Wireless Personal Communications

, Volume 109, Issue 2, pp 963–980 | Cite as

Finding Mobility Pattern of Movable Target in Wireless Sensor Networks by Crowdsourcing Designed Mechanism

  • Ramin Dehdasht-HeydariEmail author
  • Homa Kavand


Target tracking in wireless sensor networks is one of the well-known applications of such networks. The use of sensor-based electronic devices is becoming widespread and can be used for target tracking method. The obvious feature of these networks based on crowdsourcing mechanism is that the sensor nodes can be mobile. This paper presents a target tracking in a wireless sensor network which is generated by a crowdsourcing mechanism. The path of the target tracking has been extracted through SIR particle filter and statistical analysis model. Because of knowing the direction of the target movement can be effective in predicting the pursuit nodes and reducing of energy consumption, the proposed target tracking algorithm is based on prediction. The simulation results of the proposed algorithm on a wireless sensor network has been concluded by NS2 package. More effective target tracking algorithms can be presented by means of achieved mobility pattern in this research.


Crowdsourcing Energy consumption Mean squared error Mobility pattern of target Target tracking Wireless sensor networks SIR particle filter 



  1. 1.
    Sohraby, K., Minoli, D., & Znati, T. (2007). Wirless sensor networks. Hoboken: Wiley.CrossRefGoogle Scholar
  2. 2.
    Karl, H., & Willig, A. (2005). Protocols and architectures for wireless sensor networks. Hoboken: Wiley.CrossRefGoogle Scholar
  3. 3.
    Dhurandher, S. K., Obaidat, M. S., & Gupta, M. (2012). Providing reliable and link stability-based geocasting model in underwater environment. International Journal of Communication Systems, 25(3), 356–375.CrossRefGoogle Scholar
  4. 4.
    Chen, W. M., Li, C. S., Chiang, F. Y., & Chao, H. C. (2007). Jumping ant routing algorithm for sensor networks. Computer Communications, 30(14–15), 2892–2903.CrossRefGoogle Scholar
  5. 5.
    Misra, S., Oommen, B. J., Yanamandra, S., & Obaidat, M. S. (2010). Random early detection for congestion avoidance in wired networks: A discretized pursuit learning-automata-like solution. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics, 40(1), 66–76.CrossRefGoogle Scholar
  6. 6.
    Ho, A. H., Ho, Y. H., & Hua, K. A. (2010). Handling high mobility in next-generation wireless ad hoc networks. International Journal of Communication Systems, 23(9–10), 1078–1092.CrossRefGoogle Scholar
  7. 7.
    Zhang, Z. J., Fu, J. S., & Chao, H. C. (2013). An energy-efficient motion strategy for mobile sensors in mixed wireless sensor networks. International Journal of Distributed Sensor Networks, 1, 2013.Google Scholar
  8. 8.
    Schwiebert, L., Gupta, S. K. S., & Weinmann, J. (2001). Research challenges in wireless networks of biomedical sensors. In Proceedings of the 7th annual international conference on Mobile computing and networkingMobiCom’01 (pp. 151–165).Google Scholar
  9. 9.
    Ojha, T., & Misra, S. (2013). MobiL: A 3-dimensional localization scheme for mobile underwater sensor networks. In 2013 National conference on communications (NCC) (pp. 0–4).Google Scholar
  10. 10.
    Misra, S., & Jain, A. (2011). Policy controlled self-configuration in unattended wireless sensor networks. Journal of Network and Computer Applications, 34(5), 1530–1544.CrossRefGoogle Scholar
  11. 11.
    Cao, N., Brahma, S., & Varshney, P. K. (2015). Target tracking via crowdsourcing: A mechanism design approach. IEEE Transactions on Signal Processing, 63(6), 1464–1476.MathSciNetCrossRefGoogle Scholar
  12. 12.
    Misra, S., & Singh, S. (2012). Localized policy-based target tracking using wireless sensor networks. ACM Transactions on Sensor Networks, 8(3), 1–30.CrossRefGoogle Scholar
