Abstract
Most ecological management applications use Wireless Sensor Networks (WSNs) to collect data regularly, with great temporal redundancy. As a result, a significant amount of energy is used transmitting redundant data, making it tremendously problematic to attain a satisfactory network lifetime, which is a bottleneck in enduring such environmental monitoring applications. A two-vector prediction model based on Normalized Quantile Regression (NQR) for Data Aggregation is proposed to proficiently accomplish energy reduction in synchronized data collecting cycles. The introduced NQR algorithm provides high-accuracy prediction. With accurate estimates, energy usage is reduced.Furthermore, it extends the network's lifetime. In intracluster transmissions, NQR uses a two-vector data-prediction algorithm to coordinate the anticipated sensor's reading and, as a result, minimize cumulative inefficiencies from unin-terrupted predictions. NQR algorithm can be integrated with both homogeneous and heterogeneous WSNs. When compared to existing methods, the suggested NQR methodology is shown to have high energy efficiency.The results show greater prediction accuracy, more positive predictions with high data quality, which help the network last longer.
An energy-efficient data prediction and processing approach for the internet of things and sensing based applications
