Data Management in IOT Applications

  • Authors

    • Dr Sanakkayala SatyaNarayana
    • G V. Sai Bharath
    • Katakam Sri Lakshmi Sahithi
    • Adusumilli Sai Rutwik
    2018-05-31
    https://doi.org/10.14419/ijet.v7i2.32.15572
  • IOT, management of data, heterogeneity, SWIFT architecture.

  • With the technology leaping towards a new phase the next big that is happening is IOT and managing the huge amount of data that is being produced. To apprehend the real Internet of Things in which the entirely is interconnected, direct interactions between sensors and actuators, also known as bindings, are essential. As more and more devices are getting connected to the internet there is a lot of data that is being generated. We need to maintain the quality of data and it should be manageable for future use. Consequently, in evaluation to subsisting studies on smart cities we give a information driven edge depicting the central information administration methodologies employed to check consistency, interoperability, granularity and re-convenience of the information created by strategies for the fundamental Internet of Things( IoT) for smart cities. We try to find the proper communication between the devices and finally try to implement the details for a system. In this paper we are trying to do survey on how the large amount of data is being stored and various strategies for handling the data by using some architectures for the smart traffic system. We are trying to use the SWIFT architecture for analyzing the traffic in smart cities.

     

     

  • References

    1. [1] Networking and Information Technology Research and Development(NITRD) Program. (2015).SmartandConnectedCommunitiesFramework.[Online].Available: https://www.nitrd.gov/sccc/materials/scccframework.pdf.

      [2] Y. Sun, Y. Xia, H. Song, and R. Bie, ``Internet of things services for small towns,'' in Proc. Int. Conf. Identi_cat., Inf. Knowl. Internet Things (IIKI), Oct. 2014, pp. 92_95.

      [3] J. Jara, Y. Sun, H. Song, R. Bie, D. Genooud, and Y. Bocchi, ``Internet of Things for cultural heritage of smart cities and smart regions,'' in Proc.IEEE 29th Int. Conf. Adv. Inf. Netw. Appl.Workshops (WAINA), Mar. 2015,pp. 668_675.

      [4] H. Song, R. Srinivasan, T. Sookoor, and S. Jeschke, Smart Cities: Founda- tions and Principles. Hoboken, NJ, USA: Wiley, 2016.

      [5] U.S. Department of Transportation. (2013). Livability 101. [Online].

      [6] MervatAbu-Elkheir,Mohammad Hayajneh,and,Najah Abu Ali: Data Management for the Internet of Things: Design Primitives and Solution Published online 2013 Nov 14.

      [7] A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery ProspectiveNilamadhab Mishra, Chung-Chih Lin, Hsien-Tsung Chang First Published January 1, 2015

      [8] N. D. Milder and A. Dane, ``Revitalizing small towns: Resolving downtown challenges,'' Econ. Develop.J.,2013.[Online]. Available:http://www.ruraldesign.org/resource/revitalizing-small-towns-resolving-downtown-challenges.

      [9] Annex, ``World commission on environment and development,'' Oxford Univ. Press, New York, NY, USA, 1987. Tech. Rep. A/42/427, 1987.

      [10] http://www.softwareadvice.com/resources/iot-data-analytics-use-cases/

      [11] Approaches and Methods for Data Analytics in IoT written by DAVE WAGSTAFF, JANUARY 2016

      [12] Smart cities: A survey on data management, security, enabling technologies written by Ammar Gharaibeh, Member, IEEE, Mohammad A. Salahuddin, Member, IEEE, Sayed J. Hussini, Student Member, IEEE

      [13] Strategies to handle Big Data for traffic management in smart cities written by Satyanarayana V Nandury and Beneyaz A Begum.

      [14] L. M. A. Bettencourt, “The Uses of Big Data in Cities,†Big Data, vol.2, no. 1, pp. 12-22, Mar. 2013.

      [15] C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos, “Context aware computing for The Internet of Things: A Survey,†IEEE Commun. Surveys Tuts., vol. 16, no. 1, pp. 414-454, Jan. 1, 2014.

      [16] G. S. Mann, and A. McCallum, “Generalized expectation criteria for semi-supervised learning with weakly labeled data,†J. of Mach. Learning Res., vol. 11, pp. 955-984, Feb. 2010.

      [17] A. Blum, and T. Mitchell, “Combining labeled and unlabeled data with co-training,†in Proc. 11th Ann. Conf. on Comput.Learning Theory, pp.92–100, 1998.

      [18] K. Nigam, and R. Ghani, “Analyzing the effectiveness and applicability of co-training,†in Proc. of CIKM, 2000.

      [19] Z. H. Zhou, and M. Li, “Tri-training: exploiting unlabeled data using three classifiers,†Trans. Knowl. and Data Eng., vol. 17, no. 11, pp. 1529-1541, 2005.

      [20] B. Schilit, N. Adams, and R. Want, “Context-Aware Computing Applications,†1st Int. Workshop on Mobile Comput. Syst. and Appl.,1994, pp. 85-90. 2016 Intl. Conference on Advances in Computing, Communications and Informatics (ICACCI), Sept. 21-24, 2016, Jaipur, India.

  • Downloads

  • How to Cite

    Sanakkayala SatyaNarayana, D., V. Sai Bharath, G., Sri Lakshmi Sahithi, K., & Sai Rutwik, A. (2018). Data Management in IOT Applications. International Journal of Engineering & Technology, 7(2.32), 224-227. https://doi.org/10.14419/ijet.v7i2.32.15572