An efficient data fusion and assurance mechanism using temporal and spatial correlations for home automation networks

Abstract

In this paper, we propose an efficient and accurate data fusion mechanism through temporal and spatial correlations for Home Automation Networks. Data fusion is a process to decrease the transmission number of similar sensory values, thus its importance is in the spotlight because energy efficiency is a main issue on the networks. In order to reduce the transmission amount of data, each device decides whether to send new sensing data or not after comparing it with previous sensing values(temporal correlation) and the last transmitted value of the neighbor device(spatial correlation) which is a one-hop range via a predetermined user threshold. Our mechanism includes a commit and assurance process for security enhancement. For the performance evaluation of our mechanism, we use the temperature data measured on real networks and extend them using Gaussian distribution in order to obtain the more test data sets. The simulation results show that using both types of correlations is more efficient than just using one type for data fusion in terms of the data transmission amount and accuracy. Moreover, our simulation for the commit and assurance process demonstrates only slightly more energy.