A Dynamic Trust Weight Allocation Technique for Data Reconstruction in Mobile Wireless Sensor Networks

Abstract

Data accuracy and low energy consumption in mobile wireless sensor networks (MWSN) are crucial attributes for real-time applications. Although there are many existing methods to reconstruct data for wireless sensor networks, there are few developed for highly mobile environments. We propose Dynamic Trust Weight Allocation Technique (DTWA), a novel in-network data reconstruction method that determines the trust level in the data accuracy of each candidate node by evaluating spatio-temporal correlations, trajectory behavior, quantity and quality of data, and the number of hops traveled by the received data from the source. DTWA is capable of evaluating second-hand data when there is no first-hand data available and selecting second-hand data when this last is more accurate than the first-hand data. Our results demonstrate that data reconstructed using DTWA depicts significantly lower Root Mean Square Error (RMSE) compared to the IMC method when tested for both low and high incomplete dataset scenarios.