ABSTRACT The Internet of Things (IoT) has witnessed rapid global adoption, driving the development of intelligent networks that provide smart services and computing at the network edge. This paper introduces a Multi‐Layered Aggregation and Lightweight Prediction Framework that integrates data aggregation and data prediction methods, specifically designed for IoT‐based Wireless Sensor Networks (WSNs). The framework first employs temporal and spatial data aggregation (TDA and SDA) to minimize transmissions between cluster member nodes (CMNs) and cluster heads (CHs). It then applies a lightweight data prediction (LDP) model, based on linear extrapolation with adaptive correction, to further reduce data transfer volume between CHs and the base station (BS). Unlike approaches relying solely on aggregation or prediction, the proposed framework leverages their synergy to achieve significant energy savings and prolong network lifetime. Experimental validation using real‐world LUYF data confirms its superiority over state‐of‐the‐art data reduction techniques, demonstrating simplicity, low computational overhead, and effective transmission reduction. Despite its lightweight design, LDP remains reliable and versatile, seamlessly integrating with various cluster‐based data aggregation schemes. Overall, the proposed framework preserves data integrity and quality while conserving energy and extending the operational lifespan of WSNs.

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 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.