用智能算法自动优化车载网络通信协议，提升传输效率与可靠性。
Automatic tuning of communication protocols for vehicular ad hoc networks using metaheuristics
- 采用五种进化算法自动调优文件传输协议参数。
- 粒子群算法在城市与高速场景下均实现最低丢包率和最快传输速度。
- 适合车联网系统设计者快速获取最优通信配置方案。
车载自组织网络（VANETs）由可自发互联的车辆组成，无需预设基础设施。在部署前对通信协议进行最优配置至关重要，以预先获得理想的网络服务质量（QoS）。本文研究如何配置文件传输协议（FTC），以优化传输时间、丢包数和传输数据量，在真实城市场景与高速公路场景下展开实验。采用ns-2仿真平台，对比了五种主流优化算法：粒子群优化（PSO）、差分进化（DE）、遗传算法（GA）、进化策略（ES）和模拟退火（SA）。结果表明，PSO在两种场景中均表现最佳，显著优于其他算法，有效提升了传输效率与可靠性。
原文摘要 · Abstract (English)
The emerging field of vehicular ad hoc networks (VANETs) deals with a set of communicating vehicles which are able to spontaneously interconnect without any pre-existing infrastructure. In such kind of networks, it is crucial to make an optimal configuration of the communication protocols previously to the final network deployment. This way, a human designer can obtain an optimal QoS of the network beforehand. The problem we consider in this work lies in configuring the File Transfer protocol Configuration (FTC) with the aim of optimizing the transmission time, the number of lost packets, and the amount of data transferred in realistic VANET scenarios. We face the FTC with five representative state-of-the-art optimization techniques and compare their performance. These algorithms are: Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA), Evolutionary Strategy (ES), and Simulated Annealing (SA). For our tests, two typical environment instances of VANETs for Urban and Highway scenarios have been defined. The experiments using ns- 2 (a well-known realistic VANET simulator) reveal that PSO outperforms all the compared algorithms for both studied VANET instances.
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