服务质量意识多目标增强差异演变时间优化间隙道跳跃在异质物联网中的调度 传感器网络
Aida Vatankhah1, Ramiro Liscano1
1Department of Electrical, Computer and Software Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, Canada.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
本研究介绍了一种新的优化算法,即QoS意识的多目标增强差异进化优化 (QMDE),用于工业物联网 (IoT) 网络. QMDE有效地满足了严格的服务质量 (QoS) 要求,显著改善了数据包交付和减少延迟.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 优化算法 优化算法
背景情况:
- 工业物联网 (IoT) 应用要求严格的延迟和可靠性.
- IEEE 802.15.4e时间间隔通道跳转 (TSCH) 协议解决了这些需求.
- 在TSCH网络中为多目标QoS优化进行集中调度是复杂的.
研究的目的:
- 引入一个新的优化算法,QMDE,用于提高工业物联网网络的 QoS.
- 在设计集中调度系统时,应对多目标优化挑战.
- 为了确保所需的服务吞吐量以及关键的 QoS 度量,如延迟和数据包丢失.
主要方法:
- 开发QoS意识的多目标增强差异演变优化 (QMDE) 算法.
- 使用TSCH-SIM和Matlab进行共模拟,R2023a.
- 在各种传感器网络拓和工业QoS场景中进行了广泛的模拟.
- 对交通意识调度算法 (TASA) 的比较评估.
主要成果:
- QMDE生成最佳时间表,满足工业服务的QoS要求.
- 在16至100个节点的传感器网络中展示了有效的性能.
- 与TASA相比,QMDE显著提高了数据包交付比率 (PDR) 并减少了延迟.
结论:
- QMDE算法成功地解决了TSCH调度中的多目标优化问题.
- 在工业物联网中,QMDE为满足严格的 QoS 需求提供了强大的解决方案.
- 拟议的算法在PDR和延迟指标方面提供了卓越的性能.
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