smartly sense the parameter. To optimize the energy consumption of these sensors and increase the network lifetime, this paper presents a learning- based adaptive sampling framework that explores the sparsity in the time series data and finds optimal sampling instants for the next measurement cycle. Principal component analysis (PCA) is used to sparsify the time domain signal and the sparse signal is reconstructed from its low-dimensional signal using the sparse Bayesian learning (SBL) method. An optimization function is formed that solves the trade-off between accuracy and energy consumption and finds the optimal sampling instants for the next measurement cycle. The performance of the proposed adaptive sampling framework is tested on air pollution monitoring dataset. The simulation results validate the energy efficiency of the proposed method. Compared to the existing adaptive sampling algorithms the proposed learning-based algorithm saves up to 58% energy with a marginally higher computational complexity while maintaining an acceptable range of sensing error.