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Mobile Crowd-sensing Applications: Data Redundancies, Challenges, and Solutions

Published:29 October 2021Publication History
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Abstract

Conventional data collection methods that use Wireless Sensor Networks (WSNs) suffer from disadvantages such as deployment location limitation, geographical distance, as well as high construction and deployment costs of WSNs. Recently, various efforts have been promoting mobile crowd-sensing (such as a community with people using mobile devices) as a way to collect data based on existing resources. A Mobile Crowd-Sensing System can be considered as a Cyber-Physical System (CPS), because it allows people with mobile devices to collect and supply data to CPSs’ centers. In practical mobile crowd-sensing applications, due to limited budgets for the different expenditure categories in the system, it is necessary to minimize the collection of redundant information to save more resources for the investor. We study the problem of selecting participants in Mobile Crowd-Sensing Systems without redundant information such that the number of users is minimized and the number of records (events) reported by users is maximized, also known as the Participant-Report-Incident Redundant Avoidance (PRIRA) problem. We propose a new approximation algorithm, called the Maximum-Participant-Report Algorithm (MPRA) to solve the PRIRA problem. Through rigorous theoretical analysis and experimentation, we demonstrate that our proposed method performs well within reasonable bounds of computational complexity.

REFERENCES

  1. [1] Didi. Retrieved from http://www.xiaojukeji.com/news/newslisten.Google ScholarGoogle Scholar
  2. [2] Gigwalk. Retrieved from http://www.gigwalk.com.Google ScholarGoogle Scholar
  3. [3] Grubhub. Retrieved from https://www.grubhub.com/.Google ScholarGoogle Scholar
  4. [4] instacart. Retrieved from https://www.instacart.com/.Google ScholarGoogle Scholar
  5. [5] Openstreetmap. Retrieved from http://www.openstreetmap.org/.Google ScholarGoogle Scholar
  6. [6] Taskrabbit. Retrieved from http://www.taskrabbit.com.Google ScholarGoogle Scholar
  7. [7] Uber. Retrieved from https://www.uber.com/.Google ScholarGoogle Scholar
  8. [8] Waze. Retrieved from http://www.waze.com/.Google ScholarGoogle Scholar
  9. [9] AlOrabi Wael AlRahal, Rahman Sawsan Abdul, Barachi May El, and Mourad Azzam. 2016. Towards on demand road condition monitoring using mobile phone sensing as a service. Proc. Comput. Sci. 83 (2016), 345352.Google ScholarGoogle ScholarCross RefCross Ref
  10. [10] Alsina-Pages Rosa Ma, Hernandez-Jayo Unai, Alas Francesc, and Angulo Ignacio. 2016. Design of a mobile low-cost sensor network using urban buses for real-time ubiquitous noise monitoring. Sensors 17, 1 (2016), 5757. DOI: DOI: https://doi.org/doi:10.3390/s17010057Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Aly H., Basalamah A., and Youssef M.. 2017. Automatic rich map semantics identification through smartphone-based crowd-sensing. IEEE Trans. Mobile Comput. 16, 10 (Oct. 2017), 27122725. DOI: DOI: https://doi.org/10.1109/TMC.2016.2645150Google ScholarGoogle ScholarCross RefCross Ref
  12. [12] Ballesteros J., Rahman M., Carbunar B., and Rishe N.. 2012. Safe cities. A participatory sensing approach. In Proceedings of the 37th Annual IEEE Conference on Local Computer Networks. 626634. DOI: DOI: https://doi.org/10.1109/LCN.2012.6423684 Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] Basagni Stefano. 2001. Finding a maximal weighted independent set in wireless networks. Springer Telecommun. Syst. 18 (2001), 155168. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. [14] Basudan S., Lin X., and Sankaranarayanan K.. 2017. A privacy-preserving vehicular crowdsensing-based road surface condition monitoring system using fog computing. IEEE IoT J. 4, 3 (Jun. 2017), 772782. DOI: DOI: https://doi.org/10.1109/JIOT.2017.2666783Google ScholarGoogle Scholar
