Abstract
Wireless Sensor Network (WSN) is one of the fundamental technologies used in the Internet of Things (IoT) which is deployed for diverse applications to carry out precise real-time observations. The limited resources of WSN with massive volume of fast-flowing IoT data make the aggregation and analytics of data more challenging. Recently, data mining-based solutions have been proposed to effectively handle the data being generated by the sensors and to analyze the data patterns for deducing the required information from it. The increasing need of these techniques motivated us to propose a distributed and efficient data mining technique that not only handles the massive and rapidly generated data by the nodes, but also increases the life span of the network. In this paper, we propose a novel scheme for the IoT based WSN that mines the sensor data using association rule without moving it to any Cluster Head (CH) or Base Station (BS). The new proposed scheme enables sensors to perform computations locally and only the minimum higher-level statistical summaries of the data at Cluster Members (CMs) are exchanged with their CH. This considerably reduces the communication overhead which ultimately prolongs the network lifetime. The proposed scheme is evaluated via extensive simulations and the results obtained demonstrate that the integration of the proposed scheme in the existing protocols significantly reduces the communication overhead which ultimately prolongs the network lifetime and stability.