Machine Learning Algorithms for Optimization and Intelligence in Wireless Networks
Lopamundra Hota, Biraja Prasad Nayak, Arun Kumar
Abstract
Wireless sensor networks (WSNs), mobile ad hoc networks (MANETs), vehicular ad hoc networks (VANETs), and underwater sensor networks (USNs) are dynamic and resource-constrained wireless communication environments that face numerous challenges. Machine learning algorithms have emerged as powerful tools to tackle these challenges and improve these networks’ performance, efficiency, and security. This survey explores the application of machine learning algorithms in WSNs, MANETs, VANETs, and USNs. This chapter provides an overview of each network type’s fundamental concepts and characteristics, emphasizing their unique requirements and constraints. Next, the chapter delves into various machine learning (ML) algorithms commonly employed in these networks, including traditional ML and reinforcement learning (RL) algorithms. The fundamental principles, advantages, and potential applications in network optimization, data transmission, routing, MAC optimization, resource allocation, fault detection, intrusion detection, and other QoS for a network are discussed for the ML algorithms. Furthermore, the chapter presents specific use cases and applications of ML algorithms in each network type, showcasing their potential to improve network performance, energy efficiency, security, and reliability. The challenges and considerations in implementing machine learning algorithms in these dynamic network environments are discussed. The chapter also highlights the need for adaptive and self-learning network architectures for real-time decision-making algorithms and collaborative intelligence approaches. Finally, the chapter identifies the open research directions and future trends in integrating ML algorithms in WSNs, MANETs, VANETs, and USNs. This survey serves as a comprehensive reference for researchers, engineers, and practitioners working in the field of wireless and ad hoc networks. It offers insights into the current state-of-the-art challenges and potential applications of machine learning algorithms in WSNs, MANETs, VANETs, and USNs, fostering further advancements and innovations in this rapidly evolving domain.