journal article·2021-06-30Open access

Ble Based Indoor Positioning System and Minimal Zone Searching Algorithm (MZS) Applied to Visitor Trajectories within a Museum

Richard Jérémy, Bertet Karell, Faucher Cyril

Applied Sciences· Volume 11· Issue 13

Abstract

Museums are perfect experimentation grounds for indoor positioning technologies. Indeed, museum managers are always pleased to hold these kinds of events where it offers the opportunity to the public to be a part of such experimentation and allowing us at the same time to popularize our research with them. In this paper, we describe an experiment that held within the museum of natural history of La Rochelle with a class of high school volunteers. We will explain our systems that has been built to work in this specific case, and among other things formalize our algorithm for indoor localization that has not had an equivalent in the state of the art. The minimal zone searching algorithm (MZS) can compute in real time the position of a visitor, shaped as a zone with an average surface of 3 m2 when resources are limited and when the placement of nodes must respect the constraints imposed by the room’s layout. This method offered good results with data collected during the experimentation, such as a meaningful representation of the position of a visitor and most importantly a stable execution during the whole experience even when the subject was in tight spaces.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Indoor and Outdoor Localization Technologies · Robotics and Sensor-Based Localization · Underwater Vehicles and Communication Systems

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

L3i Laboratory, 17000 La Rochelle, France

References (29)

Show source-provided references
1. A Survey of Indoor Localization Systems and Technologies10.1109/COMST.2019.2911558 ↗
2. Piccinni, G., Avitabile, G., and Coviello, G. (2016, January 10–13). An improved technique based on Zadoff-Chu sequences for distance measurements. Proceedings of the 2016 IEEE Radio and Antenna Days of the Indian Ocean (RADIO), Reunion, France.10.1109/RADIO.2016.7772043 ↗
3. Real-Time Distance Evaluation System for Wireless Localization10.1109/TCSI.2020.2979347 ↗
4. Dynamic Wireless Indoor Localization Incorporating With an Autonomous Mobile Robot Based on an Adaptive Signal Model Fingerprinting Approach10.1109/TIE.2018.2833021 ↗
5. Recent Advances in Wireless Indoor Localization Techniques and System
6. Survey of Wireless Indoor Positioning Techniques and Systems10.1109/TSMCC.2007.905750 ↗
7. Conte, G., de Marchi, M., Nacci, A.A., Rana, V., and Sciuto, D. (2014, January 3–6). BlueSentinel: A first approach using iBeacon for an energy efficient occupancy detection system. Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings (BuildSys’14), Memphis, TN, USA.10.1145/2676061.2674078 ↗
8. An Analysis of Visitors’ Behavior in the Louvre Museum: A Study Using Bluetooth Data10.1068/b130047p ↗
9. BLE Beacons for Indoor Positioning at an Interactive IoT-Based Smart Museum10.1109/JSYST.2020.2969088 ↗
10. Giuliano, R., Cardarilli, G.C., Cesarini, C., di Nunzio, L., Fallucchi, F., Fazzolari, R., Mazzenga, F., Re, M., and Vizzarri, A. (2020). Indoor Localization System Based on Bluetooth Low Energy for Museum Applications. Electronics, 9.10.3390/electronics9061055 ↗
11. Juniarta, N., Couceiro, M., Napoli, A., and Raïssi, C. (2018, January 12–14). Sequential Pattern Mining using FCA and Pattern Structures for Analyzing Visitor Trajectories in a Museum. Proceedings of the CLA 2018—The 14th International Conference on Concept Lattices and Their Applications, Olomouc, Czech Republic.
12. Kontarinis, A., Marinica, C., Vodislav, D., Zeitouni, K., Krebs, A., and Kotzinos, D. (2017, January 7–9). Towards a Better Understanding of Museum Visitors’ Behavior through Indoor Trajectory Analysis. Proceedings of the Seventh International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage (DiPP2017), Burgas, Bulgaria.10.55630/dipp.2017.7.1 ↗
13. SWiBluX: Multi-Sensor Deep Learning Fingerprint for Precise Real-Time Indoor Tracking10.1109/JSEN.2019.2892590 ↗
14. Wenger, M., Carrera, J., and Zhao, Z. (2019). Indoor positioning using Raspberry Pi with UWB. [Bachelor’s Thesis, Universität Bern].
15. An accurate fingerprinting based indoor positioning algorithm
16. Subedi, S., Kwon, G., Shin, S., Hwang, S., and Pyun, J.Y. (2016, January 5–8). Beacon based indoor positioning system using weighted centroid localization approach. Proceedings of the 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), Vienna, Austria.10.1109/ICUFN.2016.7536951 ↗
17. Dong, Q., and Dargie, W. (2012, January 28–30). Evaluation of the reliability of RSSI for indoor localization. Proceedings of the 2012 International Conference on Wireless Communications in Underground and Confined Areas, Clermont-Ferrand, France.
18. Li, G., Geng, E., Ye, Z., Xu, Y., Lin, J., and Pang, Y. (2018). Indoor Positioning Algorithm Based on the Improved RSSI Distance Model. Sensors, 18.10.3390/s18092820 ↗
19. Wang, Y., Yang, X., Zhao, Y., Liu, Y., and Cuthbert, L. (2013, January 11–14). Bluetooth positioning using RSSI and triangulation methods. Proceedings of the 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA.
20. Pradhan, S., Bae, Y., Pyun, J.Y., Ko, N.Y., and Hwang, S. (2019). Hybrid TOA Trilateration Algorithm Based on Line Intersection and Comparison Approach of Intersection Distances. Energies, 12.10.3390/en12091668 ↗
21. Cantón Paterna, V., Calveras Augé, A., Paradells Aspas, J., and Pérez Bullones, M.A. (2017). A Bluetooth Low Energy Indoor Positioning System with Channel Diversity, Weighted Trilateration and Kalman Filtering. Sensors, 17.10.3390/s17122927 ↗
22. He, S., Hu, T., and Chan, S.-H.G. (2015, January 6–8). Contour-based Trilateration for Indoor Fingerprinting Localization. Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems (SenSys’15), Delft, The Netherlands.10.1145/2809695.2809703 ↗
23. Smartphone-Based Indoor Positioning Using BLE iBeacon and Reliable Lightweight Fingerprint Map10.1109/JSEN.2020.2989411 ↗
24. Semi-supervised Deep Reinforcement Learning in Support of IoT and Smart City Services10.1109/JIOT.2017.2712560 ↗
25. Novel iBeacon Placement for Indoor Positioning in IoT10.1109/JSEN.2018.2875037 ↗
26. Sharma, R., and Badarla, V. (2018, January 16–19). Geometrical Optimization of A Novel Beacon Placement Strategy for 3D Indoor Localization. Proceedings of the 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Indore, India.10.1109/ANTS.2018.8710138 ↗
27. Ramadhan, H., Yustiawan, Y., and Kwon, J. (2020). Applying Movement Constraints to BLE RSSI-Based Indoor Positioning for Extracting Valid Semantic Trajectories. Sensors, 20.10.3390/s20020527 ↗
28. SemanticSLAM: Using Environment Landmarks for Unsupervised Indoor Localization10.1109/TMC.2015.2478451 ↗
29. Towards a semantic indoor trajectory model: Application to museum visits10.1007/s10707-020-00430-x ↗