Abstract
Crowdsensing applications are becoming more popular with time. In this work, we present a crowdsensing application for capturing road traffic information to help citizens to get real-time traffic condition. Such real-time information can be beneficial for citizens to plan their journeys. However, crowdsensing in this specific case, generates spatio-temporal data collected from numerous users; storing and processing such data in real-time can be quite challenging. The MapReduce programming approach has been proposed for processing data in this context. The MapReduce jobs used to process and analyze the data captured from the crowdsensing application are presented as well as the design of the crowdsensing application. Implementation of the MapReduce jobs proposed shows that data can be effectively processed and analyzed to present near real-time information about the road traffic flow while at the same time discarding used data which is no longer required.
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References
Rodrigues, J.G., Aguiar, A., Barros, J.: SenseMyCity: crowdsourcing an urban sensor. arXiv preprint arXiv:1412.2070 (2014)
Campbell, A.T., et al.: The rise of people-centric sensing. IEEE Internet Comput. 12, 12–21 (2008)
Ganti, K., Ye, F., Lei, H.: Mobile crowdsensing: current state and future challenges. IEEE Commun. Mag. 49, 32–39 (2011)
InfoSec Institute: Crowdsensing: state of the art and privacy aspects, July 2014. http://resources.infosecinstitute.com/crowdsensing-state-art-privacy-aspects/
Lee, J., Hoh, B.: Sell your experiences: a market mechanism based incentive for participatory sensing. In: Proceedings of IEEE PerCom 2012, Manheim, Germany (2010)
Tham, C., Luo, T.: Quality of contributed service and market equilibrium for participatory sensing. IEEE Trans. Mob. Comput. 14(4), 829–842 (2015)
Yang, D., Xue, G., Fang, X.: Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing. In: Proceedings of ACM MobiCom 2012, Istanbul, Turkey (2012)
Yang, H., Parthasarathy, S.: Mining spatial and spatio-temporal patterns in scientific data. In: Proceedings of the 22nd International Conference on Data Engineering Workshops (ICDEW 2006), p. 146 (2006)
Venkateswara Rao, K., Govardhan, A., Chalapati Rao, K.V.: Spatiotemporal data mining: issues, tasks and applications. Int. J. Comput. Sci. Eng. Surv. (IJCSES) 3(1), 39 (2012)
Shekhar, S., et al.: Spatiotemporal data mining: a computational perspective. ISPRS Int. J. Geo-Inf. 4, 2306–2338 (2015). https://doi.org/10.3390/ijgi4042306
Bogorny, V., Shekhar, S.: Spatial and spatio-temporal data mining. In: The Proceedings of the IEEE 10th International Conference on Data Mining (ICDM), Sydney, NSW, Australia (2010)
Pallavi, A.R., Annapurna, V.K.: Enforcing security for smartphone user by crowdsourcing model using internet of things. Int. J. Adv. Res. Comput. Sci. Technol. (IJARCST 2016) 4(2), 1217 (2016)
Gilbert, P., Cox, L.P., Jung, J., Wetherall, D.: Toward trustworthy mobile sensing. In: Proceedings of the Eleventh Workshop on Mobile Computing Systems, HotMobile 2010, Annapolis, Maryland, pp. 31–36 (2010)
Talasila, M., Curtmola, R., Borcea, C.: Handbook of Sensor Networking: Advanced Technologies and Applications. CRC Press, Boca Raton (2015)
Bhatlavande, A.S., Phatak, A.A.: Data aggregation techniques in wireless sensor networks: literature survey. Int. J. Comput. Appl. 115(10), 4 (2015)
Tham, C.-K., Sun, W.: A Spatio-temporal incentive scheme with consumer demand awareness for participatory sensing. J. Comput. Netw. 108, 148–159 (2016)
Yaqooba, I., et al.: Big data: from beginning to future. Int. J. Inf. Manag. 36, 1231–1247 (2016)
Marz, N., Warren, J.: Big Data: Principles and Best Practices of Scalable Real-Time Data Systems. Manning Publications Co., Shelter Island (2015)
Gill, A.Q., Phennel, N., Lane, D., Phung, V.L.: IoT-enabled emergency information supply chain architecture for elderly people: the Australian context. Inf. Syst. 58, 75–86 (2016)
Jiang, D., Ooi, B.C., Shi, L., Wu, S.: The performance of MapReduce: an in-depth study. J. Proc. VLDB Endow. 3(1–2), 472–483 (2010)
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Armoogum, S., Munchetty-Chendriah, S. (2018). Using the MapReduce Approach for the Spatio-Temporal Data Analytics in Road Traffic Crowdsensing Application. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-00916-8_38
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