基于车辆互联网 (IoV) 的任务调度方法使用模糊逻辑技术在雾中计算使车辆特设网络 (VANET) 成为可能
Muhammad Ehtisham1, Mahmood Ul Hassan2, Amin A Al-Awady2
1Department of IT, The University of Haripur, Haripur 22620, Pakistan.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
一个模糊逻辑系统优化了车辆特设网络 (VANET) 中的任务调度,以实现车辆互联网 (IoV). 这种智能运输系统方法减少了智能汽车的延迟并改善了响应时间.
科学领域:
- 智能运输系统 (ITS) 是一种智能运输系统.
- 车辆特设网络 (VANETs) 的使用
- 汽车互联网 (IoVs) 的互联网
- 迷雾计算 迷雾计算 迷雾计算
背景情况:
- 智能运输系统 (ITS) 越来越依赖于车辆特设网络 (VANET) 和车辆互联网 (IoV).
- 雾计算作为云扩展,提供接近VANET的处理基础设施,支持智能汽车.
- VANET在车辆处理能力,带宽,时间和高速移动性方面面临限制,需要有效的任务卸载.
研究的目的:
- 在 IoV 架构中为 VANET 提出基于模糊逻辑的任务调度系统.
- 为了最大限度地减少延迟并提高车辆向雾层卸载任务的响应时间.
- 为了有效地管理工作负载,考虑到车辆节点的有限资源.
主要方法:
- 为VANETs开发了一个基于模糊逻辑的任务调度算法.
- 算法选择合适的处理单元,并将任务与相关资源卸载到雾层.
- 利用了超过5000个清晰值的数据集,用于雾计算指标,如系统利用率,延迟和任务截止日期.
主要成果:
- 拟议的框架在任务比率方面超过了现有的算法 (增长了13%) 并减少了平均周转时间 (增长了9%).
- 观察到显著的改善是尽量减少制作时间 (增长15%) 和克服平均延迟.
- 该系统有效地将任务安排到雾层,从而减少了响应时间和总体任务完成时间.
结论:
- 基于模糊逻辑的任务调度系统有效地解决了 IoVs 的 VANET 中的延迟和响应时间挑战.
- 拟议的方法提供了一个强大的解决方案,可以将任务卸载到雾计算环境中,同时尊重车辆资源限制.
- 在关键性能指标上,模拟结果验证了拟议技术在传统算法上的优越性.
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