Abstract
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 and placement of IoT nodes. In this work, we propose a simulating system based on Fuzzy Logic and Genetic Algorithm for IoT node selection and placement. We consider three input parameters for our Fuzzy-based selection system: IoT Node Density (IND), IoT Node’s Remaining Energy (INRE) and IoT Node’s Distance to Event (INDE). We also present a simulation system based on Genetic Algorithm which is implemented in Rust, for IoT node placement. We consider different aspects of an IoT network, considering coordination, connectivity and coverage. We describe the implementation and show the interface of simulation system. We evaluated the performance of the proposed system by a simulation scenario. For the IoT node fuzzy-based selection system, we show that the system makes a proper selection of IoT nodes. The simulation results of GA-based system show that the constructed network, can cover both events.
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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)
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)
Braulio, L.D.C., Moreno, E.D., de Macedo, D.D.J., Kreutz, D., Dantas, M.A.R.: Towards a hybrid storage architecture for IoT. In: 2018 IEEE Symposium on Computers and Communications (ISCC), pp. 00470–00473, June 2018
Lu, D., Bang, W.: Sensor placement based on an improved genetic algorithm for connected confident information coverage in an area with obstacles. In: 2017 IEEE 42nd Conference on Local Computer Networks (LCN), pp. 595–598. IEEE (2018)
Holland, J.H., et al.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)
Popereshnyak, S., Suprun, O., Suprun, O., Wieckowski, T.: IoT application testing features based on the modelling network. In: 2018 XIV-th International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), pp. 127–131 (2018)
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)
Zhao, Z., Min, G., Gao, W., Wu, Y., Duan, H., Ni, Q.: Deploying edge computing nodes for large-scale IoT: a diversity aware approach. IEEE Internet Things J. 5(5), 3606–3614 (2018)
Alagha, A., Singh, S., Mizouni, R., Ouali, A., Otrok, H.: Data-driven dynamic active node selection for event localization in IoT applications - a case study of radiation localization. IEEE Access 7, 16168–16183 (2019)
Oda, T., Barolli, A., Xhafa, F., Barolli, L., Ikeda, M., Takizawa, M.: WMN-GA: a simulation system for wmns and its evaluation considering selection operators. J. Ambient Intell. Human. Comput. 4(3), 323–330 (2013)
Xhafa, F., Sánchez, C., Barolli, L.: Genetic algorithms for efficient placement of router nodes in wireless mesh networks. In: 2010 24th IEEE International Conference on Advanced Information Networking and Applications, pp. 465–472. IEEE (2010)
Aiello, G., Certa, A., Enea, M.: A fuzzy inference expert system to support the decision of deploying a military naval unit to a mission. In: International Workshop on Fuzzy Logic and Applications, pp. 320–327. Springer, Heidelberg (2009)
Mendel, J.M.: Fuzzy logic systems for engineering: a tutorial. Proc. IEEE 83(3), 345–377 (1995)
Bhondekar, A.P., Vig, R., Singla, M.L., Ghanshyam, C., Kapur, P.: Genetic algorithm based node placement methodology for wireless sensor networks. In: Proceedings of the International Multiconference of Engineers and Computer Scientists, vol. 1, pp. 18–20 (2009)
Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)
Sastry, K., Goldberg, D., Kendall, G.: Genetic algorithms. In: Search Methodologies, pp. 97–125. Springer, Heidelberg (2005)
Xhafa, F., Sánchez, C., Barolli, L., Spaho, E.: Evaluation of genetic algorithms for mesh router nodes placement in wireless mesh networks. J. Ambient Intell. Human. Comput. 1(4), 271–282 (2010). Springer
Barolli, A., Sakamoto, S., Oda, T., Spaho, E., Barolli, L., Xhafa, F.: Performance evaluation of WMN-GA system for different settings of population size and number of generations. Hum.-Centric Comput. Inf. Sci. 4(1), 5–19 (2013)
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Cuka, M., Elmazi, D., Ikeda, M., Matsuo, K., Barolli, L. (2020). IoT Node Selection and Placement: A New Approach Based on Fuzzy Logic and Genetic Algorithm. In: Barolli, L., Hussain, F., Ikeda, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2019. Advances in Intelligent Systems and Computing, vol 993. Springer, Cham. https://doi.org/10.1007/978-3-030-22354-0_3
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DOI: https://doi.org/10.1007/978-3-030-22354-0_3
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