Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS.

Area of Science:

• Computer Science
• Artificial Intelligence
• Network Engineering

Background:

• The Internet of Things (IoT) is rapidly expanding, presenting challenges in managing heterogeneous and dynamic networks.
• IoT complexity introduces vulnerabilities, necessitating intelligent management for sustained connectivity and performance.
• Existing IoT management strategies struggle with real-time adaptation to dynamic environments and energy efficiency.

Purpose of the Study:

• To survey the application of Machine Learning (ML) techniques for enhancing IoT systems.
• To provide an overview of diverse IoT applications benefiting from ML integration.
• To identify future research challenges and review current literature on ML in IoT.

Main Methods:

• Literature review of existing research on Machine Learning in IoT.
• Categorization of ML applications across various IoT domains.
• Analysis of ML's impact on IoT connectivity, Quality of Service (QoS), and energy consumption.

Main Results:

• Machine Learning significantly improves IoT connectivity, QoS, and energy efficiency in dynamic environments.
• ML enables intelligent management, addressing the complexity and vulnerabilities inherent in large-scale IoT deployments.
• Specific ML applications are highlighted for smart cities, smart homes, and smart healthcare, detailing their advantages.

Conclusions:

• Machine Learning is pivotal for overcoming IoT challenges and unlocking the full potential of smart applications.
• Further research is needed to address the identified ML challenges in the evolving IoT landscape.
• This survey provides a comprehensive overview and a foundation for future work in ML-driven IoT solutions.