Efficient handling of Big Data Analytics in Densely Distributed Sensor Networks

The elaboration of wireless sensor networks has reached a point where each specific node of a network may store and convey a massive amount of (sensor-based information at once or terminated time). Hence in the forthcoming future, densely linked, enormously dynamic distributed sensor networks such as vehicle-2-vehicle communication setups may hold even greater knowledge potency. This is often due to the increase in node complexity. Subsequently, data volumes will become a problem for traditional data aggregation strategies traffic-wise as well as with regard to energy efficiency. For that reason, in this paper we suggest to call such scenarios as big data scenarios, they pose similar questions and problems as traditional big data concepts and granting the major focus mostly on business intelligence difficulties. Consequently our scheme would be propose an aggregation strategy tied to technological prerequisites which enable the efficient use of energy and the handling of large data volumes in an open source Hadoop frameworks with single/multi clustered architectures. Together with, we demonstrate the energy conservation potential based on experiments with actual sensor platforms in a distributed context.