Dual-Level Data Compression and Reduction in IoT Networks: Enhancing Efficiency through Sensor Node and Gateway Optimization

The proliferation of Internet of Things (IoT) devices has led to unprecedented challenges in data management and transmission efficiency. This paper introduces a novel dual-level approach to data compression and reduction in IoT networks, addressing these challenges by optimizing data handling at both sensor node and gateway levels. Unlike conventional single-level techniques, our method provides a more comprehensive solution to data optimization in IoT ecosystems. At the sensor node level, we implement Run Length Encoding (RLE) compression, while at the gateway level, we integrate progressive data clustering and reduction strategies. This dual-level approach demonstrates significant improvements in data reduction compared to single-point optimization methods. Our comparative analysis reveals the synergistic effects of combining node-level compression with gateway-level data reduction, resulting in enhanced overall system performance. We evaluate the scalability and adaptability of our approach across diverse IoT applications, considering energy efficiency implications for resource-constrained devices. Additionally, we discuss alternative data reduction techniques such as attribute reduction, canonical polyadic decomposition, and edge computing-based filtering, contextualizing our method within the broader landscape of IoT data optimization. The paper also examines the impact of our approach on network bandwidth utilization and explores future directions for multi-level data optimization in complex IoT architectures. Our findings suggest that the proposed dual-level method offers a promising solution for managing the growing data challenges in IoT networks, paving the way for more efficient and scalable IoT systems.