Abstract
In this article, we propose a new method for automatically updating a Wi-Fi indoor positioning model on a cloud server by employing uploaded sensor data obtained from the smartphone sensors of a specific user who spends a lot of time in a given environment (e.g., a worker in the environment). In this work, we attempt to track the user with pedestrian dead reckoning techniques, and at the same time we obtain Wi-Fi scan data from a mobile device possessed by the user. With the scan data and the estimated coordinates uploaded to a cloud server, we can automatically create a pair consisting of a scan and its corresponding indoor coordinates during the user's daily life and update an indoor positioning model on the server by using the information. With this approach, we try to cope with the instability of Wi-Fi-based positioning methods caused by changing environmental dynamics, that is, layout changes and moving or removal of Wi-Fi access points. Therefore, ordinary users (e.g., customers) who do not have rich sensors can benefit from the continually updating positioning model.
- Ling Bao and Stephen S Intille. 2004. Activity recognition from user-annotated acceleration data. In Pervasive 2004. 1--17.Google Scholar
Cross Ref
- Philipp Bolliger. 2008. Redpin-adaptive, zero-configuration indoor localization through user collaboration. In International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments. 55--60. Google Scholar
Digital Library
- Mauro Cettolo and Michele Vescovi. 2003. Efficient audio segmentation algorithms based on the BIC. In ICASSP 2003, 6. 537--540.Google Scholar
Cross Ref
- Xiaoyong Chai and Qiang Yang. 2005. Reducing the calibration effort for location estimation using unlabeled samples. In PerCom 2005. 95--104. Google Scholar
Digital Library
- Eddie C. L. Chan, George Baciu, and S. C. Mak. 2009. Using Wi-Fi signal strength to localize in wireless sensor networks. In International Conference on Communications and Mobile Computing, 1. 538--542. Google Scholar
Digital Library
- Shu Chen, Yingying Chen, and Wade Trappe. 2008. Exploiting environmental properties for wireless localization and location aware applications. In PerCom 2008. 90--99. Google Scholar
Digital Library
- Scott Chen and Ponani Gopalakrishnan. 1998. Speaker, environment and channel change detection and clustering via the Bayesian Information Criterion. In DARPA Broadcast News Transcription and Understanding Workshop.Google Scholar
- Wei-Peng Chen, Jennifer C. Hou, and Lui Sha. 2004. Dynamic clustering for acoustic target tracking in wireless sensor networks. IEEE Transactions on Mobile Computing 3, 3 (2004), 258--271. Google Scholar
Digital Library
- Yi-Chao Chen, Ji-Rung Chiang, Hao-hua Chu, Polly Huang, and Arvin Wen Tsui. 2005. Sensor-assisted Wi-Fi indoor location system for adapting to environmental dynamics. In ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems. 118--125. Google Scholar
Digital Library
- Arnaud Doucet. 2001. Sequential Monte Carlo Methods. Wiley Online Library.Google Scholar
- Brian Ferris, Dieter Fox, and Neil Lawrence. 2007. WiFi-SLAM using Gaussian process latent variable models. In IJCAI 2007. 2480--2485. Google Scholar
Digital Library
- Kaori Fujinami and Satoshi Kouchi. 2013. Recognizing a mobile phone's storing position as a context of a device and a user. In MobiQuitous 2013. 76--88.Google Scholar
- Andrew R. Golding and Neal Lesh. 1999. Indoor navigation using a diverse set of cheap, wearable sensors. In International Symposium on Wearable Computers. 29--36. Google Scholar
Digital Library
- Michael Gunawan, Binghao Li, Thomas Gallagher, Andrew G. Dempster, and Gunther Retscher. 2012. A new method to generate and maintain a WiFi fingerprinting database automatically by using RFID. In International Conference on Indoor Positioning and Indoor Navigation (IPIN 2012). 1--6.Google Scholar
Cross Ref
- Michael Hardegger, Gerhard Tröster, and Daniel Roggen. 2013. Improved ActionSLAM for long-term indoor tracking with wearable motion sensors. In International Symposium on Wearable Computers (ISWC'13). 1--8. Google Scholar
Digital Library
- Amit P. Jardosh, Elizabeth M. Belding-Royer, Kevin C. Almeroth, and Subhash Suri. 2005. Real-world environment models for mobile network evaluation. IEEE Journal on Selected Areas in Communications 23, 3 (2005), 622--632. Google Scholar
Digital Library
- Yifei Jiang, Xin Pan, Kun Li, Qin Lv, Robert P. Dick, Michael Hannigan, and Li Shang. 2012. ARIEL: Automatic Wi-Fi based room fingerprinting for indoor localization. In Ubicomp 2012. 441--450. Google Scholar
Digital Library
- Wonho Kang, Seongho Nam, Youngnam Han, and Sookjin Lee. 2012. Improved heading estimation for smartphone-based indoor positioning systems. In IEEE 23rd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC'12). 2449--2453.Google Scholar
Cross Ref
