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

Moreno, Jaime; Matamoros, Oswaldo Morales; Tejeida, Ricardo; Posadas, Juan

doi:10.1007/978-3-030-04491-6_5

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…With the proposed algorithm, it is possible to have access to the entire IoT network in a dynamic way, allowing to reconfigure the topology of the IoT network when a new node is added. This work has its background from a paper presented by Moreno et al 12 in the volume 11288 of the Lecture Notes in artificial intelligence .…”

Section: Related Workmentioning

confidence: 99%

Generation of broadcasting for fractal adaptive Internet of things reconfiguration under the swarm intelligence paradigm

Moreno

Matamoros

Tejeida

et al. 2020

International Journal of Distributed Sensor Networks

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Recently, a wide range of small devices, such as Wi-Fi Internet of things development boards, which are a kind of the microcontroller units in a general purpose board, are interrelated throughout the planet. In addition, certain microcontroller units interact inside our homes when turning lights on or detecting movements, measuring various parameters, such as gas concentrations, [Formula: see text], humidity, and the temperature inside a room, or adjusting the intensity of the lights inside and outside of the house. Likewise, there is a great diversity of microcontroller units, ranging from smart cellular telephones or reduced general purpose devices, ESP8266 or RaspberryPi3 to any type of Internet of things devices. Therefore, the general way of connecting the microcontroller units to the Internet is through hub nodes, so that the information can be propagated and shared among them. The main purpose of this article is to yield an adaptive reconfiguration algorithm to link all the sensor nodes (microcontroller units) of a network based on the fractal topology, avoiding the use of hub nodes, in order for the microcontroller units to share all the parameters established in the Internet of things network only through two adjacent sensor nodes as long as any sensor node in the network knows all the parameters of the other ones, even if the sensor nodes are not adjacent. To achieve the above, in this work, an Internet of things network was built based on the Hilbert fractal for being a filling-space curve yielded from the L-systems paradigm, so this fractal Hilbert topology allows access to the entire Internet of things network in a dynamic way, and it is possible to reconfigure the network topology when a new sensor node is attached by applying artificial intelligence to intelligent and dynamic environments.

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Section: Related Workmentioning

confidence: 99%

Generation of broadcasting for fractal adaptive Internet of things reconfiguration under the swarm intelligence paradigm

Moreno

Matamoros

Tejeida

et al. 2020

International Journal of Distributed Sensor Networks

Self Cite

View full text Add to dashboard Cite

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“…It is important to mention that this work is an extension of the article presented by Moreno et al [5] in the Volume 11288 of the Lecture Notes in Artificial Intelligence, but we have completely modified the theoretical and methodological frameworks, in addition of the experimental results, since this proposal is a dynamic version of the static already presented algorithm.…”

Section: Introductionmentioning

confidence: 99%

Distributed Learning Fractal Algorithm for Optimizing a Centralized Control Topology of Wireless Sensor Network Based on the Hilbert Curve L-System

Moreno

Matamoros

Tejeida

et al. 2019

Sensors

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Wireless sensor networks (WSNs) consist of a large number of small devices or nodes, called micro controller units (MCUs) and located in homes and/or offices, to be operated through the internet from anywhere, making these devices smarter and more efficient. Quality of service routing is one of the critical challenges in WSNs, especially in surveillance systems. To improve the efficiency of the network, in this article we proposes a distributed learning fractal algorithm (DFLA) to design the control topology of a wireless sensor network (WSN), whose nodes are the MCUs distributed in a physical space and which are connected to share parameters of the sensors such as concentrations of C O 2 , humidity, temperature within the space or adjustment of the intensity of light inside and outside the home or office. For this, we start defining the production rules of the L-systems to generate the Hilbert fractal, since these rules facilitate the generation of this fractal, which is a fill-space curve. Then, we model the optimization of a centralized control topology of WSNs and proposed a DFLA to find the best two nodes where a device can find the highly reliable link between these nodes. Thus, we propose a software defined network (SDN) with strong mobility since it can be reconfigured depending on the amount of nodes, also we employ a target coverage because distributed learning fractal algorithm (DLFA) only consider reliable links among devices. Finally, through laboratory tests and computer simulations, we demonstrate the effectiveness of our approach by means of a fractal routing in WSNs, by using a large amount of WSNs devices (from 16 to 64 sensors) for real time monitoring of different parameters, in order to make efficient WSNs and its application in a forthcoming Smart City.

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

Cited by 2 publications

References 9 publications

Generation of broadcasting for fractal adaptive Internet of things reconfiguration under the swarm intelligence paradigm

Generation of broadcasting for fractal adaptive Internet of things reconfiguration under the swarm intelligence paradigm

Distributed Learning Fractal Algorithm for Optimizing a Centralized Control Topology of Wireless Sensor Network Based on the Hilbert Curve L-System

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