Wireless Personal Communications

, Volume 77, Issue 3, pp 2037–2060 | Cite as

PhyCon: Discovering Physical Connectivity for Indoor WLAN Using Mobility

  • Marco A. GonzalezEmail author
  • Javier Gomez
  • Francisco Garcia
  • Victor Rangel


The concept of connectivity in wireless networks is a well-established term referring to the ability of nodes to communicate with other nodes directly or through other nodes working as relays. In this paper, a different aspect of connectivity is presented named physical connectivity, which we defined as the ability of nodes to physically reach other nodes, not only through open spaces, but also through corridors, doors, rooms, etc. For indoor wireless local area networks (WLANs), we believe that awareness of physical connectivity is a key factor for developing emerging applications such as guiding, localization, tracking, physical routing, etc. Related studies only consider the problem of direct connectivity or line-of-sight (LOS), however, we consider physical connectivity should span beyond LOS conditions enabling nodes to reach other nodes through any path available. In this paper a novel method to discover physical connectivity named PhyCon is presented. PhyCon combines node mobility and the inverse-square relationship between received signal strength and distance to discover physical connectivity paths among wireless users. Results from simulations and testbed experiments show PhyCon can discover a high percentage of physical connectivity paths using normal mobility behavior of users found in indoor WLAN.


