On the Feasibility of Using Two Mobile Phones and WLAN Signal to Detect Co-Location of Two Users for Epidemic Prediction
An epidemic may be controlled or predicted if we can monitor the history of physical human contacts. As most people have a smart phone, a contact between two persons can be regarded as a handshake between the two phones. Our task becomes how to detect the moment the two mobile phones are close. In this paper, we investigate the possibility of using the outdoor WLAN signals, provided by public Access Points, for off-line mobile phones collision detection. Our method does not require GPS coverage, or real-time monitoring. We designed an Android app running in the phone’s background to periodically collect the outdoor WLAN signals. This data are then analysed to detect the potential contacts. We also discuss several approaches to handle the mobile phone diversity, and the WLAN scanning latency issue. Based on our measurement campaign in the real world, we conclude that it is feasible to detect the co-location of two phones with the WLAN signals only.
KeywordsEpidemic tracking Co-location WLAN tracking
The authors would like to thank the anonymous reviewers for their insightful comments on the paper. This research is funded by the Computer Science Department of Royal Holloway, University of London, and EPSRC grant EP/K033344/1 (“Mining the Network Behaviour of Bots”).
- Bahl P, Padmanabhan VN (2000) RADAR: an in-building RF-based user location and tracking system. In: Proceedings of nineteenth annual joint conference of the IEEE computer and communications societies, INFOCOM 2000, vol 2. IEEE, pp 775–784Google Scholar
- Chintalapudi K, Padmanabha Iyer A, Padmanabhan VN (2010) Indoor localization without the pain. In: Proceedings of the sixteenth annual international conference on mobile computing and networking ACM, pp 173–184Google Scholar
- Ibrahim M, Youssef M (2013). Enabling wide deployment of GSM localization over heterogeneous phones. In: IEEE international conference on communications, pp 6396–6400Google Scholar
- Kaemarungsi K, Krishnamurthy P (2004) Properties of indoor received signal strength for wlan location fingerprinting, in mobile and ubiquitous systems: networking and services. The first annual international conference on obiquitous. IEEE, pp 14–23Google Scholar
- Krumm J, Hinckley K (2004). The nearme wireless proximity server. In UbiComp: ubiquitous computing. Springer, Heidelberg pp 283–300Google Scholar
- Lee M, Han D (2012) QRLoc: user-involved calibration using quick response codes for Wi-Fi based indoor localization. In: 7th international conference on computing and convergence technology (ICCCT). IEEE, pp 1460–1465Google Scholar
- Martin E, Vinyals O, Friedland G, Bajcsy R (2010) Precise indoor localization using smart phones. In: Proceedings of the international conference on multimedia, pp 787–790Google Scholar
- Park JG, Curtis D, Teller S, Ledlie J (2011) Implications of device diversity for organic localization. In: Proceedings of INFOCOM. IEEE, pp 3182–3190Google Scholar
- Pei L, Liu J, Guinness R, Chen Y, Kroger T, Chen R, Chen L (2012). The evaluation of WiFi positioning in a bluetooth and WiFi coexistence environment. In: ubiquitous positioning, indoor navigation, and location based service (UPINLBS). IEEE, pp 1–6Google Scholar
- Wang H, Sen S, Elgohary A, Farid M, Youssef M, Choudhury R (2012) No need to war-drive: unsupervised indoor localization. In: Proceedings of the 10th international conference on mobile systems, applications, and services, ACM, pp 197–210Google Scholar
- Yoneki E (2011) Fluphone study: virtual disease spread using haggle In: Proceedings of the 6th ACM workshop on challenged networks, ACM, pp 65–66Google Scholar