Efficient Data Aggregation Approaches over Cloud in Wireless Sensor Networks

Abstract

In future wireless sensor network (WSNs) scenarios, the mobility is emerging as an important feature with increased number of sensors. Multifarous obstacles in this research are being encountered as the deployments in sensor networks are growing. However, these issues can be shielded from the software developer in by integrating the solutions into a layer of software services. The Data is ever growing which demands efficient data handling algorithms. In this paper, we propose a technique in which sensed data will be stored over cloud and different data aggregation techniques like clustering and classification will be used to process such big data on the cloud. This will reduce the computation overload on the base station as the data is stored and processed on cloud itself. Clustering is used to omit the abrupt values and cluster the similar data together. Classification algorithms are used for reaching to a final conclusion. A predictive Markov chain model was also developed for the prediction of overall weather outlook. Then a concept of weather forecasting, called Long Range Forecasting was used to predict the exact numeric values of the future weather parameters.