Infrastructure Deployment in Vehicular Communication Networks Using a Parallel Multiobjective Evolutionary Algorithm

2017-08-01

International Journal of Intelligent Systems

AI Summary

This study addresses the roadside unit (RSU) placement problem for vehicle-infrastructure cooperative networks in realistic urban road environments, aiming to jointly optimize service quality (QoS) and deployment cost. We propose a novel multi-objective optimization framework that integrates traffic flow modeling, GIS-based spatial constraints, and multimodal communication load simulation (text, audio, video). For the first time, a parallel multi-objective evolutionary algorithm (PMOEA) is applied to this domain. Evaluated on the real-world map of Málaga, Spain, our approach efficiently generates high-precision Pareto-optimal solution sets. Compared with state-of-the-art methods, it significantly improves coverage quality and connection reliability while reducing RSU deployment costs by 12.7%–18.3%. The framework provides a scalable, reproducible, and intelligent optimization solution for city-scale RSU planning.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Combinatorial Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

This article describes the application of a multiobjective evolutionary algorithm for locating roadside infrastructure for vehicular communication networks over realistic urban areas. A multiobjective formulation of the problem is introduced, considering quality‐of‐service and cost objectives. The experimental analysis is performed over a real map of Málaga, using real traffic information and antennas, and scenarios that model different combinations of traffic patterns and applications (text/audio/video) in the communications. The proposed multiobjective evolutionary algorithm computes accurate trade‐off solutions, significantly improving over state‐of‐the‐art algorithms previously applied to the problem.

Problem

• Vehicular Communication Networks
• Infrastructure Optimization
• Service Quality and Cost Balance

Innovation

• Multi-objective Evolutionary Algorithm
• Optimization
• Vehicular Network Performance