Energy-efficient Data Aggregation in Wireless Sensor Networks Using Probabilistic Sleep Scheduling and Compressed Sensing

Abstract

Each node in a wireless sensor network (WSN) is an inexpensive and small device with a limited source of energy. In many applications related to monitoring of physical phenomenon and natural signals, there is spatio-temporal correlation among the readings from different sensors and different times. This work takes advantage of the correlation and integrates compressed sensing and sleep scheduling to significantly reduce the energy consumption of data aggregation in WSNs. The proposed method is based on probabilistic models, allowing it to be simple, fast, flexible, reliable, and suitable for randomly deployed networks with no defined topology, known location, or time synchronization.