在工业物联网中使用量子知情的人工蜂鸟优化算法进行节能集群
S Rajkumar1, R Gopalakrishnan2
1Department of Computer Science and Engineering, K. S. Rangasamy College of Technology, Tiruchengode, 637215, Tamil Nadu, India. rajkumars@ksrct.ac.in.
Scientific reports
|October 10, 2025
概括
一个新的能源效率高的量子信息化人工蜂鸟优化算法 (EEQIAHBOA) 增强了工业物联网 (IIoT) 网络. 这种方法提高了能源效率32.12%,网络寿命提高了35.62%.
科学领域:
- 计算机科学 计算机科学
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
背景情况:
- 工业物联网 (IIoT) 网络面临着复杂性,实时处理和大量数据量的挑战.
- 在IIoT中,聚类对于组织设备和数据至关重要,以改善资源利用和网络管理.
- 采用集群智能优化算法来选择最佳的集群头 (CHs) 并确保可靠的路由以提高性能.
研究的目的:
- 为IIoT网络提出一个能源效率高的量子信息化人工蜂鸟优化算法 (EEQIAHBOA).
- 为解决IIoT系统中节能这一关键问题.
- 通过优化集群和路由来最大限度地提高工业物联网应用程序的性能和可靠性.
主要方法:
- 开发EEQIAHBOA,整合量子知情原理与人工蜂鸟优化.
- 实现EEQIAHBOA作为路由算法,确定信息启发式因素和有效的编码方案.
- 应用EEQIAHBOA作为一个集群算法,考虑CH选择的剩余能量,节点距离和能耗.
主要成果:
- 在EEQIAHBOA,能源效率提高了32.12%.
- 与基线方法相比,实现了35.62%的网络寿命增长.
- 该算法在收集信息并将其传送到基站进行决策方面被证明是有效的.
结论:
- EEQIAHBOA为IIoT提供了能源效率和网络寿命的显著提升.
- 拟议的算法为复杂的工业环境中的路由和集群提供了可靠的解决方案.
- 通过优化资源管理和节能,EEQIAHBOA有助于实现更具成本效益和可靠的工业应用.
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