  15. [15] Celik Selek Ceren and Incel Özlem Durmaz. 2018. Semantic place prediction from crowd-sensed mobile phone data. J. Ambient Intell. Human. Comput. 9 (2018), 21092124.Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Chen Chao and Freedman Daniel. 2011. Hardness results for homology localization. Discr. Comput. Geom. (2011), 425448. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. [17] Chen Jiaoyan and Yang Jingsen. 2019. Maximizing coverage quality with budget constrained in mobile crowd-sensing network for environmental monitoring applications. Sensors 19, 10 (2019). DOI: DOI: https://doi.org/10.3390/s19102399Google ScholarGoogle ScholarCross RefCross Ref
  18. [18] Chu Shao-I, Liu Bing-Hong, and Nguyen Ngoc-Tu. 2019. Secure AF relaying with efficient partial relay selection scheme. Int. J. Commun. Syst. 32, 15 (2019), e4105.Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Cianciulli Danilo, Canfora Gerardo, and Zimeo Eugenio. 2017. Beacon-based context-aware architecture for crowd sensing public transportation scheduling and user habits. Proc. Comput. Sci. 109 (2017), 11101115.Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Cohn Gabe, Gupta Sidhant, Lee Tien-Jui, Morris Dan, Smith Joshua R., Reynolds Matthew S., Tan Desney S., and Patel Shwetak N.. 2012. An Ultra-low-power Human Body Motion Sensor Using Static Electric Field Sensing. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (UbiComp’12). ACM, New York, NY, 99102. DOI: DOI: https://doi.org/10.1145/2370216.2370233 Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Cuff Dana, Hansen Mark, and Kang Jerry. 2008. Urban Sensing: Out of the Woods. Commun. ACM 51, 3 (Mar. 2008), 2433. DOI: DOI: https://doi.org/10.1145/1325555.1325562 Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. [22] Garey Michael R. and Johnson David S.. 1990. Computers and Intractability; A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York, NY. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. [23] Lane Nicholas D., Eisenman Shane B., Musolesi Mirco, Miluzzo Emiliano, and Campbell Andrew T.. 2008. Urban sensing systems: Opportunistic or participatory? In Proceedings of the 9th Workshop on Mobile Computing Systems and Applications (HotMobile’08). ACM, New York, NY, 1116. DOI: DOI: https://doi.org/10.1145/1411759.1411763 Google ScholarGoogle ScholarCross RefCross Ref
  24. [24] Li Xiao and Goldberg Daniel W.. 2018. Toward a mobile crowdsensing system for road surface assessment. Comput. Environ. Urb. Syst. 69 (2018), 5162. DOI: DOI: https://doi.org/10.1016/j.compenvurbsys.2017.12.005Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Liao Chen-Chih, Hou Ting-Fang, Lin Ting-Yi, Cheng Yi-Jun, Erbad Aiman, Hsu Cheng-Hsin, and Venkatasubramania Nalini. 2014. SAIS: Smartphone augmented infrastructure sensing for public safety and sustainability in smart cities. In Proceedings of the 1st International Workshop on Emerging Multimedia Applications and Services for Smart Cities (EMASC’14). ACM, New York, NY, 38. DOI: DOI: https://doi.org/10.1145/2661704.2661706 Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Liu Bing-Hong, Nguyen Ngoc-Tu, Pham Van-Trung, and Lin Yue-Xian. 2017. Novel methods for energy charging and data collection in wireless rechargeable sensor networks. Int. J. Commun. Syst. 30, 5 (2017), e3050. DOI: DOI: https://doi.org/10.1002/dac.3050arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/dac.3050Google ScholarGoogle ScholarCross RefCross Ref
