A Self-Adaptive Bluetooth Indoor Localization System using LSTM-based Distance Estimator

Zhuo Li, Jiannong Cao, Xiulong Liu, Jiuwu Zhang, Haoyuan Hu, Didi Yao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


In recent years, there is an increasing demand for indoor localization services with the aim to locate people and objects inside buildings. However, localization accuracy is susceptible to inaccurate and high variant sensor measurements due to the unpredictable fluctuations of received wireless signals and the sensitivity of hardware devices. To address this issue, in this paper, we establish a new Bluetooth indoor localization system, whose architecture can be basically decomposed into two parts: the internet-of-things (IoT) framework and the localization module. Concretely, the IoT platform uses the state-of-the-art light weight Spring Boot microservice framework consisting of multi-layer structure. In the localization module, it follows the general process of trilateration but significantly distinguished from it. A set of measures are adopted to strengthen the system's robustness when obtained measurements cannot be fully trusted. Specifically, in the first place, rather than using conventional propagation model to predict the distance between Bluetooth transmitter and receiver, we design a bran-new LSTM-based distance estimator which can better depict the nonlinearity of attenuation characteristics of radio signal. Moreover, we also employ a series of self-adaptive mechanisms, including elastic radius intersecting, multiple weighted centroid localization and self-adaptive Kalman tracking, to make the system robust against inaccurate measurements and unpredictable sudden variation of received wireless signal. A bunch of tests are conducted in both ideal lab environment and Alibaba's large-scale warehouse, and experimental results show our indoor localization system outperforms the state-of-the-art benchmarks by a large margin in both localization accuracy and stability.

Original languageEnglish
Title of host publicationICCCN 2020 - 29th International Conference on Computer Communications and Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728166070
Publication statusPublished - Aug 2020
Event29th International Conference on Computer Communications and Networks, ICCCN 2020 - Honolulu, United States
Duration: 3 Aug 20206 Aug 2020

Publication series

NameProceedings - International Conference on Computer Communications and Networks, ICCCN
ISSN (Print)1095-2055


Conference29th International Conference on Computer Communications and Networks, ICCCN 2020
Country/TerritoryUnited States


  • Bluetooth indoor localization
  • LSTM-based distance estimator
  • Self-adaptive Kalman tracking

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

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