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Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks
With the dramatic increase of connected devices, the Internet of things (IoT) paradigm has become an important solution in supporting dense scenarios such as smart cities. The concept of heterogeneous networks (HetNets) has emerged as a viable solution to improving the capacity of cellular networks...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263749/ https://www.ncbi.nlm.nih.gov/pubmed/30400631 http://dx.doi.org/10.3390/s18113779 |
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author | Gomez, Cesar A. Shami, Abdallah Wang, Xianbin |
author_facet | Gomez, Cesar A. Shami, Abdallah Wang, Xianbin |
author_sort | Gomez, Cesar A. |
collection | PubMed |
description | With the dramatic increase of connected devices, the Internet of things (IoT) paradigm has become an important solution in supporting dense scenarios such as smart cities. The concept of heterogeneous networks (HetNets) has emerged as a viable solution to improving the capacity of cellular networks in such scenarios. However, achieving optimal load balancing is not trivial due to the complexity and dynamics in HetNets. For this reason, we propose a load balancing scheme based on machine learning techniques that uses both unsupervised and supervised methods, as well as a Markov Decision Process (MDP). As a use case, we apply our scheme to enhance the capabilities of an urban IoT network operating under the LoRaWAN standard. The simulation results show that the packet delivery ratio (PDR) is increased when our scheme is utilized in an unbalanced network and, consequently, the energy cost of data delivery is reduced. Furthermore, we demonstrate that better outcomes are attained when some techniques are combined, achieving a PDR improvement of up to about 50% and reducing the energy cost by nearly 20% in a multicell scenario with 5000 devices requesting downlink traffic. |
format | Online Article Text |
id | pubmed-6263749 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-62637492018-12-12 Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks Gomez, Cesar A. Shami, Abdallah Wang, Xianbin Sensors (Basel) Article With the dramatic increase of connected devices, the Internet of things (IoT) paradigm has become an important solution in supporting dense scenarios such as smart cities. The concept of heterogeneous networks (HetNets) has emerged as a viable solution to improving the capacity of cellular networks in such scenarios. However, achieving optimal load balancing is not trivial due to the complexity and dynamics in HetNets. For this reason, we propose a load balancing scheme based on machine learning techniques that uses both unsupervised and supervised methods, as well as a Markov Decision Process (MDP). As a use case, we apply our scheme to enhance the capabilities of an urban IoT network operating under the LoRaWAN standard. The simulation results show that the packet delivery ratio (PDR) is increased when our scheme is utilized in an unbalanced network and, consequently, the energy cost of data delivery is reduced. Furthermore, we demonstrate that better outcomes are attained when some techniques are combined, achieving a PDR improvement of up to about 50% and reducing the energy cost by nearly 20% in a multicell scenario with 5000 devices requesting downlink traffic. MDPI 2018-11-05 /pmc/articles/PMC6263749/ /pubmed/30400631 http://dx.doi.org/10.3390/s18113779 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gomez, Cesar A. Shami, Abdallah Wang, Xianbin Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks |
title | Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks |
title_full | Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks |
title_fullStr | Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks |
title_full_unstemmed | Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks |
title_short | Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks |
title_sort | machine learning aided scheme for load balancing in dense iot networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263749/ https://www.ncbi.nlm.nih.gov/pubmed/30400631 http://dx.doi.org/10.3390/s18113779 |
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