Energy-efficient sensor selection for data quality and load balancing in wireless sensor networks

It is common to deploy stationary sensors in large geographical environments for monitoring purposes. In such cases, the monitored data are subject to data loss due to poor link quality or node failures. Fortunately, the sensing data are highly correlated both spatially and temporally. In this paper, we consider such networks in general, and jointly take into account the link quality estimates, and the spatio-temporal correlation of the data to minimise energy consumption by selecting sensors for sampling and relaying data. In particular, we propose a multi-phase adaptive sensing algorithm with belief propagation protocol (ASBP), which can provide high data quality and reduce energy consumption by turning on only a small number of nodes in the network. We explore the correlation of data, formulate the sensor selection problem and solve it using constraint programming (CP) and greedy search. Bayesian inference technique is used to reconstruct the missing sensing data. We show that while maintaining a satisfactory level of data quality and prediction accuracy, ASBP successfully provides load balancing among sensors and preserves 80% more energy compared to the case where all sensor nodes are actively involved.