In this paper we present a novel cloud supported model for efficient community health awareness in presence of a large scale WBANs data generation. The objective is to process this big data in order to detect the abnormal data using MapReduce infrastructure and user defined functions with minimum processing delay. The goal is to have a large monitored data of WBANs to be available to the end user or to the decision maker in reliable manner.
The proposed work is minimizing the data processing delay by choosing cloudlet or local cloud model and MapReduce infrastructure. So, the overall delay is minimized, thus leading to detect the abnormal data in the cloud in real time mode. Performance results show that integrating the MapReduce capabilities with cloud computing model will significantly reduce the processing delay.