Fast energy-aware OLSR routing in VANETs by means of a parallel evolutionary algorithm

2012-04-27

Cluster Computing

AI Summary

To address the high energy consumption and slow convergence of the Optimized Link State Routing (OLSR) protocol in vehicular ad hoc networks (VANETs), this paper proposes an energy-aware routing optimization method based on a parallel evolutionary algorithm. Specifically, a parallel genetic algorithm is integrated into the OLSR control plane, coupled with distributed fitness evaluation and a mobility prediction model to jointly optimize topology awareness, energy balancing, and low-latency path selection. Furthermore, the OLSR protocol is extended to support dynamic feedback of node energy states. NS-2 simulation results demonstrate that, compared to standard OLSR, the proposed approach reduces average energy consumption by 32%, accelerates routing convergence by a factor of 2.1, and decreases end-to-end delay by 27%. These improvements significantly enhance both energy efficiency and real-time performance in VANETs.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Problem

• Energy Efficiency
• OLSR Optimization
• Vehicular Ad-Hoc Networks

Innovation

• Parallel Evolutionary Algorithm
• Energy Efficiency
• Vehicular Ad-Hoc Networks (VANETs)