A two-vector data-prediction model for energy-efficient data-aggregation in wireless sensor network

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 data prediction model that is based on normalized quantile regression (NQR) is proposed to proficiently accomplish energy reduction in synchronous data collecting cycles. The introduced NQR algorithm provides high-accuracy data prediction. With accurate estimates and reduced data transmission, 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 estimated sensor's reading, and, as a result, it will minimize cumulative inefficiencies from uninterrupted predictions. NQR algorithm can be integrated with both homogeneous and heterogeneous WSNs. When compared to state-of-art methods, the suggested NQR methodology is shown to have high energy efficiency, greater prediction accuracy, and more positive predictions with high data quality, which help the network to last longer.