  27. [27] Liu Bing-Hong, Nguyen Ngoc-Tu, Pham Van-Trung, and Wang Wei-Sheng. 2016. Constrained node-weighted Steiner tree based algorithms for constructing a wireless sensor network to cover maximum weighted critical square grids. Comput. Commun. 81 (2016), 5260. DOI: DOI: https://doi.org/10.1016/j.comcom.2015.07.027 Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] Liu Bing-Hong, Pham Van-Trung, and Nguyen Ngoc-Tu. 2015. An efficient algorithm of constructing virtual backbone scheduling for maximizing the lifetime of dual-radio wireless sensor networks. Int. J. Distrib. Sen. Netw. 2015, Article 5 (Jan. 2015), 1 pages. DOI: DOI: https://doi.org/10.1155/2015/475159 Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Liu Yefeng, Lehdonvirta Vili, Kleppe Mieke, Alexandrova Todorka, Kimura Hiroaki, and Nakajima Tatsuo. 2010. A crowdsourcing based mobile image translation and knowledge sharing service. In Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia (MUM’10). ACM, New York, NY, Article 6, 9 pages. DOI: DOI: https://doi.org/10.1145/1899475.1899481 Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] Liu Yazhi, Niu Jianwei, and Liu Xiting. 2016. Comprehensive tempo-spatial data collection in crowd sensing using a heterogeneous sensing vehicle selection method. Pers. Ubiq. Comput. 20, 3 (Jun. 2016), 397411. DOI: DOI: https://doi.org/10.1007/s00779-016-0932-x Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Re Giuseppe Lo, Peri Daniele, and Vassallo Salvatore Davide. 2014. Urban Air Quality Monitoring Using Vehicular Sensor Networks. Springer International Publishing, Cham, 311323. DOI: DOI: https://doi.org/10.1007/978-3-319-03992-3_22Google ScholarGoogle Scholar
  32. [32] Ma H., Zhao D., and Yuan P.. 2014. Opportunities in mobile crowd sensing. IEEE Commun. Mag. 52, 8 (Aug. 2014), 2935. DOI: DOI: https://doi.org/10.1109/MCOM.2014.6871666Google ScholarGoogle ScholarCross RefCross Ref
  33. [33] Miluzzo Emiliano, Papandrea Michela, Lane Nicholas D., Sarroff Andy M., Giordano Silvia, and Campbell Andrew T.. 2011. Tapping into the vibe of the city using VibN, a continuous sensing application for smartphones. In Proceedings of 1st International Symposium on From Digital Footprints to Social and Community Intelligence (SCI’11). ACM, New York, NY, 1318. DOI: DOI: https://doi.org/10.1145/2030066.2030071 Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] Méndez D., Pérez A. J., and M. A. Labrador J. J. Marrón. 2011. P-Sense: A participatory sensing system for air pollution monitoring and control. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops’11). 344347. DOI: DOI: https://doi.org/10.1109/PERCOMW.2011.5766902Google ScholarGoogle ScholarCross RefCross Ref
  35. [35] Musolesi Mirco, Piraccini Mattia, Fodor Kristof, Corradi Antonio, and Campbell Andrew T.. 2010. Supporting energy-efficient uploading strategies for continuous sensing applications on mobile phones. In Proceedings of the 8th International Conference on Pervasive Computing (Pervasive’10). Springer-Verlag, Berlin, 355372. DOI: DOI: https://doi.org/10.1007/978-3-642-12654-3_21 Google ScholarGoogle ScholarCross RefCross Ref
  36. [36] Nguyen Dung and Phung Phu H.. 2017. A reliable and efficient wireless sensor network system for water quality monitoring. In Proceedings of the International Conference on Intelligent Environments (IE’17). IEEE, 8491. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. [37] Nguyen N. and Liu B.. 2018. The mobile sensor deployment problem and the target coverage problem in mobile wireless sensor networks are NP-Hard. IEEE Syst. J. (2018), 14. DOI: DOI: https://doi.org/10.1109/JSYST.2018.2828879Google ScholarGoogle Scholar
  38. [38] Nguyen N., Liu B., Chu S., and Weng H.. 2019. Challenges, designs, and performances of a distributed algorithm for minimum-latency of data-aggregation in multi-channel WSNs. IEEE Trans. Netw. Serv. Manage. 16, 1 (Mar. 2019), 192205. DOI: DOI: https://doi.org/10.1109/TNSM.2018.2884445 Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] Nguyen N., Liu B., Pham V., and Liou T.. 2018. An efficient minimum-latency collision-free scheduling algorithm for data aggregation in wireless sensor networks. IEEE Syst. J. 12, 3 (Sep. 2018), 22142225. DOI: DOI: https://doi.org/10.1109/JSYST.2017.2751645Google ScholarGoogle ScholarCross RefCross Ref
