A two‐vector <scp>data‐prediction</scp> model for <scp>energy‐efficient data‐aggregation</scp> in wireless sensor network

Most ecological management applications use wireless sensor networks (WSNs) to collect data regularly, with great temporal redundancy. As a result, significant amount of energy is used transmitting redundant data, making it tremendously problematic attain satisfactory network lifetime, which bottleneck in enduring such environmental monitoring applications. A two-vector prediction model that based on normalized quantile regression (NQR) proposed proficiently accomplish reduction synchronous collecting cycles. The introduced NQR algorithm provides high-accuracy prediction. With accurate estimates and reduced transmission, usage reduced. Furthermore, extends the network's lifetime. In intracluster transmissions, uses data-prediction coordinate estimated sensor's reading, and, as will minimize cumulative inefficiencies from uninterrupted predictions. can be integrated both homogeneous heterogeneous WSNs. When compared state-of-art methods, suggested methodology shown have high efficiency, greater accuracy, more positive predictions quality, help last longer.