A Data Predication Model for Integrating Wireless Sensor Networks and Cloud Computing
Samer Samarah · Procedia Computer Science · 2015
Cloud computing has been proved to be a promising solution for managing and processing big data by providing a data center centric and efficient algorithms for managing and organizing the data. One of the cloud system's data sources is Wireless Sensor Networks (WSNs). WSNs present a new way of data-stream sources in which data is received periodically from different sensors; resulting in a large amount of data accumulated over a short period. WSNs have limited resources in which a fine-detailed data streams lead to exhaustive energy consumption. In this paper, we propose a data prediction model that is built within the sensor nodes and used by the cloud system to generate the data. The purpose of the proposed model is to exempt the sensor nodes from sending a large amount of data and thus reduces the energy consumption of the sensor's battery. We manage to formulate the prediction model as a line equation through two n-dimensional vectors in n-space. Initial results showed that the proposed model will be capable to achieve a better error rate as compared to traditional data prediction techniques.