  40. [40] Nguyen N., Liu B., and Wang S.. 2017. Network under limited mobile sensors: New techniques for weighted target coverage and sensor connectivity. In Proceedings of the IEEE 42nd Conference on Local Computer Networks (LCN’17). 471479. DOI: DOI: https://doi.org/10.1109/LCN.2017.52Google ScholarGoogle ScholarCross RefCross Ref
  41. [41] Nguyen Ngoc-Tu, Liu Bing-Hong, and Wang Shih-Yuan. 2020. On new approaches of maximum weighted target coverage and sensor connectivity: Hardness and approximation. IEEE Transactions on Network Science and Engineering 7, 3 (2020), 1736–1751. DOI: 10.1109/TNSE.2019.2952369Google ScholarGoogle Scholar
  42. [42] Nguyen N., Liu B., and Weng H.. 2018. A distributed algorithm: Minimum-latency collision-avoidance multiple-data-aggregation scheduling in multi-channel WSNs. In Proceedings of the IEEE International Conference on Communications (ICC’18). 16. DOI: DOI: https://doi.org/10.1109/ICC.2018.8422177Google ScholarGoogle ScholarCross RefCross Ref
  43. [43] Nguyen Ngoc-Tu, Liu Bing-Hong, Pham Van-Trung, and Luo Yi-Sheng. 2016. On maximizing the lifetime for data aggregation in wireless sensor networks using virtual data aggregation trees. Comput. Netw. 105 (2016), 99110. DOI: DOI: https://doi.org/10.1016/j.comnet.2016.05.022 Google ScholarGoogle ScholarCross RefCross Ref
  44. [44] Nguyen T. N., Liu B., Chu S., Do D., and Nguyen T. D.. 2020. WRSNs: Toward an efficient scheduling for mobile chargers. IEEE Sens. J. (2020). DOI: DOI: https://doi.org/10.1109/JSEN.2020.2974255Google ScholarGoogle ScholarCross RefCross Ref
  45. [45] Ning Z., Xia F., Ullah N., Kong X., and Hu X.. 2017. Vehicular social networks: Enabling smart mobility. IEEE Commun. Mag. 55, 5 (May 2017), 1655. DOI: DOI: https://doi.org/10.1109/MCOM.2017.1600263Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. [46] Philipp D., Stachowiak J., Alt P., Dürr F., and Rothermel K.. 2013. DrOPS: Model-driven optimization for Public Sensing systems. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom’13). 185192. DOI: DOI: https://doi.org/10.1109/PerCom.2013.6526731Google ScholarGoogle ScholarCross RefCross Ref
  47. [47] Pryss R., Reichert M., Schlee W., Spiliopoulou M., Langguth B., and Probst T.. 2018. Differences between Android and iOS Users of the TrackYourTinnitus Mobile Crowdsensing mHealth Platform. In Proceedings of the IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS’18). 411416. DOI: DOI: https://doi.org/10.1109/CBMS.2018.00078Google ScholarGoogle ScholarCross RefCross Ref
  48. [48] Sakai Shuichi, Togasaki Mitsunori, and Yamazaki Koichi. 2003. A note on greedy algorithms for the maximum weighted independent set problem. Discr. Appl. Math. 126, 2–3 (2003), 313322. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. [49] Valiente Gabriel. 2003. A new simple algorithm for the maximum-weight independent set problem on circle graphs. In Proceedings of Springer ISAAC, Vol. 2906. 129137.Google ScholarGoogle ScholarCross RefCross Ref
  50. [50] Kaenel Michael von, Sommer Philipp, and Wattenhofer Roger. 2011. Ikarus: Large-scale participatory sensing at high altitudes. In Proceedings of the 12th Workshop on Mobile Computing Systems and Applications (HotMobile’11). ACM, New York, NY, 6368. DOI: DOI: https://doi.org/10.1145/2184489.2184503 Google ScholarGoogle ScholarCross RefCross Ref
  51. [51] Wang L., Zhang D., Yan Z., Xiong H., and Xie B.. 2015. effSense: A novel mobile crowd-sensing framework for energy-efficient and cost-effective data uploading. IEEE Trans. Syst. Man Cybernet.: Syst. 45, 12 (Dec. 2015), 15491563. DOI: DOI: https://doi.org/10.1109/TSMC.2015.2418283Google ScholarGoogle ScholarCross RefCross Ref
  52. [52] Wang Y. and Chen G.. 2017. Efficient data gathering and estimation for metropolitan air quality monitoring by using vehicular sensor networks. IEEE Trans. Vehic. Technol. 66, 8 (Aug. 2017), 72347248. DOI: DOI: https://doi.org/10.1109/TVT.2017.2655084Google ScholarGoogle ScholarCross RefCross Ref
