High-Accuracy and Efficient DV-Hop Localization for IoT Using Hop Loss

TL;DR

This paper introduces a novel hop loss model called distance-based connectivity consistency (DCC) for DV-Hop localization in IoT, which improves accuracy and reduces computation time by avoiding hop-count recalculations.

Contribution

The paper proposes DCC, a new hop loss modeling approach that enhances localization accuracy and computational efficiency in DV-Hop algorithms for IoT.

Findings

DCC improves localization accuracy over existing algorithms.

DCC reduces total computation time by 30% to 40%.

Theoretical proof guarantees full coverage of hop errors.

Abstract

Accurate localization is critical for Internet of Things (IoT) applications. Using hop loss in DV-Hop-based algorithms is a promising approach. Nevertheless, challenges lie in overcoming the computational complexity caused by re-calculating the predicted hop-counts, and how to further optimize the modeling for better accuracy. In this paper, a novel hop loss modeling, distance-based connectivity consistency (DCC), is proposed. By focusing on the first order connectivity, DCC avoids computing predicted hop-counts, and significantly reduces the time complexity. We also provide a proof to theoretically guarantee that this design achieves a full coverage of all hop errors. In addition, by computing a continuous loss function instead of the discrete hop-count errors, DCC further improves the localization accuracy. In the evaluations, DCC demonstrates notable improvements in accuracy over other highly regarded algorithms, and reduces 30% to 40% total computation time compared with the baseline algorithm using hop loss.