Information prediction in sensor networks using Milne-Simpson's scheme

Wireless sensor network (WSN) is used to collect physical information from the environment at real time. The information may be temperature, humidity and air pressure. In modern days, the huge number of wireless sensors are distributed in the physical environment. So, the proper power management scheme is necessary for WSN. Interestingly, by using prediction algorithms in the literature, we can predict the future data and compare the predicted data with actual information. In this approach, if the absolute value is within the threshold value, then we can save power by not sending the actual measurements to the base station as the base station is already equipped with similar data prediction algorithm. Previous works are done on this problem by using Simpson 3/8 method and Kalman filter algorithm. Unfortunately, they are not very efficient when the threshold value is small. To maximize the power savings for smart sensors, we are proposing a Milne Simpsons algorithm for prediction and estimation of the transmitted signals. With this method, the prediction accuracy is higher than existing methods. With our proposed approach, the data prediction accuracy rate will be high, resulting in low power consumption in wireless networks.