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Comprehensive tempo-spatial data collection in crowd sensing using a heterogeneous sensing vehicle selection method

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Abstract

The wide distribution of mobile vehicles installed with various sensing devices and wireless communication interfaces has made vehicular mobile crowd sensing possible in practice. However, owing to the heterogeneity of vehicles in terms of sensing interfaces and mobilities, collecting comprehensive tempo-spatial sensing data with only one sensing vehicle is impossible. Moreover, the sensing data collected may expire in the future; as a result, sensing vehicles may have to continuously collect sensing data to ensure the relevance of such data. Although including more sensing vehicles can improve the quality of collected sensing data, this step also requires additional cost. Thus, how to continuously collect comprehensive tempo-spatial sensing data with a limited number of heterogeneous sensing vehicles is a critical issue in vehicular mobile crowd sensing systems. In this work, a heterogeneous sensing vehicle selection (HVS) method for the collection of comprehensive tempo-spatial sensing data is proposed. On the basis of the spatial distribution and sensing interfaces of sensing vehicles and the tempo-spatial coverage of collected sensing data, a utility function is designed in HVS to estimate the sensing capacity of sensing vehicles. Then, according to the utilities of sensing vehicles and the restriction on the number of recruited sensing vehicles, sensing vehicle selection is modeled as a knapsack problem. Finally, a greedy optimal sensing vehicle selection algorithm is designed. Real trace-driven simulations show that the HVS algorithm can collect sensing data with a higher coverage ratio in a more uniform and continuous manner than existing mobile crowd sensing methods.

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References

  1. Ganti RK, Ye F, Lei H (2011) Mobile crowdsensing: current state and future challenges. IEEE Commun Mag 49:32–39

    Article  Google Scholar 

  2. Guo B, Yu Z, Zhou X, Zhang D From participatory sensing to mobile crowd sensing. In: IEEE international conference on pervasive computing and communications workshops (PERCOM 2014 Workshops), pp 593–598

  3. Khan WZ, Xiang Y, Aalsalem MY, Arshad Q (2013) Mobile phone sensing systems: a survey. IEEE Commun Surv Tutor 15:402–427

    Article  Google Scholar 

  4. Devarakonda S, Sevusu P, Liu H, Liu R, Iftode L, Nath B (2013) Real-time air quality monitoring through mobile sensing in metropolitan areas. In: Proceedings of the 2nd ACM SIGKDD international workshop on urban computing 2013, pp 1–8

  5. Zhou P, Cheng Z, Li M Smart traffic monitoring with participatory sensing. In: Proceedings of the 11th ACM conference on embedded networked sensor systems (SenSys’13), pp 1–2

  6. Banerjee R, Sinha A, Saha A (2013) Participatory sensing based traffic condition monitoring using horn detection. In: Proceedings of the 28th annual ACM symposium on applied computing, pp 567–569

  7. Perttunen M, Kostakos V, Riekki J, Ojala T (2015) Urban traffic analysis through multi-modal sensing. Pers Ubiquitous Comput 19:709–721

    Article  Google Scholar 

  8. Liu Y, Li X (2015) Heterogeneous participant recruitment for comprehensive vehicle sensing. Plos One 10:1–20

    Google Scholar 

  9. Dutta P, Aoki PM, Kumar N, Mainwaring A, Myers C, Willet W, Woodruff A Common sense: participatory urban sensing using a network of handheld air quality monitors. In: Proceedings of the 7th ACM conference on embedded networked sensor systems (SenSys), pp 349–350

  10. Zheng Y, Liu T, Wang Y, Zhu Y, Liu Y, Chang E Diagnosing new york city’s noises with ubiquitous data. In: Proceedings of the ACM international joint conference on pervasive and ubiquitous computing (UbiComp 2014), pp 715–725

  11. Pankratius V, Lind F, Coster A, Erickson P, Semeter J (2014) Mobile crowd sensing in space weather monitoring: the mahali project. IEEE Commun Mag 52:22–28

    Article  Google Scholar 

  12. Zhou P, Zheng Y, Li M (2014) How long to wait? Predicting bus arrival time with mobile phone based participatory sensing. IEEE Trans Mob Comput 13:1228–1241

    Article  Google Scholar 

  13. Kostakos V, Camacho T, Mantero C (2013) Towards proximity-based passenger sensing on public transport buses. Pers Ubiquitous Comput 17:1807–1816

    Article  Google Scholar 

  14. Chon Y, Lane ND, Kim Y, Zhao F, Cha H, Understanding the coverage and scalability of place-centric crowd sensing. In: The ACM international joint conference on pervasive and ubiquitous computing (UbiComp’13), pp 3–12

  15. Lee J-S, Hoh B Sell your experiences: a market mechanism based incentive for participatory sensing, In: IEEE international conference on pervasive computing and communications (PerCom’10), pp 60–68

  16. Jaimes LG, Vergara-Laurens I, Labrador MA A location-based incentive mechanism for participatory sensing systems with budget constraints. In: IEEE international conference on pervasive computing and communications (PerCom’12), pp 103–108

  17. Ioannis B, Vana K Mobile stream sampling under time constraints, In: IEEE 14th international conference on mobile data management (MDM’13), pp 227–236