Connectivity Indoors RSSI WLAN Mobility 


  1. 1.
    Sirbu, M., Lehr, W., & Gillet, S. (2006). Evolving wireless access technologies for municipal broadband. Journal of Government Information Quarterly, 23, 480–502.CrossRefGoogle Scholar
  2. 2.
    Virone, G., Wood, A., Selavo, L., Cao, Q., Fang, L. , Doan, T., et al. (April 2006). An advanced wireless sensor network for health monitoring. In Proceedings of transdisciplinary conference on distributed diagnosis and home healthcare (pp. 95–100), Seatle, WA, USA.Google Scholar
  3. 3.
    Sha, K., Shi, W., & Watkins, O. (May 2006). Using wireless sensor networks for fire rescue applications: Requirements and challenges. In Proceedings of IEEE international conference on electro/information technology (pp. 239–244), East Lansing, Michigan, USA.Google Scholar
  4. 4.
    Du, W., Fang, L., & Ning, P. (April 2005). Lad: Localization anomaly detection for wireless sensor networks. In Proceedings of IEEE 19th international parallel and distributed processing symposium (pp. 874–886), Denver, CO, USA.Google Scholar
  5. 5.
    Chin, A., Wang, H., Zhu, L., Xu, B., & Zhang, K. (October 2011). Connecting people in the workplace through ephemeral social networks. In Proceedings of IEEE third international conference on social computing (socialcom) (pp. 527–530), Boston, MA, USA.Google Scholar
  6. 6.
    Tseng, Y., Pan, M., & Tsai, Y. (2006). A distributed emergency navigation algorithm for wireless sensor networks. IEEE Computers, 39(7), 55–62.CrossRefGoogle Scholar
  7. 7.
    Pan, M., Tsai, C., & Tseng, Y. (2006). Emergency guiding and monitoring applications in indoor 3d environments by wireless sensor networks. International Journal of Sensor Networks, 1, 2–10.CrossRefGoogle Scholar
  8. 8.
    Gonzalez, M. A., Gomez, J., Rangel, V., Lopez-Guerrero, M., & de Oca, M. M. M. (2009). GUIDE-gradient: A guiding algorithm for mobile nodes in WLAN and ad hoc networks. Wireless Personal Communications, 2(4), 629–653.Google Scholar
  9. 9.
    Yadav, V., Mishra, M. K., Sngh, A., & Gore, M. M. (2009). Localization scheme for three dimensional wireless sensor networks using GPS enabled mobile sensor nodes. International Journal of Next-Generation Networks (IJNGN), 1(1), 60–72.Google Scholar
  10. 10.
    Koutsonikolas, D., Das, S. M., & Hu, Y. C. (2007). Path planning of mobile landmarks for localization in wireless sensor networks. Computer Communications, 30(13), 2577–2592.CrossRefGoogle Scholar
  11. 11.
    Kim, K., & Lee, W. (August 2007). MBAL: A mobile Beacon-assisted localization scheme for wireless sensor networks. In Proceedings of 16th international conference on computer communications and networks (ICCCN 2007) (pp. 57–62), Honolulu, Hawaii, USA.Google Scholar
  12. 12.
    Johnson, D. B., & Maltz, D. A. (1996). Dynamic source routing in ad hoc wireless networks. Mobile Computing, 353, 153–181.CrossRefGoogle Scholar
  13. 13.
    Perkins, C. E., Belding-Royer, E. M., & Chakeres, I. (February 1999). Ad-hoc on-demand distance vector routing. In Proceedings of the 2nd IEEE workshop on mobile computing systems and applications (pp. 90–100), New Orleans, LA, USA.Google Scholar
  14. 14.
    Golden, S., & Bateman, S. S. (2007). Sensor measurements for Wi-Fi location with emphasis on time-of-arrival ranging. IEEE Transactions on Mobile Computing, 6(10), 1185–1198.CrossRefGoogle Scholar
  15. 15.
    Uthansakul, P., & Uthansakul, M. (February 2009). WLAN positioning based on joint TOA and RSS characteristics. International Journal of Electronics, Communications and Computer Engineering, 1(3).Google Scholar
  16. 16.
    Yamasaki, R., Tamaki, A. O. T., Matsuzawa, N., & Kato, T. (October 2005). TDOA location system for IEEE 802.11b WLAN. In Proceedings of the IEEE wireless communications and networking conference (WCNC05) (pp. 2338–2343), New Orleans.Google Scholar
  17. 17.
    Niculescu, D., & Nath, B. (March 2003). Ad-hoc positioning system (APS) using AoA. In Proceedings of the 22nd annual conference on computer communications (IEEE INFOCOM) (pp. 1734–1743), San Francisco, CA.Google Scholar
  18. 18.
    Kitasuka, T., Hisazumi, K., Nakanishi, T., & Fukuda, A. (March 2005). Positioning techniques of wireless LAN terminals using RSSI between terminals. In Proceedings of the ACM international symposium on mobile ad hoc networking and computing (MobiHoc 2003) (pp. 47–53), Las Vegas, NV, USA.Google Scholar
  19. 19.
    Bahl, P., & Padmanabhan, V. N. (March 2000). RADAR: An in-building RF-based user location and tracking system. In Proceedings of the 19th annual conference on computer communications (IEEE INFOCOM) (pp. 7–9), Tel Aviv, Israel.Google Scholar
  20. 20.
    Vicaire, P., & Stankovic, J. (March 2004). Improvements on distributed, range free localization for wireless sensor networks. Technical Report of University of Virginia, vol. CS-2004-35.Google Scholar
  21. 21.
    Shang, Y., Ruml, W., & Zhang, Y. (June 2003). Localization from Mere connectivity. In Proceedings of the ACM international symposium on mobile ad hoc networking and computing (MobiHoc 2003) (pp. 201–212), Annapolis, MD, USA.Google Scholar
  22. 22.
    Baouche, C., Freitas, A., & Misson, M. (2009). Radio proximity detection in a WSN to localize mobile entities within a confined area. Journal of Communications, 4(4), 232–240.CrossRefGoogle Scholar
  23. 23.
    Valadas, R. T., Moreira, A. C., Lomba, C. T., Tavares, A. R., & de Oliveira Duarte, A. M. (1998). The infrared physical layer of the IEEE 802.11 standard for wireless local area networks. IEEE Communications Magazine, 36(12), 107–112.CrossRefGoogle Scholar
  24. 24.
    Sohn, B., Lee, J., Chae, H. , & Yu, W. (October 2007). Localization system for mobile robot using wireless communication with IR landmark. In Proceedings of 1st international conference on robot communication and coordination (ROBOCOMM 2007), Athens, Greece.Google Scholar
  25. 25.
    Wylie, M., & Holtzman, J. (September–October 1996). The non-line of sight problem in mobile location estimation. In Proceedings of 5th IEEE international conference on universal personal communications (ICUPC 1996) (pp. 827–831), Cambridge, MA, USA.Google Scholar
  26. 26.
    Wysocki, T. A., & Zepernick, H. J. (2000). Characterization of the indoor radio propagation channel at 2.4 GHz. Journal of Telecommunications and Information Technology, 3(4), 84–90.Google Scholar
  27. 27.
    Benedetto, F., Giunta, G., Toscano, A., & Vegni, L. (April 2007). Dynamic LOS/NLOS statistical discrimination of wireless mobile channels. In Proceedings of 65th IEEE vehicular technology conference (pp. 3071–3075), Los Angeles, CA, USA.Google Scholar
  28. 28.
    Hashemi, H. (1993). The indoor radio propagation channel. IEEE, 81(7), 943–968.CrossRefGoogle Scholar
  29. 29.
    Lustmann, Y., & Porrat, D. (2010). Indoor channel spectral statistics, k-factor and reverberation distance. IEEE Transactions on Antennas and Propagation Journal, 58, 3685–3692.CrossRefGoogle Scholar
  30. 30.
    Kaemarungsi, K., Krishnamurthy, P. Properties of Indoor Received Signal Strength for WLAN Location Fingerprinting. In Proc. of the 1st Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MOBIQUITOUS 04), Boston, Massachusetts, USA, pp. 14–23, August 2004.Google Scholar
  31. 31.
    Gabriel, K., & Sokal, R. (1969). A New Statistical Approach to Geographic Variation Analysis. Systematic Zoology, 18(3), 259–278.CrossRefGoogle Scholar
  32. 32.
    Lyon, G. Nmap Free Security Scanner For Network Exploration. [Online]. Available:, 2012

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Marco A. Gonzalez
    • 1
    Email author
  • Javier Gomez
    • 1
  • Francisco Garcia
    • 1
  • Victor Rangel
    • 1
  1. 1.Department of Telecommunications EngineeringNational Autonomous University of MexicoMexico CityMexico

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