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

Published: 29 October 2021 Publication History

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

[2]
Gigwalk. Retrieved from http://www.gigwalk.com.
[3]
[4]
instacart. Retrieved from https://www.instacart.com/.
[5]
Openstreetmap. Retrieved from http://www.openstreetmap.org/.
[6]
Taskrabbit. Retrieved from http://www.taskrabbit.com.
[7]
[8]
[9]
Wael AlRahal AlOrabi, Sawsan Abdul Rahman, May El Barachi, and Azzam Mourad. 2016. Towards on demand road condition monitoring using mobile phone sensing as a service. Proc. Comput. Sci. 83 (2016), 345–352.
[10]
Rosa Ma Alsina-Pages, Unai Hernandez-Jayo, Francesc Alas, and Ignacio Angulo. 2016. Design of a mobile low-cost sensor network using urban buses for real-time ubiquitous noise monitoring. Sensors 17, 1 (2016), 57–57. DOI:
[11]
H. Aly, A. Basalamah, and M. Youssef. 2017. Automatic rich map semantics identification through smartphone-based crowd-sensing. IEEE Trans. Mobile Comput. 16, 10 (Oct. 2017), 2712–2725. DOI:
[12]
J. Ballesteros, M. Rahman, B. Carbunar, and N. Rishe. 2012. Safe cities. A participatory sensing approach. In Proceedings of the 37th Annual IEEE Conference on Local Computer Networks. 626–634. DOI:https://doi.org/10.1109/LCN.2012.6423684
[13]
Stefano Basagni. 2001. Finding a maximal weighted independent set in wireless networks. Springer Telecommun. Syst. 18 (2001), 155–168.
[14]
S. Basudan, X. Lin, and K. Sankaranarayanan. 2017. A privacy-preserving vehicular crowdsensing-based road surface condition monitoring system using fog computing. IEEE IoT J. 4, 3 (Jun. 2017), 772–782. DOI:
[15]
Selek Ceren Celik and Özlem Durmaz Incel. 2018. Semantic place prediction from crowd-sensed mobile phone data. J. Ambient Intell. Human. Comput. 9 (2018), 2109–2124.
[16]
Chao Chen and Daniel Freedman. 2011. Hardness results for homology localization. Discr. Comput. Geom. (2011), 425–448.
[17]
Jiaoyan Chen and Jingsen Yang. 2019. Maximizing coverage quality with budget constrained in mobile crowd-sensing network for environmental monitoring applications. Sensors 19, 10 (2019). DOI:
[18]
Shao-I Chu, Bing-Hong Liu, and Ngoc-Tu Nguyen. 2019. Secure AF relaying with efficient partial relay selection scheme. Int. J. Commun. Syst. 32, 15 (2019), e4105.
[19]
Danilo Cianciulli, Gerardo Canfora, and Eugenio Zimeo. 2017. Beacon-based context-aware architecture for crowd sensing public transportation scheduling and user habits. Proc. Comput. Sci. 109 (2017), 1110–1115.
[20]
Gabe Cohn, Sidhant Gupta, Tien-Jui Lee, Dan Morris, Joshua R. Smith, Matthew S. Reynolds, Desney S. Tan, and Shwetak N. Patel. 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, 99–102. DOI:https://doi.org/10.1145/2370216.2370233
[21]
Dana Cuff, Mark Hansen, and Jerry Kang. 2008. Urban Sensing: Out of the Woods. Commun. ACM 51, 3 (Mar. 2008), 24–33. DOI:https://doi.org/10.1145/1325555.1325562
[22]
Michael R. Garey and David S. Johnson. 1990. Computers and Intractability; A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York, NY.
[23]
Nicholas D. Lane, Shane B. Eisenman, Mirco Musolesi, Emiliano Miluzzo, and Andrew T. Campbell. 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, 11–16. DOI:https://doi.org/10.1145/1411759.1411763
[24]
Xiao Li and Daniel W. Goldberg. 2018. Toward a mobile crowdsensing system for road surface assessment. Comput. Environ. Urb. Syst. 69 (2018), 51–62. DOI:
[25]
Chen-Chih Liao, Ting-Fang Hou, Ting-Yi Lin, Yi-Jun Cheng, Aiman Erbad, Cheng-Hsin Hsu, and Nalini Venkatasubramania. 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, 3–8. DOI:https://doi.org/10.1145/2661704.2661706
[26]
