Characterizing multi-hop localization for Internet of things

Deployments over large geographical areas in the Internet of Things (IoT) pose a major challenge for single-hop localization techniques, giving rise to applications of multi-hop localizations. And while many proposals have been made on implementations for multi-hop localization, a close understanding of its characteristics is yet to be established. Such an understanding is necessary, and is inevitable in extending the reliability of location based services in IoT. In this paper, we study the characteristics of multi-hop localization and propose a new solution to enhance the performance of multi-hop localization techniques. We first examine popular assumptions made in simulating multi-hop localization techniques, and offer rectifications facilitating more realistic simulation models. We identify the introduced errors to follow the Gaussian distribution, and the estimated distance follows the Rayleigh distribution. We next use our simulation model to characterize the effect of the number of hops on localization in both dense and sparse deployments. We find that, contrary to common belief, it is better to use long hops in sparse deployments, while short hops are better in dense deployments – despite the traffic overhead. Finally, we propose a new solution that decreases and manages the overhead generated during the localization process.