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Modified Genetic Algorithm for Resource Selection on Internet of Things

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Futuristic Trends in Networks and Computing Technologies (FTNCT 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1206))

Abstract

With the epidemic progression in resources on IoT, discovery emerges as an eminent challenge due to requirement of their self-automation. The traditional resource discovery approaches do not provide efficient methodologies due to continuously changing IoT search metrics such as syntax, access, architecture, etc. To address the gap, the paper proposes an optimized technique, namely, Modified Genetic Algorithm for Resource Selection (MGA-RS) that intends to discover optimum data (resources) is short period of time by considering the bit strings of chromosomes. It is evaluated on datasets of Ionosphere from machine learning repository of university college, London. The best and mean fitness are selected in a way that they should be close to each other at the time when MGA-RS reaches termination condition and to minimize classification error from kNN. It is found that MGA-RS outperforms well with kNN based fitness function and is approximately 14% and 15% better than simple and rastrigin fitnesses, respectively, for selecting the optimal resources in IoT.

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References

  1. Akbari, R., Ziarati, K.: A multilevel evolutionary algorithm for optimizing numerical functions. Int. J. Ind. Eng. Comput. 2(2), 419–430 (2011)

    Google Scholar 

  2. Baskan, O., Haldenbilen, S., Ceylan, H., Ceylan, H.: A new solution algorithm for improving performance of ant colony optimization. Appl. Math. Comput. 211(1), 75–84 (2009)

    MathSciNet  MATH  Google Scholar 

  3. Bruzzone, L., Persello, C.: A novel approach to the selection of robust and invariant features for classification of hyperspectral images. In: 2008 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008, vol. 1, pp. 1–66. IEEE (2008)

    Google Scholar 

  4. Datta, S.K., Bonnet, C.: Search engine based resource discovery framework for internet of things. In: 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE), pp. 83–85. IEEE (2015)

    Google Scholar 

  5. Datta, S.K., Bonnet, C.: Describing things in the internet of things: from core link format to semantic based descriptions. In: 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), pp. 1–2. IEEE (2016)

    Google Scholar 

  6. Fogel, D.B.: Evolutionary Computation: The Fossil Record. Wiley-IEEE Press, New York (1998)

    Book  MATH  Google Scholar 

  7. Geetha, S.: Social internet of things. World Sci. News 41, 76 (2016)

    Google Scholar 

  8. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)

    Article  Google Scholar 

  9. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)

    Book  Google Scholar 

  10. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  11. Naruchitparames, J., Güneş, M.H., Louis, S.J.: Friend recommendations in social networks using genetic algorithms and network topology. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 2207–2214. IEEE (2011)

    Google Scholar 

  12. Nitti, M., Atzori, L., Cvijikj, I.P.: Network navigability in the social internet of things. In: 2014 IEEE World Forum on Internet of Things (WF-IoT), pp. 405–410. IEEE (2014)

    Google Scholar 

  13. Ostermaier, B., Römer, K., Mattern, F., Fahrmair, M., Kellerer, W.: A real-time search engine for the web of things. In: 2010 Internet of Things (IOT), pp. 1–8. IEEE (2010)

    Google Scholar 

  14. Robertson, D.I.: ‘Tansyt’ method for area traffic control. Traffic Eng. Control 8(8) (1969)

    Google Scholar 

  15. Srinivas, M., Patnaik, L.M.: Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans. Syst. Man Cybern. Cybern. 24(4), 656–667 (1994)

    Article  Google Scholar 

  16. Sundmaeker, H., Guillemin, P., Friess, P., Woelfflé, S.: Vision and challenges for realising the internet of things. Clust. Eur. Res. Proj. Internet Things Eur. Comm. 3(3), 34–36 (2010)

    Google Scholar 

  17. Taherdangkoo, M., Paziresh, M., Yazdi, M., Bagheri, M.: An efficient algorithm for function optimization: modified stem cells algorithm. Open Eng. 3(1), 36–50 (2013)

    Article  Google Scholar 

  18. Tian, J., Hu, Q., Ma, X., Han, M.: An improved KPCA/GA-SVM classification model for plant leaf disease recognition. J. Comput. Inf. Syst. 8(18), 7737–7745 (2012)

    Google Scholar 

  19. Vandana, C., Chikkamannur, A.A.: Study of resource discovery trends in Internet of Things (IoT). Int. J. Adv. Network. Appl. 8(3), 3084 (2016)

    Google Scholar 

  20. Wallace, C.E., Courage, K., Reaves, D., Schoene, G., Euler, G.: Transyt-7f user’s manual. Technical report (1984)

    Google Scholar 

  21. Wang, H., Tan, C.C., Li, Q.: Snoogle: a search engine for pervasive environments. IEEE Trans. Parallel Distrib. Syst. 21(8), 1188–1202 (2010)

    Article  Google Scholar 

  22. Webster, F.: Traffic signal settings, road research technical paper no. 39. Road Research Laboratory (1958)

    Google Scholar 

  23. Yap, K.K., Srinivasan, V., Motani, M..: Max: human-centric search of the physical world. In: Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems, pp. 166–179. ACM (2005)

