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Selection of IoT Devices in Opportunistic Networks: A Fuzzy-Based Approach Considering IoT Device’s Selfish Behaviour

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Abstract

In opportunistic networks the communication opportunities (contacts) are intermittent and there is no need to establish an end-to-end link between the communication nodes. The enormous growth of devices having access to the Internet, along the vast evolution of the Internet and the connectivity of objects and devices, has evolved as Internet of Things (IoT). There are different issues for these networks. One of them is the selection of IoT devices in order to carry out a task in opportunistic networks. In this work, we implement a Fuzzy-Based System for IoT device selection in opportunistic networks. For our system, we use four input parameters: IoT Device’s Selfish Behaviour (IDSB), IoT Device Remaining Energy (IDRE), IoT Device Storage (IDST) and IoT Device Contact Duration (IDCD). The output parameter is IoT Device Selection Decision (IDSD). The simulation results show that the proposed system makes a proper selection decision of IoT devices in opportunistic networks. The IoT device selection is increased up to 14% and decreased 23% by increasing IDRE and IDSB, respectively.

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References

  1. Mantas, N., Louta, M., Karapistoli, E., Karetsos, G.T., Kraounakis, S., Obaidat, M.S.: Towards an incentive-compatible, reputation-based framework for stimulating cooperation in opportunistic networks: a survey. IET Netw. 6(6), 169–178 (2017)

    Google Scholar 

  2. Sharma, D.K., Sharma, A., Kumar, J., et al.: KNNR: K-nearest neighbour classification based routing protocol for opportunistic networks. In: 10th International Conference on Contemporary Computing (IC3), pp. 1–6. IEEE (2017)

    Google Scholar 

  3. Kraijak, S., Tuwanut, P.: A survey on internet of things architecture, protocols, possible applications, security, privacy, real-world implementation and future trends. In: 16th International Conference on Communication Technology (ICCT), pp. 26–31. IEEE (2015)

    Google Scholar 

  4. Arridha, R., Sukaridhoto, S., Pramadihanto, D., Funabiki, N.: Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system. Int. J. Space-Based Situated Comput. 7(2), 82–93 (2017)

    Google Scholar 

  5. Dhurandher, S.K., Sharma, D.K., Woungang, I., Bhati, S.: HBPR: history based prediction for routing in infrastructure-less opportunistic networks. In: 27th International Conference on Advanced Information Networking and Applications (AINA), pp. 931–936. IEEE (2013)

    Google Scholar 

  6. Spaho, E., Mino, G., Barolli, L., Xhafa, F.: Goodput and PDR analysis of AODV, OLSR and DYMO protocols for vehicular networks using CAVENET. Int. J. Grid Utility Comput. 2(2), 130–138 (2011)

    Google Scholar 

  7. Abdulla, M., Simon, R.: The impact of intercontact time within opportunistic networks: protocol implications and mobility models. TechRepublic White Paper (2009)

    Google Scholar 

  8. Karakostas, G., Markou, E.: Emergency connectivity in ad-hoc networks with selfish nodes. In: Latin American Symposium on Theoretical Informatics, pp. 350–361. Springer (2008)

    Google Scholar 

  9. Popereshnyak, S., Suprun, O., Suprun, O., Wieckowski, T.: IoT application testing features based on the modelling network. In: The 14th International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), pp. 127–131. IEEE (2018)

    Google Scholar 

  10. Chen, N., Yang, Y., Li, J., Zhang, T.: A fog-based service enablement architecture for cross-domain IoT applications. In: 2017 IEEE Fog World Congress (FWC), pp. 1–6. IEEE (2017)

    Google Scholar 

  11. Pozza, R., Nati, M., Georgoulas, S., Moessner, K., Gluhak, A.: Neighbor discovery for opportunistic networking in internet of things scenarios: a survey. IEEE Access 3, 1101–1131 (2015)

    Google Scholar 

  12. Akbas, M., Turgut, D.: APAWSAN: actor positioning for aerial wireless sensor and actor networks. In: IEEE 36th Conference on Local Computer Networks (LCN-2011), pp. 563–570, October 2011

    Google Scholar 

  13. Akbas, M., Brust, M., Turgut, D.: Local positioning for environmental monitoring in wireless sensor and actor networks. In: IEEE 35th Conference on Local Computer Networks (LCN 2010), pp. 806–813, October 2010

    Google Scholar 

  14. Melodia, T., Pompili, D., Gungor, V., Akyildiz, I.: Communication and coordination in wireless sensor and actor networks. IEEE Trans. Mob. Comput. 6(10), 1126–1129 (2007)

