A Multi‐Layered Aggregation and Lightweight Prediction Framework for IoT ‐Based WSNs
Khushboo Jain, Arun S. Agarwal, Laxman Singh, Sreesh Gaur · Concurrency and Computation Practice and Experience · 2025
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.