Abstract
Abstract
Mobile crowd sensing (MCS) is an emerging sensing platform that concedes mobile users to efficiently collect data and share information with the MCS service providers. Despite its benefits, a key challenge in MCS is how beneficially select a minimum subset of participants from the large user pool to achieve the desired level of coverage. In this paper, we propose several algorithms to choose a minimum number of mobile users(or participants) who met the desired level of coverage. We consider two different cases, in the first case, only a single participant is allowed to upload a data packet for a particular target, whereas for the other case, two participants are allowed to do the same (provided that the target is covered by more than one participants). An optimal solution to the problem can be found by solving integer linear programmings (ILP’s). However, due to the exponential complexity of the ILP problem, for the large input size, it is infeasible from the point of execution time as well as the requirement of having the necessary information about all the participants in a central location. We also propose a distributed participant selection algorithm considering both the cases, which are dynamic in nature and run at every user. Each user exchanges their message with the neighbors to decide whether to remain idle or active. A series of experiments are executed to measure the performance of the proposed algorithms. Simulation results reveal the proximity of the proposed distributed algorithm compared to the optimal result providing the same coverage.
- Azmy SB, Zorba N, Hassanein HS (2018) Quality of coverage: A novel approach to coverage for mobile crowd sensing systems. In: 2018 Global Information Infrastructure and Networking Symposium (GIIS), pp 1–5Google Scholar
- Crowdsourcing with smartphonesInternet Comput IEEE201216364410.1109/MIC.2012.70Google ScholarDigital Library
- Maximizing coverage quality with budget constrained in mobile crowd-sensing network for environmental monitoring applicationsSensors20191910239910.3390/s19102399Google ScholarCross Ref
- Chen L, Wang L, Zhang D, Li S, Pan G (2016) Enup: energy-efficient data uploading for mobile crowd sensing applications. In: 2016 Intl IEEE Conferences on Ubiquitous Intelligence Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pp 1074–1078Google Scholar
- Chon Y, Lane N D, Kim Y, Zhao F, Cha H (2013) Understanding the coverage and scalability of place-centric crowdsensing. In: Proceedings of the International Joint Conference on pervasive and ubiquitous computing, pp 3–12Google Scholar
- Coverage of targets in mobile sensor networks with restricted mobilityIEEE Access20186108031081310.1109/ACCESS.2018.2801941Google ScholarCross Ref
- Multiobjective optimization model for service node selection based on a tradeoff between quality of service and resource consumption in mobile crowd sensingIEEE Internet Things J201741258268Google Scholar
- El Khatib RF, Zorba N, Hassanein HS (2018) Cost-efficient multi-tasking in coverage-aware mobile crowd sensing. In: 2018 14th International Wireless Communications Mobile Computing Conference (IWCMC)Google Scholar
- Mobile crowdsensing: current state and future challengesIEEE Commun Mag20114911323910.1109/MCOM.2011.6069707Google ScholarCross Ref
- Activecrowd: a framework for optimized multitask allocation in mobile crowdsensing systemsIEEE Trans Hum-Mach Syst201747339240310.1109/THMS.2016.2599489Google ScholarCross Ref
- Worker-contributed data utility measurement for visual crowdsensing systemsIEEE Trans Mob Comput20171682379239110.1109/TMC.2016.2620980Google ScholarDigital Library
- Quality-aware pricing for mobile crowdsensingIEEE/ACM Trans Netw20182641728174110.1109/TNET.2018.2846569Google ScholarDigital Library
- Coverage-guaranteed and energy-efficient participant selection strategy in mobile crowdsensingIEEE Internet of Things J2019623202321110.1109/JIOT.2018.2880463Google Scholar
- Jin H, Su L, Chen D, Nahrstedt K, Xu J (2015) Quality of information aware incentive mechanisms for mobile crowd sensing systems. In: Proceedings of the 16th ACM International Symposium on mobile ad hoc networking and computing, MobiHoc ’15, page 167-176, New York, NY, USA. Association for Computing MachineryGoogle Scholar
- Li H, Li T, Li F, Wang W, Wang Y (2016) Enhancing participant selection through caching in mobile crowd sensing. In: 2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS), pp. 1–10Google Scholar
- A cooperative-based model for smart-sensing tasks in fog computingIEEE Access20175212962131110.1109/ACCESS.2017.2756826Google ScholarCross Ref
- Multi-objective optimization for multi-task allocation in mobile crowd sensingProc Comput Sci201915536036810.1016/j.procs.2019.08.051Google ScholarCross Ref
- Liu J, Shen H, Zhang X (2016) A survey of mobile crowdsensing techniques: a critical component for the internet of things. In: 2016 25th International Conference on Computer Communication and Networks (ICCCN), pp. 1– 6Google Scholar
