Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Gomez, Cesar A., Shami, Abdallah, Wang, Xianbin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
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
_version_ 1783375353984581632
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
work_keys_str_mv AT gomezcesara machinelearningaidedschemeforloadbalancingindenseiotnetworks
AT shamiabdallah machinelearningaidedschemeforloadbalancingindenseiotnetworks
AT wangxianbin machinelearningaidedschemeforloadbalancingindenseiotnetworks