A distributed framework for discovering associated patterns from sensor data in industry applications

Mining useful information from a large amount of data generated in wireless sensor networks (WSNs) is a challenging task in many applications. Existing associated sensor patterns (ASPs) techniques assume that the data structure of the mining task is small enough to fit in the main memory of a single processor. However, WSNs are an integral part of the Internet of Things (IoT) environment and will generate large volumes of data while sensors suffer from limited processing and memory constraints, which means such an assumption does not hold any longer. To resolve this, here we proposed a MapReduce based distributed framework to mine ASPs from sensor data. Extensive performance analyses show that our technique is significantly time efficient in discovering associated sensor patterns.