An Energy Efficient Data Architecture for Wireless Sensor Networks

We live in a technological generation surrounded by interconnected sensors that can collect and distribute immense amounts of data on a daily basis. These data would have a better connotation and would have been more practical if sensor-based networks allowed us to capture and monitor the characteristics of physical objects from a highly dynamic environment. At this point, sensor-based networks could substantially enhance their applicability if machines process and interpret vast amounts of data correctly, an essential characteristic of scalable and interoperable wireless sensor network architectures. Through this research project, a) We will identify and evaluate wireless sensor network architectures enabling ap-
plications from a highly dynamic environment. Then a data architecture will be proposed to enhance machine-to-machine (M2M) communication and human understanding, considering the issues and challenges of sensor networks. The future proposed data architecture will overcome the existing data frameworks’ limitations identified in the literature review. b) The significant contribution of the research is to propose energy-efficient data collection models (the first layer of data architecture) that will reduce data transmissions using prediction models between nodes in sensor networks. The proposed models intend to predict values at the sink node using coefficients built and transmitted by sensor platforms. The goal is to build models that improve the energy of battery-powered sensory devices by reducing data
transmissions and recovering values at sink nodes using the same coefficients of models while
ensuring data integrity. Furthermore, the models are evaluated using real data sets from real
sensor networks with the following metrics; RMSE, MAE, MSE, data reduction percentage,
and energy savings.