Energy-efficient scheduling strategies for minimizing big data collection in cluster-based sensor networks

Today, wireless sensor networks (WSNs) have been widely used in monitoring various applications, such as environment, military and health-care, etc. The explosive growth of the data volume generated in these applications has led to one of the most challenging research issues of the big data era. To deal with such amounts of data, exploring data correlation and scheduling strategies have received great attention in WSNs. In this paper, we propose an efficient mechanism based on the Euclidean distance for searching the spatial-temporal correlation between sensor nodes in periodic applications. Based on this correlation, we propose two sleep/active strategies for scheduling sensors in the network. The first one searches the minimum number of active sensors based on the set covering problem while the second one takes advantages from the correlation degree and the residual energy of the sensors for scheduling them in the network. Our mechanism with the proposed strategies were successfully tested on real sensor data. Compared to other existing techniques, the simulation results show that our mechanism significantly extends the lifetime of the network while conserving the quality of the collected data and the coverage of the monitored area.