  13. 13.
    Chandrasekar Ramachandran, R., Misra, S., & Obaidat, M. S. (2008). A probabilistic zonal approach for swarm-inspired wildfire detection using sensor networks. International Journal of Communication Systems, 21, 1047–1073.CrossRefGoogle Scholar
  14. 14.
    Camp, T., Boleng, J., & Davies, V. (2002). A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing, 2(5), 483–502.CrossRefGoogle Scholar
  15. 15.
    Bai, F., & Helmy, A. (2004). A survey of mobility models in wireless adhoc networks. In Wireless adhoc networks. University of Southern California, USA (pp. 1–30).Google Scholar
  16. 16.
    Aschenbruck, N., & Gerhards-Padilla, E. (2008). A survey on mobility models for performance analysis in tactical mobile networks. Journal of Telecommunications and Information Technology, 2, 54–61.Google Scholar
  17. 17.
    Haerri, J., Filali, F., & Bonnet, C. (2009). Mobility models for vehicular ad hoc networks: a survey and taxonomy. IEEE Communications Surveys & Tutorials, 11(4), 19–41.CrossRefGoogle Scholar
  18. 18.
    Chen, M., Wang, X., Kwon, T., & Chao, H. C. (2011). Multiple mobile agents’ itinerary planning in wireless sensor networks: Survey and evaluation. IET Communications, 5(12), 1769–1776.CrossRefGoogle Scholar
  19. 19.
    Jardosh, A. P., Belding-Royer, E. M., Almeroth, K. C., & Suri, S. (2005). Real-world environment models for mobile network evaluation. IEEE Journal on Selected Areas in Communications, 23(3), 622–632.CrossRefGoogle Scholar
  20. 20.
    Le Boudec, J., & Vojnovi, M. (2005). Perfect simulation and stationarity of a class of mobility models. In Proceedings of the IEEE Infocom, Miami, FL, USA (pp. 2743–2754).Google Scholar
  21. 21.
    Maeda, K., Sato, K., Konishi, K., Yamasaki, A., & Uchiyama, A. (2005). Getting urban pedestrian flow from simple observation: Realistic mobility generation in wireless network simulation categories and subject descriptors. In Proceedings of the 8th ACM/IEEE international symposium on modeling, analysis and simulation of wireless and mobile systems, Montreal, Canada (pp. 151–158).Google Scholar
  22. 22.
    Einstein, A. (1956). Investigation on the theory of the Brownian movement. Mineola: Dover Publications.zbMATHGoogle Scholar
  23. 23.
    Johnson, D. B., & Maltz, D. A. (1996). Dynamic source routing in ad hoc wireless networks. pp. 153–181.Google Scholar
  24. 24.
    Royer, E. M., Melliar-smitht, P. M., & Mosert, L. E. (2001). An analysis of the optimum node density for ad hoc mobile networks. In Proceedings of the IEEE international conference on communications, Helsinki (pp. 857–861).Google Scholar
  25. 25.
    Liang, B., & Haas, Z. J. (1999). Predictive distance-based mobility management for PCS networks. In Proceedings of the INFOCOM, New York, NY, USA (pp. 1377–1384).Google Scholar
  26. 26.
    Hsu, W., Merchant, K., Shu, H., Hsu, C., & Helmy, A. (2005). Weighted waypoint mobility model and its impact on ad hoc networks. Mobile Computing and Communications Review, 5, 1769–1776.Google Scholar
  27. 27.
    Tuduce, C., & Gross, T. (2005). A mobility model based on WLAN traces and its validation. In Proceedings of the IEEE Infocom Miami, FL, USA (vol. 1, no. c, pp. 664–674).Google Scholar
  28. 28.
    Jain, R., & Lelescu, D. (2005). Model T: An empirical model for user registration patterns in a campus wireless LAN. In The 11th annual international conference on mobile computing and networking, Cologne, Germany (pp. 170–184).Google Scholar
  29. 29.
    Lelescu, D., Kozat, C., Jain, R., & Balakrishnan, M. (2006). Model T ++: An empirical joint space-time registration model categories and subject descriptors. In Proceedings of the ACM MOBIHOC, Florence, Italy (pp. 61–72).Google Scholar
  30. 30.
    Yoon, J., Noble, B. D., Liu, M., Arbor, A., & Kim, M. (2006). Building realistic mobility models from coarse-grained traces. In Proceedings of the ACM MobiSys, Uppsala, Sweden (pp. 170–199).Google Scholar
