Abstract
The Internet of Things (IoT) paves the way to interact with the smart objects namely sensors, hardware, circuits and software. Research in IoT ensures that collecting, processing and distributing the data needs to be improved to carryout data aggregation, processing and dissemination tasks of IoT data management. Data Processing focuses on the characteristics Velocity, Volume, Variety, Variability, and Veracity. IoT Data Management may further be categorized as Communication, Storage and Processing. Data communication involves data processing among objects, sensor data and hardware. To store the data, Cloud or distributed storage is used and processing involves filtering and analytics. Data dissemination distributes the processed data to end users. Message-delay in multi-hop massive IoT network is significantly optimized. This chapter enumerates the IoT data management frameworks, challenges and issues. Also, deployment of IoT Data management for smart home and smart city is described.
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References
Cooper, J., James, A.: Challenges for database management in the Internet of Things. IETE Techn. Rev. 26, 320–329 (2009)
Sabrina, B., Djallel, E.B., Azeddine, B., Homero, T.C.: Big data challenges and data aggregation strategies in wireless sensor networks. IEEE Access Spec. Sect. Real-Time Edge Anal. Big Data Int. Things 6, 20558–20571 (2018)
Tole, A.A.: Big data challenges. Database Syst. J. 4, 31–40 (2013)
Catalin, C., Monica, C.: Large data management in IoT application. In: IEEE Conference, pp. 1–5 (2016)
Abu, E.M.: Data management for the Internet of Things: green directions. IEEE, pp. 386–390 (2012)
Yuchao, Z., Suparna, D., Wei, W., Klaus, M.: Enabling query of frequently updated data from mobile sensing sources. Inst. Commun., pp. 946–952 (2014)
Mervat, A.E., Mohammad, H., Najah, A.A.: Data management for the Internet of Things: design primitives and solution. J. Sens. 13, 15582–15612 (2013)
Efficient storage of multi-sensor object-tracking data: Xingjun, H., Hao, H., Peiquan, J., Lihua. Y. IEEE Trans. Parall. Distrib. Syst. 99, 1–5 (2001)
Lu, T., Fang, J., Cong, L.: A unified storage and query optimization framework for sensor data. In: Web Information System and Application Conference, vol. 13: 229–234 (2015)
Qin, Q., Sheng, Q.Z., Falkner, N.J.K., Dustdar, S., Wang, H., Vasilakos, V.A.: When things matter: a data-centric view of the Internet of Things. CoRR 1, 1–10 (2014)
Kristi, M., Matt, M., Maarit, M., Boya, D.: Data management life cycle. PRC 1, 17–84 (2018)
Vinod, V.N., Nanda Kishor, R.: Getting the most out of IoT with an effective data lifecycle management strategy. White paper
Heinzelman, W.R., Chandrakasan, A.P., Balakrishnan, H.: An application-specific protocol architecture for wireless microsensor networks. IEEE Trans. Wireless Commun. 1, 660–670 (2002)
Ramesh, R., Pramod, K.V.: Data aggregation techniques in sensor networks: a survey. SURFACE 1, 1–30 (2008)
Paruchuri, V., Durresi, A., Dash, D.S., Jain, R.: Optimal flooding protocol for routing in ad-hoc networks. In: IEEE Wireless Communications and Networking Conference, pp. 93–102
Jelasity, M., Babaoglu, O.: T-Man: Gossip-Based Overlay Topology Management. Engineering Self-Organising Systems, pp. 1–15. Springer (2006)
Jelasity, M., Montresor, A., Babaoglu, O.: Gossip-based aggregation in large dynamic networks. ACM Trans. Comput. Syst. 23, 219–252 (2005)
Jelasity, M., Voulgaris, S., Guerraoui, R., Kermarrec, A.-M., Van Steen, M.: Gossip-based peer sampling. ACM Trans. Comput. Syst. (TOCS) 25, 1–8 (2007)
Younis, O., Fahmy, S.: HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans. Mob. Comput. 3, 366–379 (2004)
Chatterjea, S., Havinga, P.: A Dynamic data aggregation scheme for wireless sensor networks. Proc. Progr. Res. Integr. Syst. Circ. 1, 1–12 (2003)
Payal, J., Anu, C.: The comparison between leach protocol and pegasis protocol based on lifetime of wireless sensor networks. Int. J. Comput. Sci. Mob. Comput. 6, 15–19 (2017)
Halvorsen, H.P., Jonsaas, A., Mylvaganam, S., Timmerberg, J., Thiriet, J.C.: Case studies in IoT-smart-home solutions pedagogical perspective with industrial applications and some latest developments. In: The 27th EAEEIE Annual Conference, pp. 1–8 (2017)
Ruben, C.H., Rafael, T., Maristela, T.H., Robson, O.A., Luis, J.G.V., Kim, T.H.: Distributed data service for data management in Internet of Things middleware. J. Sens. 17, 1–25 (2017)
Yasir, M., Farhan, A., Ibrar, Y., Asma, A., Muhammad, I., Sghaier, G.: Internet-of-Things based smart cities: recent advances and challenges. IEEE Commun. Mag. 1, 1–14 (2017)
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Joshva Devadas, T., Thayammal, S., Ramprakash, A. (2020). IoT Data Management, Data Aggregation and Dissemination. In: Peng, SL., Pal, S., Huang, L. (eds) Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm. Intelligent Systems Reference Library, vol 174. Springer, Cham. https://doi.org/10.1007/978-3-030-33596-0_16
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DOI: https://doi.org/10.1007/978-3-030-33596-0_16
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