Correlation-Based Sensor Activity Scheduling Mechanisms for Wireless Sensor Networks

Year over year, our world becomes more and more smarter; things around us are increasingly connecting to the Internet and share information with people. This hyperconnected world leads to a new generation of Internet called the Internet of things (IoT). In such network, wireless sensor networks (WSN) constitute the eyes of the system that allow to monitor surrounding environments and sending data to a sink node for a latter analyzing. Unfortunately, the limited power energy of the sensors along with the huge amount of data collected, which are almost useless and repetitive, make saving network lifetime and decisions making are the most challenges in WSNs. In this paper, we propose two data correlation techniques followed by two scheduling mechanisms in order to switch correlated sensors into sleep/active modes. The idea behind our approach is to reduce data transmission from neighbouring sensors by eliminating data redundancy thus, saving the sensor energy and enhancing the network lifetime. The first correlation technique aims to search the similarity between datasets based on the variation between sensed data while the second one is based on the data correlation matrix. Through simulation on real sensor data, we evaluated the efficiency of our approach in terms of maintaining the node energies, ensuring zone coverage and preserving the integrity of the information.