    Google Scholar 

  15. Inaba, T., Sakamoto, S., Kolici, V., Mino, G., Barolli, L.: A CAC scheme based on fuzzy logic for cellular networks considering security and priority parameters. In: The 9th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA 2014), pp. 340–346 (2014)

    Google Scholar 

  16. Spaho, E., Sakamoto, S., Barolli, L., Xhafa, F., Barolli, V., Iwashige, J.: A fuzzy-based system for peer reliability in JXTA-overlay P2P considering number of interactions. In: The 16th International Conference on Network-Based Information Systems (NBiS 2013), pp. 156–161 (2013)

    Google Scholar 

  17. Matsuo, K., Elmazi, D., Liu, Y., Sakamoto, S., Mino, G., Barolli, L.: FACS-MP: a fuzzy admission control system with many priorities for wireless cellular networks and its performance evaluation. J. High Speed Netw. 21(1), 1–14 (2015)

    Google Scholar 

  18. Grabisch, M.: The application of fuzzy integrals in multicriteria decision making. Eur. J. Oper. Res. 89(3), 445–456 (1996)

    MathSciNet  MATH  Google Scholar 

  19. Inaba, T., Elmazi, D., Liu, Y., Sakamoto, S., Barolli, L., Uchida, K.: Integrating wireless cellular and ad-hoc networks using fuzzy logic considering node mobility and security. In: The 29th IEEE International Conference on Advanced Information Networking and Applications Workshops (WAINA 2015), pp. 54–60 (2015)

    Google Scholar 

  20. Kulla, E., Mino, G., Sakamoto, S., Ikeda, M., Caballé, S., Barolli, L.: FBMIS: a fuzzy-based multi-interface system for cellular and ad hoc networks. In: International Conference on Advanced Information Networking and Applications (AINA 2014), pp. 180–185 (2014)

    Google Scholar 

  21. Elmazi, D., Kulla, E., Oda, T., Spaho, E., Sakamoto, S., Barolli, L.: A comparison study of two fuzzy-based systems for selection of actor node in wireless sensor actor networks. J. Ambient Intell. Hum. Comput. 6(5), 635–645 (2015)

    Google Scholar 

  22. Zadeh, L.: Fuzzy logic, neural networks, and soft computing. ACM Commun. 37(3), 77–85 (1994)

    Google Scholar 

  23. Spaho, E., Sakamoto, S., Barolli, L., Xhafa, F., Ikeda, M.: Trustworthiness in P2P: performance behaviour of two fuzzy-based systems for JXTA-overlay platform. Soft Comput. 18(9), 1783–1793 (2014)

    Google Scholar 

  24. Inaba, T., Sakamoto, S., Kulla, E., Caballe, S., Ikeda, M., Barolli, L.: An integrated system for wireless cellular and ad-hoc networks using fuzzy logic. In: International Conference on Intelligent Networking and Collaborative Systems (INCoS 2014), pp. 157–162 (2014)

    Google Scholar 

  25. Matsuo, K., Elmazi, D., Liu, Y., Sakamoto, S., Barolli, L.: A multi-modal simulation system for wireless sensor networks: a comparison study considering stationary and mobile sink and event. J. Ambient Intell. Hum. Comput. 6(4), 519–529 (2015)

    Google Scholar 

  26. Kolici, V., Inaba, T., Lala, A., Mino, G., Sakamoto, S., Barolli, L.: A fuzzy-based CAC scheme for cellular networks considering security. In: International Conference on Network-Based Information Systems (NBiS 2014), pp. 368–373 (2014)

    Google Scholar 

  27. Liu, Y., Sakamoto, S., Matsuo, K., Ikeda, M., Barolli, L., Xhafa, F.: A comparison study for two fuzzy-based systems: improving reliability and security of JXTA-overlay P2P platform. Soft Comput. 20(7), 2677–2687 (2015)

    Google Scholar 

  28. Matsuo, K., Elmazi, D., Liu, Y., Sakamoto, S., Mino, G., Barolli, L.: FACS-MP: a fuzzy admission control system with many priorities for wireless cellular networks and its perforemance evaluation. J. High Speed Netw. 21(1), 1–14 (2015)

    Google Scholar 

  29. Mendel, J.M.: Fuzzy logic systems for engineering: a tutorial. Proc. IEEE 83(3), 345–377 (1995)

    Google Scholar 

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Correspondence to Miralda Cuka .

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Cuka, M., Elmazi, D., Ikeda, M., Matsuo, K., Barolli, L., Takizawa, M. (2020). Selection of IoT Devices in Opportunistic Networks: A Fuzzy-Based Approach Considering IoT Device’s Selfish Behaviour. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2019. Advances in Intelligent Systems and Computing, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-15032-7_22

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