Data Mining and Fusion Techniques for WSNs as a Source of the Big Data

The wide adoption of the Wireless Senor Networks (WSNs) applications around the world has increased the amount of the sensor data which contribute to the complexity of Big Data. This has emerged the need to the use of in-network data processing techniques which are very crucial for the success of the big data framework. This article gives overview and discussion about the state-of-the-art of the data mining and data fusion techniques designed for the WSNs. It discusses how these techniques can prepare the sensor data inside the network (in-network) before any further processing as big data. This is very important for both of the WSNs and the big data framework. For the WSNs, the in-network pre-processing techniques could lead to saving in their limited resources. For the big data side, receiving a clean, non-redundant and relevant data would reduce the excessive data volume, thus an overload reduction will be obtained at the big data processing platforms and the discovery of values from these data will be accelerated.