  18. Liu B, Jiang Y, Sha F, Govindan R Cloud-enabled privacy-preserving collaborative learning for mobile sensing, In: The 10th ACM conference on embedded network sensor aystems (SenSys’12), pp 57–70

  19. Jayaraman PP, Perera C, Georgakopoulos D, Zaslavsky A (2013) Efficient opportunistic sensing using mobile collaborative platform mosden, In: The 9th IEEE international conference on collaborative computing: networking, applications and worksharing, pp 1–10

  20. Sheng X, Tang J, Zhang W Energy-efficient collaborative sensing with mobile phones, In: The 31rd annual IEEE international conference on computer communications (INFOCOM’12), pp 1916–1924

  21. Chon Y, Lane ND, Kim Y, Zhao F, Cha H, Understanding the coverage and scalability of place-centric crowd sensing, In: Proceedings of the 2013 ACM international joint conference on pervasive and ubiquitous computing (UbiComp 2013), pp 3–12

  22. Ho C-J, Vaughan JW Online task assignment in crowdsourcing markets. In: AAAI, vol 12, pp 45–51

  23. Reddy S, Estrin D, Srivastava M (2010) Recruitment framework for participatory sensing data collections. Pervasive Comput 138–155

  24. Tuncay G, Benincasa G, Helmy A Autonomous and distributed recruitment and data collection framework for opportunistic sensing. In: Proceedings of Mobicom’12, pp 407–410

  25. Ahmed A, Yasumoto K, Yamauchi Y, Ito M Distance and time based node selection for probabilistic coverage in people-centric sensing. In: Proceedings of the 8th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks (SECON 2011), pp 134–142

  26. Hachem S, Pathak A, Issarny V Probabilistic registration for large-scale mobile participatory sensing. In: Proceedings of the IEEE international conference on pervasive computing and communications (PerCom 2013), pp 132–140

  27. Zhang D, Xiong H, Wang L, Chen G Crowdrecruiter: selecting participants for piggyback crowdsensing under probabilistic coverage constraint. In: Proceedings of the ACM international joint conference on pervasive and ubiquitous computing (UbiComp 2014), pp 703–714

  28. Song Z, Liu CH, Wu J, Ma J, Wang W (2014) Qoi-aware multitask-oriented dynamic participant selection with budget constraints. IEEE Trans Veh Technol 63:4618–4632

    Article  Google Scholar 

  29. Hull B, Bychkovsky V, Zhang Y, Chen K, Goraczko M, Miu A, Shih E, Balakrishnan H, Madden S Cartel: a distributed mobile sensor computing system. In: The 4th ACM conference on embedded networked sensor systems (Sensys’06), pp 125–138

  30. Hu S-C, Wang Y-C, Huang C-Y, Tsengy Y-C, Kuoz L-C, Chen C-Y Vehicular sensing system for co2 monitoring applications. In: IEEE VTS Asia pacific wireless communications symposium (APWCS’09), pp 168–171

  31. Görmer S, Park S-B, Egbert P (2009) Vision-based rain sensing with an in-vehicle camera. In: IEEE intelligent vehicles symposium, pp 279–284

  32. Hyyppä J, Jaakkola A, Hyyppä H (2009) Map updating and change detection using vehicle-based laser scanning. In: The IEEE joint urban remote sensing event, pp 1–6

  33. Cortez RA, Luna J-M, Fierro R, Wood J Multi-vehicle testbed for decentralized environmental sensing. In: The IEEE international conference on robotics and automation (ICRA’10), pp 3052–3058

  34. Zhu Y, Li Z, Zhu H, Li M, Zhang Q (2013) A compressive sensing approach to urban traffic estimation with probe vehicles. IEEE Trans Mob Comput 12:2289–2302

    Article  Google Scholar 

  35. Jang J, Baik N (2012) Diagnosing hazardous road surface conditions through probe vehicle as mobile sensing platform. In: The international conference on winter maintenance and surface transportation weather, pp 54–60

  36. Yuan J, Zheng Y, Xie X, Sun G Driving with knowledge from the physical world. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’11), pp 316–324

  37. Yuan J, Zheng Y, Zhang C, Xie W, Xie X, Sun G, Huang Y T-drive: driving directions based on taxi trajectories. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information aystems (GIS’10), pp 99–108

  38. Huang H, Zhang D, Zhu Y, Li M, Wu M-Y (2012) A metropolitan taxi mobility model from real gps traces. J Univ Comput Sci 18:1072–1092

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61572060, 61370197, 61170296 and 61190125), the R&D Program (2013BAH35F01) and key technologies R&D program project of Tangshan (15130251a).

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Authors and Affiliations

  1. College of Information Engineering, North China University of Science and Technology, Tangshan, 063000, China

    Yazhi Liu

  2. State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China

    Jianwei Niu & Xiting Liu

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  1. Yazhi Liu
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  2. Jianwei Niu
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  3. Xiting Liu
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Correspondence to Jianwei Niu.

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Liu, Y., Niu, J. & Liu, X. Comprehensive tempo-spatial data collection in crowd sensing using a heterogeneous sensing vehicle selection method. Pers Ubiquit Comput 20, 397–411 (2016). https://doi.org/10.1007/s00779-016-0932-x

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  • DOI: https://doi.org/10.1007/s00779-016-0932-x

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