Review on data mining techniques in wireless sensor networks

Over the past few years, wireless sensor networks (WSN) have emerged as one of the most exciting fields in Computer Science research. A Wireless Sensor Network (WSN) is a set of sensors that are integrated with a physical environment. These sensors are small in size, and capable of sensing physical phenomenon and processing them. The main aim of deploying applications based on WSNs is to make use of the data sensed by the sensors to raise the real-time decisions. The main limitations of WSNs are characteristics of sensor nodes and nature of sensor data generated by networks. Due to these limitations, traditional data mining techniques are not suitable to WSNs. As the data generated by WSNs is highly resource-constrained, huge in volume, fast changing, it is very challenging to design suitable data mining techniques for WSNs. This inspires to explore a novel and appropriate data mining technique capable of extracting knowledge from huge volume and variety of continuously arriving data from WSNs. In this paper different existing data mining techniques adopted for WSNs are examined with different classification, evaluation approaches, Finally, some research challenges related to adopting data mining techniques in WSNs are also pointed out.