    Google Scholar 

  24. Zaslavsky, A., Jayaraman, P.P.: Discovery in the internet of things: the internet of things (ubiquity symposium). Ubiquity 2015, 1–10 (2015). 2

    Article  Google Scholar 

  25. Zhang, J., Chung, H.S.H., Lo, W.L.: Clustering-based adaptive crossover and mutation probabilities for genetic algorithms. IEEE Trans. Evol. Comput. 11(3), 326–335 (2007)

    Article  Google Scholar 

  26. Roopa, M.S., Pattar, S., Buyya, R., Venugopal, K.R., Iyengar, S.S., Patnaik, L.M.: Social Internet of Things (SIoT): foundations, thrust areas, systematic review and future directions. Comput. Commun. (2019)

    Google Scholar 

  27. Song, Z., Sun, Y., Wan, J., Huang, L., Xu, Y., Hsu, C.H.: Exploring robustness management of social internet of things for customization manufacturing. Future Gener. Comput. Syst. 92, 846–856 (2019)

    Article  Google Scholar 

  28. Rho, S., Chen, Y.: Social Internet of Things: applications, architectures and protocols (2019)

    Google Scholar 

  29. Han, G., Zhou, L., Wang, H., Zhang, W., Chan, S.: A source location protection protocol based on dynamic routing in WSNs for the Social Internet of Things. Future Gener. Comput. Syst. 82, 689–697 (2018)

    Article  Google Scholar 

  30. Meena Kowshalya, A., Valarmathi, M.L.: Dynamic trust management for secure communications in Social Internet of Things (SIoT). Sadhana 43(9), 1–8 (2018). https://doi.org/10.1007/s12046-018-0885-z

    Article  MathSciNet  MATH  Google Scholar 

  31. Lin, K., Li, C., Fortino, G., Rodrigues, J.J.: Vehicle route selection based on game evolution in social internet of vehicles. IEEE Internet Things J. 5(4), 2423–2430 (2018)

    Article  Google Scholar 

  32. Ning, Z., Wang, X., Kong, X., Hou, W.: A social-aware group formation framework for information diffusion in narrowband Internet of Things. IEEE Internet Things J. 5(3), 1527–1538 (2017)

    Article  Google Scholar 

  33. Chen, Z., Ling, R., Huang, C.M., Zhu, X.: A scheme of access service recommendation for the Social Internet of Things. Int. J. Commun. Syst. 29(4), 694–706 (2016)

    Article  Google Scholar 

  34. Nitti, M., Murroni, M., Fadda, M., Atzori, L.: Exploiting social internet of things features in cognitive radio. IEEE Access 4, 9204–9212 (2016)

    Article  Google Scholar 

  35. Chen, G., Huang, J., Cheng, B., Chen, J.: A social network based approach for IoT device management and service composition. In: IEEE World Congress on Services, pp. 1–8 (2015)

    Google Scholar 

  36. Li, Z., Chen, R., Liu, L., Min, G.: Dynamic resource discovery based on preference and movement pattern similarity for large-scale social Internet of Things. IEEE Internet Things J. 3(4), 581–589 (2015)

    Article  Google Scholar 

  37. Atzori, L., Iera, A., Morabito, G., Nitti, M.: The Social Internet of Things (SIoT)-when social networks meet the Internet of Things: concept, architecture and network characterization. Comput. Network. 56(16), 3594–3608 (2012)

    Article  Google Scholar 

  38. Chen, R., Bao, F., Guo, J.: Trust-based service management for social internet of things systems. IEEE Trans. Dependable Secure Comput. 13(6), 684–696 (2015)

    Article  Google Scholar 

  39. Sigillito, V.G., Wing, S.P., Hutton, L.V., Baker, K.B.: Classification of radar returns from the ionosphere using neural networks. Johns Hopkins APL Tech. Digest 10(3), 262–266 (1989)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science Engineering/Information Technology, Jaypee University of Information Technology, Waknaghat Solan, 173234, Himachal Pradesh, India

    Monika Bharti & Himanshu Jindal

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  1. Monika Bharti
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  2. Himanshu Jindal
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Correspondence to Monika Bharti .

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Editors and Affiliations

  1. Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    Pradeep Kumar Singh

  2. CDAC, Mohali, India

    Sanjay Sood

  3. Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Yugal Kumar

  4. Polish Academy of Sciences, Warsaw, Poland

    Marcin Paprzycki

  5. Southern Federal University, Rostov-on-Don, Russia

    Anton Pljonkin

  6. Jiangsu Normal University, Xuzhou, China

    Wei-Chiang Hong

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Bharti, M., Jindal, H. (2020). Modified Genetic Algorithm for Resource Selection on Internet of Things. In: Singh, P., Sood, S., Kumar, Y., Paprzycki, M., Pljonkin, A., Hong, WC. (eds) Futuristic Trends in Networks and Computing Technologies. FTNCT 2019. Communications in Computer and Information Science, vol 1206. Springer, Singapore. https://doi.org/10.1007/978-981-15-4451-4_14

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  • DOI: https://doi.org/10.1007/978-981-15-4451-4_14

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  • Print ISBN: 978-981-15-4450-7

  • Online ISBN: 978-981-15-4451-4

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