GSMDV-Hop: A Modularized and Greedy-Strategy-Based Framework for Enhanced DV-Hop Localization Accuracy and Stability

In this work, an innovative solution to address the limitations of the distance vector hop (DV-Hop) model, which fails to meet localization accuracy requirements and lacks a robust role analysis mechanism for flexible application expansion, is proposed. By integrating a pre-experimentation mechanism and a multidimensional evaluation system, the model is systematically deconstructed and improved, resulting in the development of a modularized and greedy strategy modular DV-Hop (referred to as GSMDV-Hop). The proposed approach uses pre-experimental tests to identify optimal performance functions within each module through a greedy strategy. A binary controller is introduced to seamlessly integrate these modules, enabling flexible control and forming a multidimensional evaluation framework with adjustable benchmarks and diverse performance indicators. The experimental results indicate that the incorporation of optimized modules reduces the localization error by an average of 50%, with peak optimization rates exceeding 70%. The proposed algorithm significantly outperforms existing methods in terms of optimization effectiveness, stability, and versatility, making it a compelling choice for advanced localization tasks in diverse Internet-of-Things (IoT) applications.