Crowdsensing sub-populations in a region

  • Robert Steele
  • Luis G. JaimesEmail author
Original Research


Crowdsensing refers to an approach for collecting of data from a large number of smart devices and sensors carried by many individuals and has been employed for numerous applications, which include pollution monitoring, traffic monitoring and noise sensing. It is an important mechanism for building applications in the smart environments enabled by the internet-of-things. However, often a given problem may dictate that samples are drawn from a defined sub-population of participants, for example based on characteristics of the participant such as location, demographics or other profile attribute, rather than from any possible member of the whole population. In this article we introduce an approach for crowdsensing with a consideration for how to sample from specific sub-populations in a region, delineated in a dimension-based way analogous to the multi-dimensional data model used in data warehousing. Simulation and performance results are provided demonstrating the approach’s ability to maintain active participants, provide coverage of the region of interest, and to be able to scalably sample the variable of interest in relation to the sub-population. This is the first work to our knowledge to address and propose an approach to the specific problem of crowdsourcing from specific attribute-defined sub-populations.


Crowdsensing Sub-population Smart city Analytics 


  1. Boulekrouche B, Jabeur N, Alimazighi Z (2016) Toward integrating grid and cloud-based concepts for an enhanced deployment of spatial data warehouses in cyber-physical system applications. J Ambient Intell Humaniz Comput 7(4):475–487CrossRefGoogle Scholar
  2. Chakeri A, Jaimes LG (2018) An incentive mechanism for crowdsensing markets with multiple crowdsourcers. IEEE Internet Things J 5(2):708–715CrossRefGoogle Scholar
  3. Clarke A, Steele R (2014) Health participatory sensing networks. Mob Inf Syst 10(3):229–242Google Scholar
  4. Clarke A, Steele R (2015) Smartphone-based public health information systems: anonymity, privacy and intervention. J Assoc Inf Sci Technol 66(12):2596–2608CrossRefGoogle Scholar
  5. Cuzzocrea A, Bellatreche L, Song I (2013) Data warehousing and OLAP over big data: current challenges and future research directions. In: Proceedings of the sixteenth international workshop on Data warehousing and OLAP, DOLAP 2013, San Francisco, CA, USA, pp 67–70, Oct 28 2013Google Scholar
  6. Falaki H, Mahajan R, Kandula S, Lymberopoulos D, Govindan R, Estrin D (2010) Diversity in smartphone usage In: Proceedings of the 8th international conference on mobile systems, applications, and services (MobiSys 2010). ACM, San Francisco, California, USA, pp 179–194, 15–18 Jun 2010Google Scholar
  7. Gao H, Liu C, Wang W, Zhao J, Song Z, Su X, Crowcroft J, Leung K (2015) A survey of incentive mechanisms for participatory sensing. Commun Surv Tutor IEEE PP(99):1Google Scholar
  8. Gori F, Folino G, Jetten MSM, Marchiori E (2011) MTR: taxonomic annotation of short metagenomic reads using clustering at multiple taxonomic ranks. Bioinformatics 27(2):196–203CrossRefGoogle Scholar
  9. Jaimes LG, Vergara-Laurens I, Labrador MA (2012) A location-based incentive mechanism for participatory sensing systems with budget constraints. In: 2012 IEEE international conference on pervasive computing and communications. IEEE, Lugano, Switzerland, pp 103–108, 19–23 Mar 2012Google Scholar
  10. Jaimes LG, Vergara-Laurens IJ, Raij A (2015) A survey of incentive techniques for mobile crowd sensing. IEEE Internet Things J 2(5):370–380CrossRefGoogle Scholar