- Yungeun Kim, Yohan Chon, and Hojung Cha. 2012. Smartphone-based collaborative and autonomous radio fingerprinting. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 42, 1 (2012), 112--122. Google Scholar
Digital Library
- Jukka Kortelainen, Eero Vayrynen, Xiaofeng Jia, Tapio Seppanen, and Nitish Thakor. 2012. EEG-based detection of awakening from isoflurane anesthesia in rats. In International Conference on Engineering in Medicine and Biology Society (EMBC12). 4279--4282.Google Scholar
Cross Ref
- Anthony LaMarca, Yatin Chawathe, Sunny Consolvo, Jeffrey Hightower, Ian Smith, James Scott, Tim Sohn, James Howard, Jeff Hughes, Fred Potter, Jason Tabert, Pauline Powledge, Gaetano Borriello, and Bill Schilit. 2005. Place lab: Device positioning using radio beacons in the wild. In Pervasive 2005. 116--133. Google Scholar
Digital Library
- Hui Liu, Houshang Darabi, Pat Banerjee, and Jing Liu. 2007. Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 37, 6 (2007), 1067--1080. Google Scholar
Digital Library
- Takuya Maekawa and Shinji Watanabe. 2011. Unsupervised activity recognition with user's physical characteristics data. In International Symposium on Wearable Computers. 89--96. Google Scholar
Digital Library
- Shaul Markovitch and Paul D. Scott. 1988. The role of forgetting in learning. In ICML 1988. 459--465.Google Scholar
- Sinno Jialin Pan, James T. Kwok, Qiang Yang, and Jeffrey Junfeng Pan. 2007. Adaptive localization in a dynamic WiFi environment through multi-view learning. In 22nd Association for the Advancement of Artificial Intelligence (AAAI) Conference on Artificial Intelligence. 1108--1113. Google Scholar
Digital Library
- Jun-geun Park, Ben Charrow, Dorothy Curtis, Jonathan Battat, Einat Minkov, Jamey Hicks, Seth Teller, and Jonathan Ledlie. 2010. Growing an organic indoor location system. In MobiSys 2010. 271--284. Google Scholar
Digital Library
- Dan Pelleg and Andrew Moore. 2000. X-means: Extending k-means with efficient estimation of the number of clusters. In ICML 2000, 1. 727--734. Google Scholar
Digital Library
- Teemu Pulkkinen, Teemu Roos, and Petri Myllymäki. 2011. Semi-supervised learning for WLAN positioning. In ICANN 2011. 355--362. Google Scholar
Digital Library
- Anshul Rai, Krishna Kant Chintalapudi, Venkata N. Padmanabhan, and Rijurekha Sen. 2012. Zee: Zero-effort crowdsourcing for indoor localization. In MobiCom 2012. 293--304. Google Scholar
Digital Library
- Cliff Randell, Chris Djiallis, and Henk Muller. 2005. Personal position measurement using dead reckoning. In International Symposium on Wearable Computers 2005. 166--173. Google Scholar
Digital Library
- Patrick Robertson, Maria Garcia Puyol, and Michael Angermann. 2011. Collaborative pedestrian mapping of buildings using inertial sensors and FootSLAM. In International Technical Meeting of The Satellite Division of the Institute of Navigation.Google Scholar
- Laura Ruotsalainen. 2012. Visual gyroscope and odometer for pedestrian indoor navigation with a smartphone. In Proceedings of the ION GNSS. 17--21.Google Scholar
- John C. Stein. 1998. Indoor Radio WLAN Performance Part II: Range Performance in a Dense Office Environment. Intersil Corporation. (1998).Google Scholar
- He Wang, Souvik Sen, Ahmed Elgohary, Moustafa Farid, Moustafa Youssef, and Romit Roy Choudhury. 2012. No need to war-drive: Unsupervised indoor localization. In MobiSys 2012. 197--210. Google Scholar
Digital Library
- Roy Want, Andy Hopper, Veronica Falcão, and Jonathan Gibbons. 1992. The active badge location system. ACM Transactions on Information Systems (TOIS) 10, 1 (1992), 91--102. Google Scholar
Digital Library
- Oliver Woodman and Robert Harle. 2008. Pedestrian localisation for indoor environments. In UbiComp 2008. 114--123. Google Scholar
Digital Library
- Jie Yin, Qiang Yang, and Lionel Ni. 2005. Adaptive temporal radio maps for indoor location estimation. In PerCom 2005. 85--94. Google Scholar
Digital Library
Index Terms
Automatic Update of Indoor Location Fingerprints with Pedestrian Dead Reckoning
Recommendations
Novel fingerprinting mechanisms for indoor positioning
As wireless communications and microelectronic technology rapidly develop, diverse applications and services based on smart handheld devices have drawn the attention of researchers. The popularity of Indoor Location Based services and applications has ...
Cross-assistive approach for PDR and Wi-Fi positioning
UbiComp '14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct PublicationIn indoor positioning using Wi-Fi, there is a problem that the accuracy is not stable by the occurrence of large errors. Large errors tend to occur when density of wireless LAN access points is low or the radio wave condition is unstable. Furthermore, ...
GPS-assisted Indoor Pedestrian Dead Reckoning
Indoor pedestrian dead reckoning (PDR) using embedded inertial sensors in smartphones has been actively studied in the ubicomp community. However, PDR relying only on inertial sensors suffers from the accumulation of errors from the sensors. Researchers ...






Comments