UBETI: A DV-Hop Localization Algorithm for IoT With Distance Upper Bound Estimation by Triangle Inequality

Internet of Things (IoT) localization is crucial in smart applications. Distance vector hop (DV-Hop)-based algorithms offer cost-effective and scalable solutions relying on connectivity information. However, most algorithms have limitations in irregular areas, including a tendency to overestimate the distances between nodes. To address this issue, we propose distance upper bound estimations by triangle inequality (UBETI) to adjust the DV-Hop objectives and mitigate the distance overestimation in irregular areas. In addition, we propose a crossover strategy for the genetic algorithms (GAs) in the optimization, named stats-crossover (ST-CR). ST-CR generates the offspring of the population leveraging the statistical data of the parent population and improves the optimization performance. The two proposed strategies can be incorporated into existing DV-Hop algorithms and boost their performance. Evaluation results show that compared with other highly regarded methods, the proposed algorithm has a meaningful improvement, particularly in irregular areas. Ablation studies demonstrate the effectiveness of each step in the proposed algorithm.