The Reputation of Machine Learning in Wireless Sensor Networks and Vehicular Ad Hoc Networks

Abstract

It's difficult to deal with the dynamic nature of VANETs and WSNs in a way that makes sense. Machine learning (ML) is a preferred method for dealing with this kind of dynamicity. It is possible to define machine learning (ML) as a way of dealing with heterogeneous data in order to get the most out of a network without involving humans in the process or teaching it anything. Several techniques for WSN and VANETs based on ML are covered in this study, which provides a fast overview of the main ML ideas. Open difficulties and challenges in quickly changing networks, as well as diverse algorithms in relation to ML models and methodologies, are also covered in the following sections. We've provided a list of some of the most popular machine learning (ML) approaches for you to consider. As a starting point for further research, this article provides an overview of the various ML techniques and their difficulties. This paper's comparative examination of current state-of-the-art ML applications in WSN and VANETs is outstanding.

Deep learning algorithms and mechanisms in navigation for vehicular crowd managementsystems in real-time for smart transportation

“…Work [12] made use of real-world location tracking information gathered from Taxi Telematics system, which was created in Jeju, Korea. The purpose of their investigation was to discover meaningful taxicab movement patterns.…”