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Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks.

Cesar A Gomez1, Abdallah Shami2, Xianbin Wang3

  • 1Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada. cgomezsu@uwo.ca.

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|November 8, 2018
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Summary
This summary is machine-generated.

This study introduces a machine learning-based load balancing scheme for Internet of Things (IoT) networks. The proposed method enhances packet delivery ratio and reduces energy consumption in complex heterogeneous networks (HetNets).

Keywords:
Internet of things (IoT)LoRaWANMarkov Decision Process (MDP)heterogeneous networks (HetNets)load balancingmachine learningsmart cities

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Area of Science:

  • Computer Science
  • Electrical Engineering

Background:

  • The proliferation of connected devices necessitates efficient solutions like the Internet of Things (IoT) for dense environments such as smart cities.
  • Heterogeneous Networks (HetNets) offer enhanced capacity but face challenges in optimal load balancing due to network complexity and dynamics.

Purpose of the Study:

  • To develop and evaluate a novel load balancing scheme for urban IoT networks.
  • To improve network performance metrics including packet delivery ratio (PDR) and energy efficiency.

Main Methods:

  • A hybrid machine learning approach combining unsupervised and supervised learning techniques.
  • Integration of a Markov Decision Process (MDP) for dynamic load balancing optimization.
  • Application and simulation within a LoRaWAN urban IoT network scenario.

Main Results:

  • The proposed scheme significantly increases the Packet Delivery Ratio (PDR) in unbalanced network conditions.
  • A notable reduction in the energy cost associated with data delivery was observed.
  • Combined techniques achieved up to a 50% PDR improvement and nearly 20% energy cost reduction.

Conclusions:

  • The developed machine learning-based load balancing scheme effectively addresses HetNet complexities in IoT deployments.
  • The scheme offers a practical solution for enhancing the performance and efficiency of urban IoT networks, particularly LoRaWAN.
  • Combining multiple machine learning techniques yields superior results for PDR and energy efficiency.