A Novel Algorithm for Mining Association Rules in Wireless Ad Hoc Sensor Networks

In this paper, we propose a comprehensive framework for mining wireless ad hoc sensor networks (WASNs), which is able to extract patterns regarding the sensors´ behaviors. The main goal of determining behavioral patterns is to use them to generate rules that will improve the WASN´s quality of service by participating in the resource management process or compensating for the undesired side effects of wireless communication. The proposed framework consists of 1) a formal definition of sensor behavioral patterns and sensor association rules, 2) a novel representation structure that we refer to as the positional lexicographic tree (PLT) that is able to compress the data gathered for the mining process and thus allows the fast and efficient mining of sensor behavioral patterns, and 3) a distributed data extraction mechanism to prepare the data required for mining sensor behavioral patterns. Several experimental studies have been conducted to evaluate our PLT structure and our proposed data extraction algorithms for mining wireless sensor networks.