# Using Machine Learning for Handover Optimization in Vehicular Fog Computing

## TL;DR

This paper presents a machine learning-based approach for optimizing handovers in vehicular fog computing, using neural networks to predict fog node selection and reduce service interruptions during vehicle mobility.

## Contribution

It introduces a novel dual neural network system combining feed-forward and recurrent models to improve handover prediction and latency estimation in vehicular fog computing.

## Key findings

- Achieved 99.2% accuracy in fog node prediction
- Reduced service interruption during handovers
- Effectively predicted latency and coverage areas

## Abstract

Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of device connections and offloaded tasks between fog nodes. To accomplish this, we make use of machine learning algorithms to learn from vehicle interactions with fog nodes. Our approach uses a three-layer feed-forward neural network to predict the correct fog node at a given location and time with 99.2 % accuracy on a test set. We also implement a dual stacked recurrent neural network (RNN) with long short-term memory (LSTM) cells capable of learning the latency, or cost, associated with these service requests. We create a simulation in JAMScript using a dataset of real-world vehicle movements to create a dataset to train these networks. We further propose the use of this predictive system in a smarter request routing mechanism to minimize the service interruption during handovers between fog nodes and to anticipate areas of low coverage through a series of experiments and test the models' performance on a test set.