A New Framework for Effective Processing of WSN Application Generated Big Data

In real word automation applications needs Wireless Sensor Networks (WSNs) for many controlling or observation activities. In Zig-bee based WSN, end node sensors collect the data continuously from its’ surroundings or objects and transfer to the sink node directly or with the help of anchor nodes (routers). Data will be accumulated from long periods at the sink node. WSN generated data has the characteristics of volume, velocity, variety, variability, and veracity. This kind of data characteristics is similar to big data. Localization is an important activity in the WSN applications. Localization can give location information for the applications to provide effective services. WSN generated big data characteristics make it difficult to process using traditional database or software techniques. To process such kind of big data, Text Analytics, Audio/Video Analytics, Social Media Analytics and Predictive Analytics are some of the traditional techniques. The traditional technologies have their own limitations like Hardware: Hardware performance and capacity, Data accessing: Size of data accessing, Data computation: Data distribution and processing. In this paper, a novel framework was proposed to overcome the limitations of traditional techniques.