Estimating Spatial Averages of Environmental Parameters based on Mobile Crowdsensing

Ioannis Koukoutsidis


Mobile crowdsensing can facilitate environmental surveys by leveraging sensor-equipped mobile devices that carry out measurements covering a wide area in a short time, without bearing the costs of traditional field work. In this paper, we examine statistical methods to perform an accurate estimate of the mean value of an environmental parameter in a region, based on such measurements. The main focus is on estimates produced by considering the mobile device readings at a random instant in time. We compare stratified sampling with different stratification weights to sampling without stratification, as well as an appropriately modified version of systematic sampling. Our main result is that stratification with weights proportional to stratum areas can produce significantly smaller bias, and gets arbitrarily close to the true area average as the number of mobiles increases, for a moderate number of strata. The performance of the methods is evaluated for an application scenario where we estimate the mean area temperature in a linear region that exhibits the so-called Urban Heat Island effect, with mobile users moving in the region according to the Random Waypoint Model.


  1. Antonic, A., Bilas, V., Marjanovic, M., Matijasevic, M., Oletic, D., Pavelic, M., Zarko, I. P., Pripuzic, K., and Skorin-Kapov, L. (2014). Urban crowd sensing demonstrator: Sense the Zagreb air. In Software, Telecommunications and Computer Networks (SoftCOM), 2014 22nd International Conference on, pages 423-424. IEEE.
  2. Bash, B. A., Byers, J. W., and Considine, J. (2004). Approximately uniform random sampling in sensor networks. In Proceeedings of the 1st international workshop on Data management for sensor networks: in conjunction with VLDB 2004, pages 32-39. ACM.
  3. Bettstetter, C. and Wagner, C. (2002). The spatial node distribution of the random waypoint mobility model. WMAN, 11:41-58.
  4. Cochran, W. G. (1946). Relative accuracy of systematic and stratified random samples for a certain class of populations. The Annals of Mathematical Statistics, pages 164-177.
  5. Considine, J., Li, F., Kollios, G., and Byers, J. (2004). Approximate aggregation techniques for sensor databases. In Data Engineering, 2004. Proceedings. 20th International Conference on, pages 449- 460. IEEE.
  6. Datta, S. and Kargupta, H. (2007). Uniform data sampling from a peer-to-peer network. In Distributed Computing Systems, 2007. ICDCS'07. 27th International Conference on, pages 1-8. IEEE.
  7. Fiore, M., Nordio, A., and Chiasserini, C.-F. (2013). Investigating the accuracy of mobile urban sensing. In Wireless On-demand Network Systems and Services (WONS), 2013 10th Annual Conference on, pages 25- 28. IEEE.
  8. Ganesan, D., Ratnasamy, S., Wang, H., and Estrin, D. (2004). Coping with irregular spatio-temporal sampling in sensor networks. ACM SIGCOMM Computer Communication Review, 34(1):125-130.
  9. Ganti, R. K., Ye, F., and Lei, H. (2011). Mobile crowdsensing: current state and future challenges. Communications Magazine, IEEE, 49(11):32-39.
  10. Glynn, P. and Sigman, K. (1998). Independent sampling of a stochastic process. Stochastic processes and their applications, 74(2):151-164.
  11. Gonzalez, M. C., Hidalgo, C. A., and Barabasi, A.-L. (2008). Understanding individual human mobility patterns. Nature, 453(7196):779-782.
  12. Hyytiä, E., Lassila, P., and Virtamo, J. (2006). Spatial node distribution of the random waypoint mobility model with applications. IEEE Transactions on Mobile Computing, 5(6):680-694.
  13. Kempe, D., Dobra, A., and Gehrke, J. (2003). Gossip-based computation of aggregate information. In Foundations of Computer Science, 2003. Proceedings. 44th Annual IEEE Symposium on, pages 482-491. IEEE.
