Distributed learning fractal algorithm for optimizing a centralized control topology of wireless sensor network based on the hilbert curve L-system

Jaime Moreno, Oswaldo Morales, Ricardo Tejeida, Juan Posadas, Hugo Quintana, Grigori Sidorov

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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 CO 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.

Original languageEnglish
Article number1442
JournalSensors (Switzerland)
Volume19
Issue number6
DOIs
StatePublished - 2 Mar 2019

Keywords

  • Adaptive algorithm
  • Centralized control topology
  • Distributed learning
  • Hilbert curve
  • L-system
  • Optimization
  • Quality of service
  • Swarm intelligence
  • WSNs

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