With increasing computational utilities in recent times, the need for systems capable of processing immense and voluminous amount of information has witnessed an exponential rise. Concepts of cloud computing and Big D...
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With increasing computational utilities in recent times, the need for systems capable of processing immense and voluminous amount of information has witnessed an exponential rise. Concepts of cloud computing and Big Data have proved to be promising solutions to the emerging problems of handling huge volume of data. Big data finds utilities over a wide range of applications starting from sensor networks, data processing involving image acquisition of high-definition data (HD), predictor models involving huge volumes of historical data, classification models involving sentimental analysis, emotion analysis etc. A classic case of big data processing in sensor networks for an IoT environment has been investigated in this research paper with the sole objective of tuning the network performance using a dynamic fuzzy based model. The experimentation has been conducted on a wearable technology database based on IoT comprising of several sensors incorporated in a WBAN (Wireless body area network) technology. Superior performance is reported in the form of improved computation time, accuracy, precision when compared with the big data model without fuzzy engine.
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