Implementation of Data Mining in Wireless Sensor Networks: An Integrated Review

Abstract

-Current years have witnessed the emergence of wireless sensor networks (WSNs) as a new information-gathering paradigm, in which a large number of sensors scatter over a examination field and extract data of interests by reading real-world phenomena from the physical environment. Energy consumption becomes a primary concern in a WSN, as it is crucial for the network to functionally operate for an probable period of time. The WSN's extraordinary characteristics direct us to innovative research challenges in some data mining process. Data mining is one of the most important methods by which useful patterns in data with minimal user interference are known and available information of users and analysts to make decisions relayed on their vital organizations to adopt. Data mining, as the continuance of multiple intertwined disciplines, consisting statistics, machine learning, pattern recognition, database systems, information retrieval, World-Wide Web, visualization, and lots of application domains, has made great progress in the past decade. To ensure that the advances of data mining research and technology will competently benefit the progress of science and engineering, it is important to scrutinize the challenges on data mining posed in data-intensive science and engineering and explore how to further develop the technology to assist new discoveries and advances in science and engineering. In WSNs, hierarchical network structures have the advantage of supplying scalable and energy efficient solutions.