  11. Khuller S, Moss A, Naor J (1999) The budgeted maximum coverage problem. Inf Process Lett 70(1):39–45MathSciNetCrossRefzbMATHGoogle Scholar
  12. Kuznetsov S, Paulos E (2010) Participatory sensing in public spaces: activating urban surfaces with sensor probes. In: Proceedings of the conference on designing interactive systems. ACM, Aarhus, Denmark, pp 21–30, 16–20 Aug 2010Google Scholar
  13. Lee JS, Hoh B (2010) Dynamic pricing incentive for participatory sensing. Pervasive Mob Comput 6(6):693–708CrossRefGoogle Scholar
  14. Liu Y, Bashar AE, Li F, Wang Y, Liu K (2016) Multi-copy data dissemination with probabilistic delay constraint in mobile opportunistic device-to-device networks. In: 17th IEEE international symposium on a world of wireless, mobile and multimedia networks, WoWMoM 2016. IEEE, Coimbra, Portugal, pp 1–9, 21–24 Jun 2016 Google Scholar
  15. Liu Y, Wu H, Xia Y, Wang Y, Li F, Yang P (2017a) Optimal online data dissemination for resource constrained mobile opportunistic networks. IEEE Trans Veh Technol 66(6):5301–5315CrossRefGoogle Scholar
  16. Liu Y, Xu C, Zhan Y, Liu Z, Guan J, Zhang H (2017b) Incentive mechanism for computation offloading using edge computing: a Stackelberg game approach. Comput Netw 129:399–409CrossRefGoogle Scholar
  17. Mendez D, Labrador MA (2012) Density maps: determining where to sample in participatory sensing systems. In: Third FTRA international conference on mobile, ubiquitous, and intelligent computing, MUSIC 2012. IEEE, Vancouver, Canada, pp 35–40, 26–28 Jun 2012Google Scholar
  18. Pham HN, Sim BS, Youn HY (2011) A novel approach for selecting the participants to collect data in participatory sensing. In: 11th Annual international symposium on applications and the internet, SAINT 2011. IEEE, Munich, Germany, pp 50–55, 18–21 Jul 2011Google Scholar
  19. Reddy S, Estrin D, Srivastava M (2010) Recruitment framework for participatory sensing data collections. In: International conference on pervasive computing, Springer, Berlin, pp 138–155Google Scholar
  20. Restuccia F, Das SK, Payton J (2016) Incentive mechanisms for participatory sensing: survey and research challenges. ACM Trans Sens Netw TOSN 12(2):13Google Scholar
  21. Roy N, Misra A, Cook D (2016) Ambient and smartphone sensor assisted adl recognition in multi-inhabitant smart environments. J Ambient Intell Humaniz Comput 7(1):1–19CrossRefGoogle Scholar
  22. Shilton K, Ramanathan N, Reddy S, Samanta V, Burke J, Estrin D, Hansen M, Srivastava M (2008) Participatory design of sensing networks: strengths and challenges. In: Proceedings of the tenth anniversary conference on participatory design 2008, Indiana University, Bloomington, pp 282–285Google Scholar
  23. Sun Z, Liu CH, Bisdikian C, Branch JW, Yang B (2012) Qoi-aware energy management in internet-of-things sensory environments. In: 9th annual IEEE communications society conference on sensor, Mesh and Ad Hoc communications and networks, SECON 2012. IEEE, Seoul, Korea (South), pp 19–27, 18–21 Jun 2012 Google Scholar
  24. Vaisman A, Zimányi E (2014) Data warehouse systems: design and implementation. Springer, BerlinCrossRefGoogle Scholar
  25. Yang D, Xue G, Fang X, Tang J (2012) Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing. In: The 18th annual international conference on mobile computing and networking, Mobicom’ 12. ACM, Istanbul, Turkey, pp 173–184, 22–26 Aug 2012Google Scholar
  26. Zhang X, Yang Z, Sun W, Liu Y, Tang S, Xing K, Mao X (2016) Incentives for mobile crowd sensing: a survey. IEEE Commun Surv Tutor 18(1):54–67CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Florida Polytechnic UniversityLakelandUSA

Personalised recommendations