- Energyaware participant selection for smartphone-enabled mobile crowd sensingIEEE Syst J20171131435144610.1109/JSYST.2015.2430362Google ScholarCross Ref
- Data interpolation for participatory sensing systemsPervasive Mob Comput2013913214810.1016/j.pmcj.2012.11.001Google ScholarDigital Library
- Coverage in mobile mobile wireless sensor networks (m-wsn): a surveyComput Commun201711013315010.1016/j.comcom.2017.06.010Google ScholarDigital Library
- Mohan P, Padmanabhan VN, Ramjee R (2008) Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In: in Proceedings of the 6th ACM Conference on embedded network sensor systems (Sen- Sys), pp. 323–336Google Scholar
- Mondal S, Ghosh S, Khatua S, Das R, Biswas U (2018) Cost effective algorithms for participant selection problem in mobile crowd sensing environment. In: Fifth international conference on parallel, distributed and grid computing (PDGC), pp 453–458. DOI: https://doi.org/10.1109/PDGC.2018.8745988Google Scholar
- Queuing algorithm for effective target coverage in mobile crowd sensingIEEE Internet Things J2017441046105510.1109/JIOT.2017.2688366Google ScholarCross Ref
- Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual frameworkHCIS20166124Google Scholar
- Data quality guided incentive mechanism design for crowdsensingIEEE Trans Mob Comput201817230731910.1109/TMC.2017.2714668Google ScholarDigital Library
- Song Z, Zhang B, Liu CH, Vasilakos AV, Ma J, Wang W (2014) Qoi-aware energy-efficient participant selection. In: 2014 Eleventh Annual IEEE International Conference on sensing, communication, and networking (SECON), pp 248–256Google Scholar
- Coverage-oriented task assignment for mobile crowdsensingIEEE Internet Things J2020787407741810.1109/JIOT.2020.2984826Google ScholarCross Ref
- effsense: a novel mobile crowd-sensing framework for energy-efficient and cost-effective data uploadingIEEE Trans Syst Man Cybern Syst201545121549156310.1109/TSMC.2015.2418283Google ScholarCross Ref
- A context-driven worker selection framework for crowd-sensingInt J Distrib Sens Netw20161216Google Scholar
- Wang J, Tang J, Yang D, Wang E, Xue G (2016b) Quality-aware and fine-grained incentive mechanisms for mobile crowdsensing. In: 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS), pp 354–363Google Scholar
- Wang J, Wang Y, Zhang D, Wang F, He Y, Ma L (2017) Psallocator: nulti-task allocation for participatory sensing with sensing capability constraints. In: Proceedings of the 2017 ACM Conference on computer supported cooperative work and social computing, CSCW ’17, page 1139-1151, New York, NY, USA. Association for Computing MachineryGoogle Scholar
- Multi-task allocation in mobile crowd sensing with individual task quality assuranceIEEE Trans Mob Comput20181792101211310.1109/TMC.2018.2793908Google ScholarDigital Library
- Mobile crowd sensing task optimal allocation: a mobility pattern matching perspectiveFront Comput Sci2018120223124410.1007/s11704-017-7024-6in pressGoogle ScholarDigital Library
- Task allocation in mobile crowd sensing: State-of-the-art and future opportunitiesIEEE Internet Things J2018553747375710.1109/JIOT.2018.2864341Google ScholarCross Ref
- Maximizing spatial-temporal coverage in mobile crowd-sensing based on public transports with predictable trajectoryInt J Distrib Sens Netw2018148155014771879535110.1177/1550147718795351Google Scholar
- Icrowd: near-optimal task allocation for piggyback crowdsensingIEEE Trans Mob Comput201615082010202210.1109/TMC.2015.2483505Google ScholarDigital Library
- Crowdsensing the speaker count in the wild: implications and applicationsIEEE Commun Mag20145210929910.1109/MCOM.2014.6917408Google ScholarCross Ref
- On designing data quality-aware truth estimation and surplus sharing method for mobile crowdsensingIEEE J Sel Areas Commun201735483284710.1109/JSAC.2017.2676898Google ScholarDigital Library
- Yu J, Xiao M, Gao G, Hu C (2016) Minimum cost spatial-temporal task allocation in mobile crowdsensing. volume 9798, pages 262–271, 08Google Scholar
- Zhang D, Xiong H, Wang L, Chen G (2014) Crowdrecruiter: selecting participants for piggyback crowdsensing under probabilistic coverage constraint. In: UbiComp ’14: Proceedings of the 2014 ACM International Joint Conference on pervasive and ubiquitous computing, pp 703–714Google Scholar
- Quality-aware sensing coverage in budget-constrained mobile crowdsensing networksIEEE Trans Veh Technol20166597698770710.1109/TVT.2015.2490679Google ScholarCross Ref
- Zhou P, Chen Z, Li M (2013) Smart traffic monitoring with participatory sensing. In: Proceedings of the 11th ACM Conference on embedded networked sensor systems (SenSys), pp 1–2Google Scholar
Index Terms
- Participant selection algorithms for large-scale mobile crowd sensing environment
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