ABSTRACT
Distance estimation and proximity classification techniques are essential for numerous IoT applications and in providing efficient services in smart cities. Bluetooth Low Energy (BLE) is designed for IoT devices, and its received signal strength indicator (RSSI) has been used in distance and proximity estimation, though they are noisy and unreliable. In this study, we leverage the BLE TX power level in BLE models.We adopt a comparative analysis framework that utilizes our extensive data library of measurements. It considers commonly used state-of-the-art model, in addition to our data-driven proposed approach. The RSSI and TX power are integrated into several parametric models such as log shadowing and Android Beacon library models, and machine learning models such as linear regression, decision trees, random forests and neural networks. Specific mobile apps are developed for the study experiment. We have collected more than 1.8 millions of BLE records between encounters with various distances that range from 0.5 to 22 meters in an indoor environment. Interestingly, considering TX power when estimating the distance reduced the mean errors by up to 46% in parametric models and by up to 35% in machine learning models. Also, the proximity classification accuracy increased by up to 103% and 70% in parametric and machine learning models, respectively. This work is one of the first studies (if not the first) that analyze in depth the TX power variations in improving the distance estimation and classification.
- A.Kwiecien, M.Mackowski, M.Kojder, and M.Manczyk. 2015. Reliability of bluetooth smart technology for indoor localization system. In International Conference on Computer Networks. Springer, 444--454. Google ScholarCross Ref
- M. S. Aman, H. Jiang, C. Quint, K. Yelamarthi, and A. Abdelgawad. 2016. Reliability evaluation of iBeacon for micro-localization. In (UEMCON). IEEE, 1--5. Google ScholarCross Ref
- S Bertuletti, A Cereatti, U Della, M Caldara, and M Galizzi. 2016. Indoor distance estimated from Bluetooth Low Energy signal strength: Comparison of regression models.. In Sensors Applications Symposium (SAS). IEEE, 1--5. Google ScholarCross Ref
- H. Cho, J. Ji, Z. Chen, H. Park, and W. Lee. 2015. Accurate Distance Estimation between Things: A Self-correcting Approach. Open Journal of Internet Of Things (OJIOT) 1, 2 (2015), 19--27.Google Scholar
- Bluetooth Low Energy. https://www.bluetooth.com/what-is-bluetoothtechnology/ how-it-works/low-energy.Google Scholar
- Julian J Faraway. 2002. Practical regression and ANOVA using R. (2002).Google Scholar
- Here's how the Internet of Things will explode by 2020. www.businessinsider.com/iot-ecosystem-internet-of-things-forecasts-and-businessopportunities- 2016--2.Google Scholar
- Z. Jianyong, L. Haiyong, C. Zili, and L. Zhaohui. 2014. RSSI based Bluetooth low energy indoor positioning. In (IPIN). IEEE, 526--533. Google ScholarCross Ref
- J.Xu, W.Liu, F.Lang, Y.Zhang, C.Wang, et al. 2010. Distance measurement model based on RSSI in WSN. Wireless Sensor Network 2, 8 (2010), 606--616. Google ScholarCross Ref
- K.Urano, K. Hiroi, K. Kaji, and N. Kawaguchi. 2016. A Location Estimation Method using BLE Tags Distributed Among Participants of a Large-Scale Exhibition. In MOBIQUITOUS. ACM, 124--129. Google ScholarDigital Library
- Andy Liaw and Matthew Wiener. 2002. Classification and regression by random- Forest. R news 2, 3 (2002), 18--22.Google Scholar
- Android Beacon Library. https://altbeacon.github.io/android-beacon-library/.Google Scholar
- M. Al Qathrady and A.Helmy. https://www.cise.ufl.edu/~qathrady/reports/BLE.pdf.Google Scholar
- M. Al Qathrady, A. Helmy, and K. Almuzaini. 2016. Infection tracing in smart hospitals. In (WiMob). IEEE, 1--8. Google ScholarCross Ref
- Brian Ripley, William Venables, and Maintainer Brian Ripley. 2016. Package "nnet". R package version (2016), 3--7.Google Scholar
- R.Tabata, A.Hayashi Arisa, S.Tokunaga, S.Saiki, M.Nakamura, and S.Matsumoto. 2016. Implementation and evaluation of BLE proximity detection mechanism for Pass-by Framework.. In Computer and Information Science. IEEE/ACIS, 1--6. Google ScholarCross Ref
- Terry M Therneau, Beth Atkinson, Brian Ripley, et al. 2010. rpart: Recursive partitioning. R package version. R package version 3 (2010), 1--46.Google Scholar
- www.cdc.gov/hicpac/2007IP/2007ip part1.html. CDC.Google Scholar
- Y. Zhuang, J. Yang, Y. Li, L. Qi, and N. El-Sheimy. 2016. Smartphone-based indoor localization with bluetooth low energy beacons. Sensors 16, 5 (2016), 596.Google ScholarCross Ref
Index Terms
- Improving BLE Distance Estimation and Classification Using TX Power and Machine Learning: A Comparative Analysis
Recommendations
A new deep learning-based distance and position estimation model for range-based indoor localization systems
AbstractMany fine-grained indoor localization systems rely on accurate distance estimation between anchors and a target node to determine its exact position. The Received Signal Strength Indicator (RSSI) is commonly used for distance ...
Improving indoor positioning system using weighted linear least square and neural network
Indoor positioning has grasped great attention in recent years. Many of those technologies are related to the problem of determining the position of an object in space, such as the robot, people, and so on. In this paper, we combine a range-free method, ...
Real-time indoor positioning and route guidance system by using beacons
Real-time tracking of objects an indoor environment has become important in numerous industries, including shopping, logistics, advertising, and healthcare. This paper represents a smartphone-based real time indoor positioning and route guidance system ...
Comments