A Hybrid Model for Data Prediction in Real-World Wireless Sensor Networks

Abstract

Data prediction is proposed in wireless sensor networks (WSNs) to extend system lifetime by avoiding transmissions of redundant messages. Existing prediction-based approaches can be classified into two types. One focuses on historical data reconstruction and proposes backward models, which incur uncontrollable delay. The other focuses on the future data prediction and proposes forward models, which require additional transmissions. This letter proposes a hybrid model with the capabilities of both historical data reconstruction and future data prediction to avoid additional transmission and control delay. Two algorithms are proposed to implement this model in real-world WSNs. One is a stagewise algorithm for sensor nodes to build optimal models. The other is for the sink to reconstruct and predict sensed values. Two WSN applications are simulated based on three real data sets to evaluate the performances of the hybrid model. Simulation results demonstrate that the proposed approach has high performance in terms of energy efficiency with controllable delay.