走向智能物联网调度,使用量子启发的潜在模型来优化能源和延迟
Tahir Alyas1, Qaiser Abbas2, Adeela Hayat3
1Department of Computer Science, Lahore Garrison University, Lahore, Pakistan. tahiralyas@lgu.edu.pk.
Scientific reports
|January 19, 2026
概括
经典的物联网 (IoT) 调度与延迟和能量斗争. 这种量子启发的框架通过学习潜伏表示和使用全局优化来改进物联网调度,减少延迟和能源消耗.
科学领域:
- 计算机科学 计算机科学
- 量子计算是一种量子计算.
- 人工智能的人工智能
背景情况:
- 经典的物联网 (IoT) 调度方法在优化延迟和能源消耗方面面临着挑战.
- 像遗传算法和强化学习这样的现有方法存在局限性,例如在动态物联网环境中进行本地搜索和缓慢融合.
- 这些局限性导致不同工作负载,往返时间 (RTT) 波动和大规模部署下效率低下的性能.
研究的目的:
- 为物联网系统引入一种新的量子灵感潜伏调度框架.
- 解决经典调度方法在实现延迟和能源使用之间的平衡方面的局限性.
- 在异质和动态的网络条件下提高物联网调度的适应性和效率.
主要方法:
- 拟议的框架结合了使用编码器学习潜在表示,并使用量子灵感的全球优化.
- 通过小型任务资源嵌入,使用隐藏的编码器来发现隐藏的任务资源交互.
- 量子启发的搜索利用重叠来进行探索,使其能够适应不断变化的网络状态并规避局部最小值.
主要成果:
- 量子启发的潜伏调度框架显示了与经典基线相比的显著改进.
- 延迟时间减少了高达25%,能源消耗减少了高达28%.
- 融合分析和可扩展性测试证实了该框架在物联网调度多目标优化方面的优势.
结论:
- 量子启发的潜伏调度框架为下一代物联网调度提供了一个可扩展,可解释和节能的解决方案.
- 该方法有效地处理复杂的任务-资源交互和动态网络状态.
- 这项工作为物联网系统中先进的量子启发潜伏建模铺平了道路.
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