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Related Experiment Video

Updated: Dec 7, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

A freight integer linear programming model under fog computing and its application in the optimization of vehicle

Xiaowen Wang¹, Peng Qiu²

¹China Academy of Transportation Sciences, Beijing, China.

|September 24, 2020

Summary

Fog computing (FG) and integer linear programming (ILP) optimize Internet of Vehicles (IoV) networking deployment. This FG-ILP model offers lower costs and faster performance than cloud solutions, especially for smaller networks.

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

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Last Updated: Dec 7, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

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Published on: November 26, 2019

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

Computer Science
Networking
Operations Research

Background:

The exponential growth of data in the Internet of Vehicles (IoV) overwhelms traditional architectures.
Existing IoV systems struggle to meet increasing user demands for data processing and network efficiency.

Purpose of the Study:

To develop a novel freight integer linear programming (ILP) model integrated with fog computing (FG).
To evaluate the effectiveness of this FG-ILP model for optimizing networking deployment (ND) within IoV systems.

Main Methods:

Integration of fog computing (FG) principles with integer linear programming (ILP) to construct a specialized freight computing model.
Simulation-based analysis to assess the model's performance in various IoV networking deployment scenarios.

Main Results:

The FG-ILP model demonstrates effectiveness in optimizing objective functions, particularly in small-scale IoV scenarios.
While utilization is limited in large-scale scenarios, the FG-based ND solution significantly reduces network costs and running times compared to traditional cloud computing.
The model exhibits apparent effectiveness and efficiency, with lower costs and shorter execution times.

Conclusions:

The fog computing-based integer linear programming model provides a cost-effective and efficient solution for Internet of Vehicles networking deployment optimization.
The study offers crucial experimental validation and theoretical support for future deployments of freight vehicles within the Internet of Things ecosystem.