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Broadcasting and Sharing of Parameters in an IoT Network by Means of a Fractal of Hilbert Using Swarm Intelligence

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Abstract

Nowadays, thousand and thousand of small devices, such as Microcontroller Units (MCU’s), live around us. These MCU’not only interact with us turning on lights or identifying movement in a House but also they perform small and specific tasks such as sensing different parameters such as temperature, humidity, \(CO_2\), adjustment of the environmental lights. In addition there is a huge kind of these MCU’s like SmartPhones or small general purpose devices, ESP8266 or RaspberryPi3 or any kind of Internet of Things (IoT) devices. They are connected to internet to a central node and then they can share their information. The main goal of this article is to connect all the nodes in a fractal way without using a central one, just sharing some parameters with two adjacent nodes, but any member of these nodes knows the parameters of the rest of these devices even if they are not adjacent nodes. With a Hilbert fractal network we can access to the entire network in real time in a dynamic way since we can adapt and reconfigure the topology of the network when a new node is added using tools of Artificial Intelligence for its application in a Smart City.

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Acknowledgment

This article is supported by National Polytechnic Institute (Instituto Poliécnico Nacional) of Mexico by means of Project No. 20180514 granted by Secretariat of Graduate and Research, National Council of Science and Technology of Mexico (CONACyT). The research described in this work was carried out at the Superior School of Mechanical and Electrical Engineering (Escuela Superior de Ingeniería Mecánica y Eléctrica), Campus Zacatenco.

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Correspondence to Jaime Moreno .

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Moreno, J., Morales, O., Tejeida, R., Posadas, J. (2018). Broadcasting and Sharing of Parameters in an IoT Network by Means of a Fractal of Hilbert Using Swarm Intelligence. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Soft Computing. MICAI 2018. Lecture Notes in Computer Science(), vol 11288. Springer, Cham. https://doi.org/10.1007/978-3-030-04491-6_5

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  • DOI: https://doi.org/10.1007/978-3-030-04491-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04490-9

  • Online ISBN: 978-3-030-04491-6

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