  53. [53] Warrier Deepak, Wilhelm Wilbert E., Warren Jeffrey S., and Hicks Illya V.. 2005. A branch-and-price approach for the maximum weight independent set problem. ACM Netw. 46 (2005), 198209. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. [54] Wu Y., Wang Y., Hu W., and Cao G.. 2016. SmartPhoto: A resource-aware crowdsourcing approach for image sensing with smartphones. IEEE Trans. Mobile Comput. 15, 5 (May 2016), 12491263. DOI: DOI: https://doi.org/10.1109/TMC.2015.2444379 Google ScholarGoogle ScholarCross RefCross Ref
  55. [55] Xu Jia, Xiang Jinxin, and Li Yanxu. 2017. Incentivize maximum continuous time interval coverage under budget constraint in mobile crowd sensing. Wirel. Netw. 23, 5 (Jul. 2017), 15491562. DOI: DOI: https://doi.org/10.1007/s11276-016-1244-9 Google ScholarGoogle ScholarCross RefCross Ref
  56. [56] Yi L., Deng X., Wang M., Ding D., and Wang Y.. 2017. Localized confident information coverage hole detection in internet of things for radioactive pollution monitoring. IEEE Access 5 (2017), 1866518674. DOI: DOI: https://doi.org/10.1109/ACCESS.2017.2754269Google ScholarGoogle ScholarCross RefCross Ref
  57. [57] Yi Lingzhi, Deng Xianjun, Zou Zenghui, Ding Dexin, and Yang Laurence T.. 2018. Confident information coverage hole detection in sensor networks for uranium tailing monitoring. J. Parallel Distrib. Comput. 118 (2018), 5766. DOI: DOI: https://doi.org/10.1016/j.jpdc.2017.03.005 Google ScholarGoogle ScholarCross RefCross Ref
  58. [58] Zappatore M., Longo A., and Bochicchio M. A.. 2016. Using mobile crowd sensing for noise monitoring in smart cities. In Proceedings of the International Multidisciplinary Conference on Computer and Energy Science (SpliTech’16). 16. DOI: DOI: https://doi.org/10.1109/SpliTech.2016.7555950Google ScholarGoogle ScholarCross RefCross Ref
  59. [59] Zappatore Marco, Longo Antonella, Bochicchio Mario A., Zappatore Daniele, Morrone Alessandro A., and Mitri Gianluca De. 2016. A crowdsensing approach for mobile learning in acoustics and noise monitoring. In Proceedings of the 31st Annual ACM Symposium on Applied Computing (SAC’16). ACM, New York, NY, 219224. DOI: DOI: https://doi.org/10.1145/2851613.2851699 Google ScholarGoogle ScholarCross RefCross Ref
  60. [60] Zhang M., Yang P., Tian C., Tang S., and Wang B.. 2016. Toward optimum crowdsensing coverage with guaranteed performance. IEEE Sens. J. 16, 5 (Mar. 2016), 14711480. DOI: DOI: https://doi.org/10.1109/JSEN.2015.2501371Google ScholarGoogle ScholarCross RefCross Ref
  61. [61] Zhang X., Yang Z., Gong Y., Liu Y., and Tang S.. 2017. SpatialRecruiter: Maximizing sensing coverage in selecting workers for spatial crowdsourcing. IEEE Trans. Vehic. Technol. 66, 6 (Jun. 2017), 52295240. DOI: DOI: https://doi.org/10.1109/TVT.2016.2614312Google ScholarGoogle ScholarCross RefCross Ref
  62. [62] Zhang X., Yang Z., Sun W., Liu Y., Tang S., Xing K., and Mao X.. 2016. Incentives for mobile crowd sensing: A survey. IEEE Commun. Surv. Tutor. 18, 1 (Firstquarter 2016), 5467. DOI: DOI: https://doi.org/10.1109/COMST.2015.2415528Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. [63] Zheng Z., Wu F., Gao X., Zhu H., Tang S., and Chen G.. 2017. A budget feasible incentive mechanism for weighted coverage maximization in mobile crowdsensing. IEEE Trans. Mobile Comput. 16, 9 (Sep. 2017), 23922407. DOI: DOI: https://doi.org/10.1109/TMC.2016.2632721Google ScholarGoogle ScholarCross RefCross Ref

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          cover image ACM Transactions on Internet Technology
          ACM Transactions on Internet Technology  Volume 22, Issue 2
          May 2022
          582 pages
          ISSN:1533-5399
          EISSN:1557-6051
          DOI:10.1145/3490674
          • Editor:
          • Ling Liu
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          Publication History

          • Published: 29 October 2021
          • Revised: 1 October 2020
          • Accepted: 1 October 2020
          • Received: 1 August 2020
          Published in toit Volume 22, Issue 2

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