A Strategy for Elimination of Data Redundancy in Internet of Things (IoT) Based Wireless Sensor Network (WSN)

In order to give a complete description of an environment or to make a robust decision, a number of observations must be collected and combined from multiple sensor nodes. In these large collections of data, only some are useful, whereas others are redundant. This redundancy decreases performance in terms of computing overhead, excessive transmission, and covering a large space. The process of selecting and analyzing the useful information from the collection of sensed data is called mining. Mining is used to produce more consistent, accurate, and useful information than that provided by any individual sensor node. Data mining has been widely applied in many areas, such as object recognition, wireless sensor networks (WSNs), image processing, environment mapping, and localization. Nowadays, Internet of Things utilizes WSN as a necessary platform for sensing and communication of the data. For efficiency, mining of spatial and temporal data is performed on the sensed sample collected by sensor nodes. Therefore, in this paper, a redundancy removal strategy is proposed, which performs mining on collected data to select the appropriate information before forwarding to a base station or a cluster head in the WSN. Extensive simulations were conducted, and the related results showed that the proposed scheme had better performance compared to other schemes in our simulated scenarios.