  14. Krumm, J. and Hariharan, R. (2004). Tempio: inside/outside classification with temperature. In Second International Workshop on Man-Machine Symbiotic Systems.
  15. Kurant, M., Gjoka, M., Butts, C. T., and Markopoulou, A. (2011). Walking on a graph with a magnifying glass: stratified sampling via weighted random walks. In Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems, pages 281-292. ACM.
  16. Lee, K., Hong, S., Kim, S. J., Rhee, I., and Chong, S. (2009). Slaw: A new mobility model for human walks. In INFOCOM 2009, IEEE, pages 855-863. IEEE.
  17. Ma, H., Zhao, D., and Yuan, P. (2014). Opportunities in mobile crowd sensing. Communications Magazine, IEEE, 52(8):29-35.
  18. Massoulié, L., Le Merrer, E., Kermarrec, A.-M., and Ganesh, A. (2006). Peer counting and sampling in overlay networks: random walk methods. In Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing, pages 123-132. ACM.
  19. Mitzenmacher, M. and Upfal, E. (2005). Probability and computing: Randomized algorithms and probabilistic analysis. Cambridge University Press.
  20. Muller, C., Chapman, L., Johnston, S., Kidd, C., Illingworth, S., Foody, G., Overeem, A., and Leigh, R. (2015). Crowdsourcing for climate and atmospheric sciences: current status and future potential. International Journal of Climatology.
  21. Musolesi, M., Hailes, S., and Mascolo, C. (2004). An ad hoc mobility model founded on social network theory. In Proceedings of the 7th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems, pages 20-24. ACM.
  22. Oke, T. R. (2002). Boundary layer climates. Routledge.
  23. Quenouille, M. H. (1949). Problems in plane sampling. The Annals of Mathematical Statistics, pages 355-375.
  24. Rhee, I., Shin, M., Hong, S., Lee, K., Kim, S. J., and Chong, S. (2011). On the levy-walk nature of human mobility. IEEE/ACM transactions on networking (TON), 19(3):630-643.
  25. Ripley, B. D. (2004). Spatial statistics, volume 575. John Wiley & Sons.
  26. Saha, A. K. and Johnson, D. B. (2004). Modeling mobility for vehicular ad-hoc networks. In Proceedings of the 1st ACM international workshop on Vehicular ad hoc networks, pages 91-92. ACM.
  27. Shrivastava, N., Buragohain, C., Agrawal, D., and Suri, S. (2004). Medians and beyond: new aggregation techniques for sensor networks. In Proceedings of the 2nd international conference on Embedded networked sensor systems, pages 239-249. ACM.
  28. Stutzbach, D., Rejaie, R., Duffield, N., Sen, S., and Willinger, W. (2009). On unbiased sampling for unstructured peer-to-peer networks. IEEE/ACM Transactions on Networking (TON), 17(2):377-390.
  29. Unger, J., Sümeghy, Z., and Zoboki, J. (2001). Temperature cross-section features in an urban area. Atmospheric Research, 58(2):117-127.
  30. van der Hoeven, F., Wandl, A., Demir, B., Dikmans, S., Hagoort, J., Moretto, M., Sefkatli, P., Snijder, F., Songsri, S., Stijger, P., et al. (2014). Sensing hotterdam: Crowd sensing the rotterdam urban heat island. SPOOL, 1(2):43-58.

Paper Citation

in Harvard Style

Koukoutsidis I. (2017). Estimating Spatial Averages of Environmental Parameters based on Mobile Crowdsensing . In Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-758-211-0, pages 15-26. DOI: 10.5220/0006110900150026

in Bibtex Style

author={Ioannis Koukoutsidis},
title={Estimating Spatial Averages of Environmental Parameters based on Mobile Crowdsensing},
booktitle={Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS,},

in EndNote Style

JO - Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS,
TI - Estimating Spatial Averages of Environmental Parameters based on Mobile Crowdsensing
SN - 978-989-758-211-0
AU - Koukoutsidis I.
PY - 2017
SP - 15
EP - 26
DO - 10.5220/0006110900150026