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Activity Based Resource Allocation in IoT for Disaster Management

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Future Internet Technologies and Trends (ICFITT 2017)

Abstract

Efficient utilization of resources during disasters is a major and non-trivial problem. Improper resource allocations is due to lacking in knowledge of activity priorities. Due to disaster, in a major instances, communication networks are ruined. In this regard, Internet of Things (IoT) helps to a great extent in establishment of dynamic network for communication. Further, priority based stable matching algorithm is used for allocation of resources for the corresponding activities. This approach determines for maximum utilization of resources with complete accomplishment of activities efficiently. Also, we evaluated our approach with execution time and fairness of resource allocation for utility.

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References

  1. Wahlstrom, M., Guha-Sapir, D.: The Human Cost of Weather-Related Disasters 1995–2015. UNISDR, Geneva (2015). https://www.unisdr.org/2015/climatechange/COP21_WeatherDisastersReport_2015_FINAL.pdf

  2. Data Collection Survey for Disaster Prevention in India, Japan, October 2015. https://open_jicareport.jica.go.jp/pdf/12245155.pdf

    Google Scholar 

  3. Lee, G.M., Crespi, N., Choi, J.K., Boussard, M.: Internet of Things. In: Bertin, E., Crespi, N., Magedanz, T. (eds.) Evolution of Telecommunication Services. LNCS, vol. 7768, pp. 257–282. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41569-2_13

    Chapter  Google Scholar 

  4. Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. J. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)

    Article  Google Scholar 

  5. Muaafa, M., Concho, A.L., Ramirez-Marquez, J.: Emergency resource allocation for disaster response: an evolutionary approach (2014)

    Google Scholar 

  6. Yang, L., Yang, S.-H., Plotnick, L.: How the Internet of Things technology enhances emergency response operations. Technol. Forecast. Soc. Change 80(9), 1854–1867 (2013)

    Article  Google Scholar 

  7. Kondaveti, R., Ganz, A.: Decision support system for resource allocation in disaster management. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2009), pp. 3425–3428. IEEE (2009)

    Google Scholar 

  8. Kumar, J.S., Zaveri, M.A.: Clustering for collaborative processing in IoT network. In: Proceedings of the Second International Conference on IoT in Urban Space, pp. 95–97. ACM (2016)

    Google Scholar 

  9. Pearce, L.: Disaster management and community planning, and public participation: how to achieve sustainable hazard mitigation. Nat. Hazards 28, 211–228 (2003)

    Article  Google Scholar 

  10. Kumar, J.S., Zaveri, M.A.: Hierarchical clustering for dynamic and heterogeneous Internet of Things. Procedia Comput. Sci. 93, 276–282 (2016)

    Article  Google Scholar 

  11. Pandey, S.K., Zaveri, M.A: Localization for collaborative processing in the Internet of Things framework. In: Proceedings of the Second International Conference on IoT in Urban Space, pp. 108–110. ACM (2016)

    Google Scholar 

  12. Kominers, S.D., Sönmez, T.: Matching with slot-specific priorities: theory. Theor. Econ. 11(2), 683–710 (2016)

    Article  MathSciNet  Google Scholar 

  13. Manne, F., Naim, M., Halappanavar, M.: On stable marriages and greedy matchings. In: Proceedings of the SIAM Workshop on Combinatorial Scientific Computing, pp. 1–8. ACM (2016)

    Google Scholar 

  14. Jain, R., Chiu, D., Hawe, W.: A quantitative measure of fairness and discrimination for resource allocation in shared systems, digital equipment corporation, Technical report DEC-TR-301, vol. 38 (1984)

    Google Scholar 

  15. Arora, H., Raghu, T.S., Vinze, A.: Resource allocation for demand surge mitigation during disaster response. Decis. Support Syst. 50, 304–315 (2010)

    Article  Google Scholar 

  16. Svennerberg, G.: Beginning Google Maps API 3. Apress, New York (2010)

    Book  Google Scholar 

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Acknowledgments

This work is supported by the Department of Electronics and Information Technology (DeiTY), funded by Ministry of Human Resource Development (MHRD), Government of India (Grant No. 13(4)/2016-CC&BT).

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Correspondence to J. Sathish Kumar .

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Kumar, J.S., Zaveri, M.A., Choksi, M. (2018). Activity Based Resource Allocation in IoT for Disaster Management. In: Patel, Z., Gupta, S. (eds) Future Internet Technologies and Trends. ICFITT 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 220. Springer, Cham. https://doi.org/10.1007/978-3-319-73712-6_22

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  • DOI: https://doi.org/10.1007/978-3-319-73712-6_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73711-9

  • Online ISBN: 978-3-319-73712-6

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