Abstract
Exploring vehicles to conduct participatory urban sensing has become an economic and efficient sensing paradigm to pursue the smart city vision. Intuitively, having more vehicles participate in one sensing task, higher quality-of-information (QoI) can be achieved. However, more participation also implies a higher sensing cost, which include the cost pay to participated vehicles and 3G traffic cost. This paper introduces an interesting problem on how to select an appropriate set of vehicles to minimize the sensing cost while guaranteeing the required QoI. In this paper, we define a new QoI metric called coverage ratio satisfaction (CRS) with the consideration of coverage from both temporary and spatial aspects. Based on the CRS definition, we formulate the minimum cost CRS guaranteeing problem as an integer linear problem and propose a participant selection strategy called Vehicles Participant Selection (VPS). The high efficiency of VPS is extensively validated by real trace based experiments.
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References
Burke, J.A., Estrin, D., Hansen, M., Parker, A.: Participatory sensing. Center for Embedded Network Sensing (2006)
Zeng, D., Li, P., Guo, S., Miyazaki, T., Hu, J., Xiang, Y.: Energy minimization in multi-task software-defined sensor networks. IEEE Trans. Comput. 64(11), 3128–3139 (2015)
Song, Z., Liu, C.H., Wu, J., Ma, J., Wang, W.: QoI-Aware multitask-oriented dynamic participant selection with budget constraints. IEEE Trans. Veh. Technol. 63(9), 4618–4632 (2014)
Xiong, H., Zhang, D., Chen, G., Wang, L., Gauthier, V.: Crowdtasker: Maximizing coverage quality in piggyback crowdsensing under budget constraint. In: Proceedings of the 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2015)
Sheng, X., Tang, J.: Energy-efficient collaborative sensing with mobile phones. In: INFOCOM, Proceedings IEEE, pp. 1916–1924. IEEE (2012)
Zhao, Q., Zhu, Y., Zhu, H., Li, B.: Fair energy-efficient sensing task allocation in participatory sensing with smartphones. In: INFOCOM, Proceedings IEEE, pp. 1366–1374. IEEE (2014)
Wang, L., Zhang, D.: Effsense: energy-efficient and cost-effective data uploading in mobile crowdsensing. In: Proceedings of the ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, pp. 1075–1086. ACM (2013)
Zeng, D., Guo, S., Barnawi, A., Yu, S., Stojmenovic, I.: An improved stochastic modeling of opportunistic routing in vehicular CPS. IEEE Trans. Comput. 64(7), 1819–1829 (2015)
Zeng, D., Guo, S., Hu, J.: Reliable bulk-data dissemination in delay tolerant networks. IEEE Trans. Parall. Distrib. Syst. 25(8), 2180–2189 (2014)
Yao, H., Zeng, D., Huang, H., Guo, S., Barnawi, A., Stojmenovic, I.: Opportunistic offloading of deadline-constrained bulk cellular traffic in vehicular DTNs. IEEE Trans. Comput. 64(12), 3515–3527 (2015)
Devarakonda, S., Sevusu, P., Liu, H., Liu, R: Real-time air quality monitoring through mobile sensing in metropolitan areas. In: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, pp. 15. ACM (2013)
Du, R., Chen, C., Yang, B., Lu, N., Guan, X., Shen, X.: Effective urban traffic monitoring by vehicular sensor networks. IEEE Trans. Veh. Technol. 64(1), 273–286 (2015)
Bruno, R., Nurchis, M.: Robust and efficient data collection schemes for vehicular multimedia sensor networks. In: IEEE 14th International Symposium and Workshops on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1–10. IEEE (2013)
Li, Z., Liu, Y., Li, M., Wang, J.: Exploiting ubiquitous data collection for mobile users in wireless sensor networks. IEEE Trans. Parall. Distrib. Syst. 24(2), 312–326 (2013)
Palazzi, C.E., Pezzoni, F., Ruiz, P.M.: Delay-bounded data gathering in urban vehicular sensor networks. Pervasive Mob. Comput. 8(2), 180–193 (2012)
Center, S.G.C.: Shanghai taxi trace data (2012). http://wirelesslab.sjtu.edu.cn
Acknowledgements
This research was supported in part by the NSF of China (Grant No. 61402425, 61272470, 61305087,61440060),the China Postdoctoral Science Foundation funded project(2014M562086), the Fundamental Research Funds for National University, China University of Geosciences (Wuhan) (Grant No. CUG14065, CUGL150830, CUG120114).
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Yao, H., Zhang, C., Liu, C., Liang, Q., Yan, X., Hu, C. (2016). On Participant Selection for Minimum Cost Participatory Urban Sensing with Guaranteed Quality of Information. In: Guo, S., Liao, X., Liu, F., Zhu, Y. (eds) Collaborative Computing: Networking, Applications, and Worksharing. CollaborateCom 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 163. Springer, Cham. https://doi.org/10.1007/978-3-319-28910-6_17
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DOI: https://doi.org/10.1007/978-3-319-28910-6_17
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