The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Improving Indoor Localization Using Bluetooth Low Energy Beacons
Open accessAbstract
The paper describes basic principles of a radio-based indoor localization and focuses on the improvement of its results with the aid of a new Bluetooth Low Energy technology. The advantage of this technology lies in its support by contemporary mobile devices, especially by smartphones and tablets. We have implemented a distributed system for collecting radio fingerprints by mobile devices with the Android operating system. This system enables volunteers to create radio-maps and update them continuously. New Bluetooth Low Energy transmitters (Apple uses its “iBeacon” brand name for these devices) have been installed on the floor of the building in addition to existing WiFi access points. The localization of stationary objects based on WiFi, Bluetooth Low Energy, and their combination has been evaluated using the data measured during the experiment in the building. Several configurations of the transmitters’ arrangement, several ways of combination of the data from both technologies, and other parameters influencing the accuracy of the stationary localization have been tested.
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Cites
Survey of Wireless Indoor Positioning Techniques and Systems
Liu, H., Darabi, H., Banerjee, P., & Liu, J. (2007) · IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2007.905750 · Cited by 4,098
Locating the nodes: cooperative localization in wireless sensor networks
Patwari, N., Ash, J. N., Kyperountas, S., Hero, A. O., Moses, R. L., & Correal, N. S. (2005) · IEEE Signal Processing Magazine · doi:10.1109/msp.2005.1458287 · Cited by 3,031
SurroundSense: mobile phone localization via ambience fingerprinting
Azizyan, M., Constandache, I., & Roy Choudhury, R. (2009) · Proceedings of the 15th annual international conference on Mobile computing and networking · doi:10.1145/1614320.1614350 · Cited by 723
Angle of Arrival Localization for Wireless Sensor Networks
Peng, R., & Sichitiu, M. L. (2006) · 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks · doi:10.1109/sahcn.2006.288442 · Cited by 636
A comparative survey of WLAN location fingerprinting methods
Honkavirta, V., Perala, T., Ali-Loytty, S., & Piche, R. (2009) · 2009 6th Workshop on Positioning, Navigation and Communication · doi:10.1109/wpnc.2009.4907834 · Cited by 522
WILL: Wireless Indoor Localization without Site Survey
Wu, C., Yang, Z., Liu, Y., & Xi, W. (2013) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2012.179 · Cited by 424
Accurate indoor localization with zero start-up cost
Kumar, S., Gil, S., Katabi, D., & Rus, D. (2014) · Proceedings of the 20th annual international conference on Mobile computing and networking · doi:10.1145/2639108.2639142 · Cited by 386
Evaluation of the reliability of RSSI for indoor localization
Dong, Q., & Dargie, W. (2012) · doi:10.1109/icwcuca.2012.6402492 · Cited by 144
Does BTLE measure up against WiFi? A comparison of indoor location performance
Zhao, X., Xiao, Z., Markham, A., Trigoni, N., & Ren, Y. (2014) · Oxford University Research Archive (ORA) (University of Oxford) · Cited by 140
Design and Analysis of a Bluetooth-Based Indoor Localization System
Bruno, R., & Delmastro, F. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-39867-7_66 · Cited by 110
Cited by
Evolution of Indoor Positioning Technologies: A Survey
Brena, R. F., García-Vázquez, J. P., Galván-Tejada, C. E., Muñoz-Rodriguez, D., Vargas-Rosales, C., & Fangmeyer, J. (2017) · Journal of Sensors · doi:10.1155/2017/2630413 · Cited by 478
Collaborative Multi-Robot Systems for Search and Rescue: Coordination and Perception
Queralta, J. P., Taipalmaa, J., Pullinen, B. C., Sarker, V. K., Gia, T. N., Tenhunen, H., Gabbouj, M., Raitoharju, J., & Westerlund, T. (2020) · arXiv · doi:10.48550/arxiv.2008.12610 · Cited by 27
Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration
Naghdi, S., & O’Keefe, K. (2022) · Sensors · doi:10.3390/s22124320 · Cited by 23
Higher Education Check-Ins: Exploring the User Experience of Hybrid Location Sensing
Huang, Y., Sang, Y., Wu, Q., & Yao, Y. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359168 · Cited by 12
Metadata
- Authors
- Kriz, P., Maly, F., & Kozel, T.
- Venue
- Mobile Information Systems
- Publisher
- Hindawi Limited
- Published
- 2016
- Volume
- 2016
- Pages
- 1-11
- ISSN
- 1574-017X (ISSN-L)
- 1875-905X
- OpenAlex
- W2341720557
- Cited by
- 256
- References
- 13
- FWCI
- 15.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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