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Navigation in the social internet-of-things (SIoT) for discovering the influential service-providers using distributed learning automata

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Abstract

The integration of the social network concepts and IoT has led to the creation of a new concept called Social IoT, wherein objects or things can interact and provide services to one another. Efficient and distributed service navigation in such systems is essential due to the potentially huge number of services that are provided. This paper proposes a Distributed Learning Automata-based algorithm called DLA_N, which aims at finding service-providers (i.e., objects) with high popularity or influence. The main idea is that an object can use its friends or friends of its friends to search for the desired service provider. Taking into account the SIoT’s graph properties (i.e., the topology of the network), we define a new centrality metric that indicates the importance degree of a node in SIoT. Embedding a Learning Automata in each object, a distributed LA approach is proposed for the selection of the most influential nodes in the network. Starting from a requesting object, the proposed DLA_N algorithm learns to select a path containing objects with a high centrality metric. The distributed nature of our navigation results in high scalability and low navigation time. The results of performance evaluation indicate that the proposed method outperforms existing methods in the literature.

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Notes

  1. Distributed Learning Automata-based Navigation.

  2. https://snap.stanford.edu/data.

References

  1. Atzori L, Iera A, Morabito G (2011) SIoT: giving a social structure to the internet of things. IEEE Commun Lett 15(11):1193–1195

    Article  Google Scholar 

  2. Atzori L, Iera A, Morabito G, Nitti M (2012) The social internet of things (SIoT)—When social networks meet the Internet of Things: Concept, architecture and network characterization. Comput Netw 56(16):3594–3608

    Article  Google Scholar 

  3. Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805

    Article  Google Scholar 

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

    Google Scholar 

  5. Xia H, Cq Hu, Xiao F, Xg C, Zk P (2019) An efficient social-like semantic-aware service discovery mechanism for large-scale internet of things. Comput Netw 152:210–220

    Article  Google Scholar 

  6. Zhang D, Yang LT, Huang H (2011) Searching in internet of things: Vision and challenges. In: IEEE ninth international symposium on parallel and distributed processing with applications, pp 201–206

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

  8. Yap KK, Srinivasan V, Motani M (2005) Max: human-centric search of the physical world. In: SenSy JR, Balakrishnan H, Zhao F (eds) ACM, pp 166–179

  9. Mendes P (2011) Social-driven internet of connected objects. In: Proc. of the Interconn. Smart Objects with the Internet Workshop

  10. Travers J, Milgram S (1969) An experimental study of the small world problem. Sociometry 32:425–443

    Article  Google Scholar 

  11. Kleinberg J (2000) The small-world phenomenon: an algorithmic perspective. In: Proceedings of the thirty- second annual ACM symposium on Theory of computing. ACM, pp 163–170

  12. Girau R, Nitti M, Atzori L (2013) Implementation of an experimental platform for the social internet of things. In: Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS) Seventh International Conference on IEEE, pp 500–505

  13. Militano L, Nitti M, Atzori L, Iera A (2015) Using a distributed shapley value based approach to ensure navigability in a social network of smart objects. In: IEEE International Conference on Communications (ICC), London, pp 692–697

  14. ] Nitti M, Atzori L, Cvijikj IP (2014) Network navigability in the social internet of things. In: Proceedings IEEE World Forum Internet Things (WFIoT), pp 405–410

  15. Naruchitparames J, Gunes MH, Louis SJ (2011) Friend recommendations in social networks using genetic algorithms and network topology. In: Proceedings of the 2011 IEEE Congress on Evolutionary Computation (CEC) IEEE, pp 2207–2214

  16. Ramasamy T, Arjunasamy A (2017) Advanced heuristics for selecting friends in social internet of things. Wirel Pers Commun 97:4951–4965

    Article  Google Scholar 

  17. Mardini W, Khamayseh Y, Yassein MB, Khatatbeh MH (2018) Mining Internet of Things for intelligent objects using genetic algorithm. Comput Electr Eng 66:423–434

    Article  Google Scholar 

  18. Barreiro-Gomez J, Tembine H (2018) Distributed evolutionary games reaching power indexes: navigability in a social network of smart objects. In: 2018 European Control Conference (ECC), Limassol, pp 1062–1067

  19. Yang Z, Chen W (2018) A game theoretic model for the formation of navigable small-world networks the tradeoff between distance and reciprocity. ACM Trans Internet Technol 18:1–38

    Google Scholar 

  20. Kumar N, Chilamkurti N, Misra SC (2015) Bayesian Coalition game for the internet of things: an ambient intelligence-based evaluation. IEEE Commun Mag 53(1):48–55

    Article  Google Scholar 

  21. Malkov YA, Yashunin DA (2016) Efficient and robust approximate nearest neighbor search using hierarchical navigable small-world graphs. IEEE Trans Pattern Anal Mach Intell 42(4):824–836

    Article  Google Scholar 

  22. Perera C, Zaslavsky A, Christen P, Compton M, Georgakopoulos D (2013) Context-Aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware. In: 2013 IEEE 14th International Conference on Mobile Data Management, Milan, pp 314–322

  23. Das K, Samanta S, Pal M (2018) Study on centrality measures in social networks: a survey. Soc Netw Anal Min 2018:1–11

    Google Scholar 

  24. Grando F, Noble D, Lamb LC (2016) An analysis of centrality measures for complex and social networks. Proc. of IEEE global communications conference, p 1–6

  25. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442

    Article  Google Scholar 

  26. Barrat A, Weigt M (2000) on the properties of small-world network models. Eur Phys J B Condensed Matter Complex Syst 13:547–560

    Article  Google Scholar 

  27. Beigy H, Meybodi MR (2006) Utilizing distributed learning automata to solve stochastic shortest path problems. Int J Uncertain Fuzziness Knowl-Based Syst 14(5):591–615

    Article  MathSciNet  Google Scholar 

  28. Kitsak M, Gallos LK, Havlin S, Liljeros F, Muchnik L, Stanley HE, Makse HA (2010) Identification of influential spreaders in complex networks Nat. Phys 6(11):888–893

    Google Scholar 

  29. Kermack WO, McKendrick AG (1932) Contributions to the mathematical theory of epidemics-I. Bull Math Biol 53(1–2):33–55

    MATH  Google Scholar 

  30. Gao S, Ma J, Chen Z, Wang G, Xing C (2014) Ranking the spreading ability of nodes in complex networks based on local structure. Phys A 403:130–147

    Article  Google Scholar 

  31. Sarma AD, Nanongkai D, Pandurangan G, Tetali P (2013) Distributed random walk. J ACM 60(1):2

    MathSciNet  MATH  Google Scholar 

  32. Rehman B, Paul A, Ahmad A (2020) A query based information search in an individual’s small world of social internet of things. Comput Commun 163:176–185

    Article  Google Scholar 

  33. Amin F, Abbasi R, Rehman A, Choi GS (2019) An advanced algorithm for higher network navigation in social internet of things using small-world networks. Sensors 19(9):2007

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Authors and Affiliations

  1. Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Javad Pashaei Barbin & Behrooz Masoumi

  2. Department of Computer Engineering, Urmia University, 57561-51818, Urmia, Iran

    Saleh Yousefi

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  1. Javad Pashaei Barbin
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  2. Saleh Yousefi
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  3. Behrooz Masoumi
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Correspondence to Saleh Yousefi.

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Pashaei Barbin, J., Yousefi, S. & Masoumi, B. Navigation in the social internet-of-things (SIoT) for discovering the influential service-providers using distributed learning automata. J Supercomput 77, 11004–11031 (2021). https://doi.org/10.1007/s11227-021-03699-3

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