Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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):475–487
Chakeri A, Jaimes LG (2018) An incentive mechanism for crowdsensing markets with multiple crowdsourcers. IEEE Internet Things J 5(2):708–715
Clarke A, Steele R (2014) Health participatory sensing networks. Mob Inf Syst 10(3):229–242
Clarke A, Steele R (2015) Smartphone-based public health information systems: anonymity, privacy and intervention. J Assoc Inf Sci Technol 66(12):2596–2608
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 2013
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 2010
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):1
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):196–203
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 2012
Jaimes LG, Vergara-Laurens IJ, Raij A (2015) A survey of incentive techniques for mobile crowd sensing. IEEE Internet Things J 2(5):370–380
Khuller S, Moss A, Naor J (1999) The budgeted maximum coverage problem. Inf Process Lett 70(1):39–45
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 2010
Lee JS, Hoh B (2010) Dynamic pricing incentive for participatory sensing. Pervasive Mob Comput 6(6):693–708
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
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):5301–5315
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 129:399–409
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 2012
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 2011
Reddy S, Estrin D, Srivastava M (2010) Recruitment framework for participatory sensing data collections. In: International conference on pervasive computing, Springer, Berlin, pp 138–155
Restuccia F, Das SK, Payton J (2016) Incentive mechanisms for participatory sensing: survey and research challenges. ACM Trans Sens Netw TOSN 12(2):13
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):1–19
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 282–285
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
Vaisman A, Zimányi E (2014) Data warehouse systems: design and implementation. Springer, Berlin
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 2012
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):54–67
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Steele, R., Jaimes, L.G. Crowdsensing sub-populations in a region. J Ambient Intell Human Comput 10, 1453–1462 (2019). https://doi.org/10.1007/s12652-018-0799-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-018-0799-y