Bing-Hong Liu, Ngoc-Tu Nguyen, Van-Trung Pham, and Yue-Xian Lin. 2017. Novel methods for energy charging and data collection in wireless rechargeable sensor networks. Int. J. Commun. Syst. 30, 5 (2017), e3050. DOI:https://doi.org/10.1002/dac.3050arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/dac.3050
[27]
Bing-Hong Liu, Ngoc-Tu Nguyen, Van-Trung Pham, and Wei-Sheng Wang. 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), 52–60. DOI:https://doi.org/10.1016/j.comcom.2015.07.027
[28]
Bing-Hong Liu, Van-Trung Pham, and Ngoc-Tu Nguyen. 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:https://doi.org/10.1155/2015/475159
[29]
Yefeng Liu, Vili Lehdonvirta, Mieke Kleppe, Todorka Alexandrova, Hiroaki Kimura, and Tatsuo Nakajima. 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:https://doi.org/10.1145/1899475.1899481
[30]
Yazhi Liu, Jianwei Niu, and Xiting Liu. 2016. Comprehensive tempo-spatial data collection in crowd sensing using a heterogeneous sensing vehicle selection method. Pers. Ubiq. Comput. 20, 3 (Jun. 2016), 397–411. DOI:https://doi.org/10.1007/s00779-016-0932-x
[31]
Giuseppe Lo Re, Daniele Peri, and Salvatore Davide Vassallo. 2014. Urban Air Quality Monitoring Using Vehicular Sensor Networks. Springer International Publishing, Cham, 311–323. DOI:
[32]
H. Ma, D. Zhao, and P. Yuan. 2014. Opportunities in mobile crowd sensing. IEEE Commun. Mag. 52, 8 (Aug. 2014), 29–35. DOI:
[33]
Emiliano Miluzzo, Michela Papandrea, Nicholas D. Lane, Andy M. Sarroff, Silvia Giordano, and Andrew T. Campbell. 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, 13–18. DOI:https://doi.org/10.1145/2030066.2030071
[34]
D. Méndez, A. J. Pérez, M. A. Labrador and 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). 344–347. DOI:
[35]
Mirco Musolesi, Mattia Piraccini, Kristof Fodor, Antonio Corradi, and Andrew T. Campbell. 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, 355–372. DOI:https://doi.org/10.1007/978-3-642-12654-3_21
[36]
Dung Nguyen and Phu H. Phung. 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, 84–91.
[37]
N. Nguyen and B. Liu. 2018. The mobile sensor deployment problem and the target coverage problem in mobile wireless sensor networks are NP-Hard. IEEE Syst. J. (2018), 1–4. DOI:
[38]
N. Nguyen, B. Liu, S. Chu, and H. Weng. 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), 192–205. DOI:https://doi.org/10.1109/TNSM.2018.2884445
[39]
N. Nguyen, B. Liu, V. Pham, and T. Liou. 2018. An efficient minimum-latency collision-free scheduling algorithm for data aggregation in wireless sensor networks. IEEE Syst. J. 12, 3 (Sep. 2018), 2214–2225. DOI:
[40]
N. Nguyen, B. Liu, and S. Wang. 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). 471–479. DOI:
[41]
Ngoc-Tu Nguyen, Bing-Hong Liu, and Shih-Yuan Wang. 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:https://doi.org/10.1109/TNSE.2019.2952369
[42]
N. Nguyen, B. Liu, and H. Weng. 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). 1–6. DOI:
[43]
Ngoc-Tu Nguyen, Bing-Hong Liu, Van-Trung Pham, and Yi-Sheng Luo. 2016. On maximizing the lifetime for data aggregation in wireless sensor networks using virtual data aggregation trees. Comput. Netw. 105 (2016), 99–110. DOI:https://doi.org/10.1016/j.comnet.2016.05.022
[44]
T. N. Nguyen, B. Liu, S. Chu, D. Do, and T. D. Nguyen. 2020. WRSNs: Toward an efficient scheduling for mobile chargers. IEEE Sens. J. (2020). DOI:
[45]
Z. Ning, F. Xia, N. Ullah, X. Kong, and X. Hu. 2017. Vehicular social networks: Enabling smart mobility. IEEE Commun. Mag. 55, 5 (May 2017), 16–55. DOI:
[46]
D. Philipp, J. Stachowiak, P. Alt, F. Dürr, and K. Rothermel. 2013. DrOPS: Model-driven optimization for Public Sensing systems. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom’13). 185–192. DOI:
[47]