  31. 31.
    Kim, M., Kotz, D., & Kim, S. (2006). Extracting a mobility model from real user traces. In Proceedings of the IEEE INFOCOM, Barcelona, Spain (pp. 1–13).Google Scholar
  32. 32.
    Hsu, W., Spyropoulos, T., Psounis, K., & Helmy, A. (2007). Modeling time-variant user mobility in wireless mobile networks. In Proceedings of the IEEE INFOCOM, Anchorage, AK (pp. 758–766).Google Scholar
  33. 33.
    Kaist, K. L., Ncsu, S. H., Joon, S., & Ncsu, K. (2009). SLAW: A mobility model for human walks. In Proceedings of the IEEE INFOCOM, Rio de Janeiro, Brazil (pp. 855–863).Google Scholar
  34. 34.
    Aschenbruck, N., Munjal, A., & Camp, T. (2011). Trace-based mobility modeling for multi-hop wireless networks. Computer Communications, 34(6), 704–714.CrossRefGoogle Scholar
  35. 35.
    Yang, W., Wang, Y., Tseng, Y., & Lin, B. P. (2010). Energy-efficient network selection with mobility pattern awareness in an integrated WiMAX and WiFi network. International Journal of Communication Systems, 23, 213–230.CrossRefGoogle Scholar
  36. 36.
    Hong, J., & Kim, H. (2011). An empirical framework for user mobility models: Refining and modeling user registration patterns. Journal of Computer and System Sciences, 77(5), 869–883.MathSciNetCrossRefGoogle Scholar
  37. 37.
    Bettstetter, C. (2001). Mobility modeling in wireless networks: Categorization, smooth movement, and border effects. ACM SIGMOBILE Mobile Computing and Communications Review, 5(3), 55–66.CrossRefGoogle Scholar
  38. 38.
    Mascolo, C. (2007). Designing mobility models based on social network theory. ACM SIGMOBILE Mobile Computing and Communications Review, 1(2), 59–70.Google Scholar
  39. 39.
    Cho, E. (2011). Friendship and mobility: User movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, CA, USA (pp. 1082–1090).Google Scholar
  40. 40.
    Wang, J., Yuan, J., Shan, X., Feng, Z., Geng, J., & You, I. (2011) SaMob: A social attributes based mobility model for ad hoc networks. In Proceedings of the fifth international conference on innovative mobile and internet services in ubiquitous computing, Seoul, Korea (pp. 444–449).Google Scholar
  41. 41.
    Thakur, G. S., & Kumar, U. (2012). Gauging human mobility characteristics and its impact on mobile routing performance. International Journal of Sensor Networks, 11(3), 179–191.MathSciNetCrossRefGoogle Scholar
  42. 42.
    Vastardis, N., & Yang, K. (2012). An enhanced community-based mobility model for distributed mobile social networks. Journal of Ambient Intelligence and Humanized Computing, 5, 1–11.Google Scholar
  43. 43.
    Sung, T., & Yang, C. (2010). An adaptive joining mechanism for improving the connection ratio of ZigBee wireless sensor networks. International Journal of Communication Systems, 70, 231–251.CrossRefGoogle Scholar
  44. 44.
    Khan, R., Madani, S. A., Hayat, K., & Khan, S. U. (2012). Clustering-based power-controlled routing for mobile wireless sensor networks. International Journal of Communication Systems, 25(4), 529–542.CrossRefGoogle Scholar
  45. 45.
    Misra, S., Singh, S., Khatua, M., & Obaidat, M. S. (2015). Extracting mobility pattern from target trajectory in wireless sensor networks. International Journal of Communication Systems, 28, 213–230.CrossRefGoogle Scholar
  46. 46.
    Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174–188.CrossRefGoogle Scholar
  47. 47.
    Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference system sciences (pp. 1–10).Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical Engineering, College of Engineering, Kermanshah BranchIslamic Azad UniversityKermanshahIran
  2. 2.Department of Computer Engineering, College of Engineering, Kermanshah BranchIslamic Azad UniversityKermanshahIran

Personalised recommendations