BBIL: A Bounding-Based Iterative Method for IoT to Localize Things

Liangxiong Wei, Yanru Chen, Hao Wang, Miao Li, Zhenlei Liu, Yue Wang, Qian Luo, Liangyin Chen

Abstract: The Internet of Things (IoT) has become more popular over the past decade. For the IoT to be successful, it is vital to track the location of these things (sensors or actuators). In this article, based on a new IoT underlying architecture, a narrowband-IoT (NB-IoT)-aided, bounding-based iterative and range-free method, named BBIL, is proposed to localize things. We make use of the location information of all anchor regular nodes that can help improve the localization accuracy as much as possible. Specifically, not only single-hop and multihop anchor things but also single-hop and multihop regular things are used for localization. In addition, the communication and computational loads of the local network are greatly decreased because the anchor things can directly access the Internet using the NB-IoT modules; hence, data can be sent to the Internet through a small number of hops in the local network and processed in the cloud/edge computing facilities in a centralized way. To balance the location accuracy and energy consumption of BBIL, we propose a theoretical model to obtain the optimal number of the anchor things. BBIL is evaluated and compared with the existing methods. The simulation results indicate that the average localization error of BBIL is less than 11.6%. Also, it performs well in anisotropic networks. In addition, we verified the validity of our method in real-world scenario.