R. Pryss, M. Reichert, W. Schlee, M. Spiliopoulou, B. Langguth, and T. Probst. 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). 411–416. DOI:
[48]
Shuichi Sakai, Mitsunori Togasaki, and Koichi Yamazaki. 2003. A note on greedy algorithms for the maximum weighted independent set problem. Discr. Appl. Math. 126, 2–3 (2003), 313–322.
[49]
Gabriel Valiente. 2003. A new simple algorithm for the maximum-weight independent set problem on circle graphs. In Proceedings of Springer ISAAC, Vol. 2906. 129–137.
[50]
Michael von Kaenel, Philipp Sommer, and Roger Wattenhofer. 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, 63–68. DOI:https://doi.org/10.1145/2184489.2184503
[51]
L. Wang, D. Zhang, Z. Yan, H. Xiong, and B. Xie. 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), 1549–1563. DOI:
[52]
Y. Wang and G. Chen. 2017. Efficient data gathering and estimation for metropolitan air quality monitoring by using vehicular sensor networks. IEEE Trans. Vehic. Technol. 66, 8 (Aug. 2017), 7234–7248. DOI:
[53]
Deepak Warrier, Wilbert E. Wilhelm, Jeffrey S. Warren, and Illya V. Hicks. 2005. A branch-and-price approach for the maximum weight independent set problem. ACM Netw. 46 (2005), 198–209.
[54]
Y. Wu, Y. Wang, W. Hu, and G. Cao. 2016. SmartPhoto: A resource-aware crowdsourcing approach for image sensing with smartphones. IEEE Trans. Mobile Comput. 15, 5 (May 2016), 1249–1263. DOI:https://doi.org/10.1109/TMC.2015.2444379
[55]
Jia Xu, Jinxin Xiang, and Yanxu Li. 2017. Incentivize maximum continuous time interval coverage under budget constraint in mobile crowd sensing. Wirel. Netw. 23, 5 (Jul. 2017), 1549–1562. DOI:https://doi.org/10.1007/s11276-016-1244-9
[56]
L. Yi, X. Deng, M. Wang, D. Ding, and Y. Wang. 2017. Localized confident information coverage hole detection in internet of things for radioactive pollution monitoring. IEEE Access 5 (2017), 18665–18674. DOI:
[57]
Lingzhi Yi, Xianjun Deng, Zenghui Zou, Dexin Ding, and Laurence T. Yang. 2018. Confident information coverage hole detection in sensor networks for uranium tailing monitoring. J. Parallel Distrib. Comput. 118 (2018), 57–66. DOI:https://doi.org/10.1016/j.jpdc.2017.03.005
[58]
M. Zappatore, A. Longo, and M. A. Bochicchio. 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). 1–6. DOI:
[59]
Marco Zappatore, Antonella Longo, Mario A. Bochicchio, Daniele Zappatore, Alessandro A. Morrone, and Gianluca De Mitri. 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, 219–224. DOI:https://doi.org/10.1145/2851613.2851699
[60]
M. Zhang, P. Yang, C. Tian, S. Tang, and B. Wang. 2016. Toward optimum crowdsensing coverage with guaranteed performance. IEEE Sens. J. 16, 5 (Mar. 2016), 1471–1480. DOI:
[61]
X. Zhang, Z. Yang, Y. Gong, Y. Liu, and S. Tang. 2017. SpatialRecruiter: Maximizing sensing coverage in selecting workers for spatial crowdsourcing. IEEE Trans. Vehic. Technol. 66, 6 (Jun. 2017), 5229–5240. DOI:
[62]
X. Zhang, Z. Yang, W. Sun, Y. Liu, S. Tang, K. Xing, and X. Mao. 2016. Incentives for mobile crowd sensing: A survey. IEEE Commun. Surv. Tutor. 18, 1 (Firstquarter 2016), 54–67. DOI:
[63]
Z. Zheng, F. Wu, X. Gao, H. Zhu, S. Tang, and G. Chen. 2017. A budget feasible incentive mechanism for weighted coverage maximization in mobile crowdsensing. IEEE Trans. Mobile Comput. 16, 9 (Sep. 2017), 2392–2407. DOI:

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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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2021
Accepted: 01 October 2020
Revised: 01 October 2020
Received: 01 August 2020
Published in TOIT Volume 22, Issue 2

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Author Tags

  1. Data redundancy
  2. mobile crowd-sensing
  3. optimization
  4. participatory sensing

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