An object tracking scheme for wireless sensor networks using data mining mechanism

Recently, object tracking application of sensor networks has drawn significant attention of the researchers due to its wide application. However, most of these studies cannot deal with the trade-off between energy efficiency and accuracy of the tracking. In object tracking sensor networks (OTSNs), the movement of the object generally follows some definite patterns. The moving object location, arrival time, path are likely to hide some useful association rules, which can be excavated by applying suitable data mining algorithm. In this paper, we have proposed an object tracking scheme for OTSNs using data mining approach. We have improved the Apriori algorithm for mining association rules and made it applicable to the OTSNs. The data mining algorithm is applied to the past movement information of the object and useful association rules are excavated, which are then used to predict the next location of the object. Our scheme predicts the next location of the object more accurately and increases the network lifetime. Experimental results have been conducted to evaluate the performance of our proposed scheme for OTSNs and they show that our scheme outperforms the existing schemes in terms of energy efficiency and accuracy of tracking.