Energy-Efficient Dual Prediction-Based Data Gathering for Environmental Monitoring Applications

Abstract

How to prolong the lifetime of wireless sensor networks is an important issue in designing environmental monitoring applications. In this paper, we propose a novel energy-efficient data gathering algorithm, called EDP, which is based on dual prediction. Both sensor nodes and the sink node use the same prediction algorithm based on the same historical data. Each sensor node predicts the values of the data to be sensed according to its recorded historical information. When getting the sensory data, the node compares it with the predicted data. Only when the difference between the predicted and the sensed data values exceeds a pre-defined threshold, the sensor node sends the newly sensed data to the sink node. For the sink node, during a reporting period, if it receives data from a sensor node, the received data will be used as the "sensed data"; otherwise, the sink node will use the prediction algorithm to get the "predicted value" in the same way as the sensor node does. Theoretical analysis and simulation studies show that EDP can greatly reduce the amount of data transmitted in the network, leading to significant energy saving on the sensor nodes and extension of the lifetime of the entire network.