基于多因素成本函数的干扰意识集群与Voronoi细胞分区用于密集的WSN
Soundrarajan Sam Peter1, Parimanam Jayarajan2, Rajagopal Maheswar3
1Department of Artificial Intelligence and Data Science, Sri Eshwar College of Engineering, Coimbatore 641202, Tamil Nadu, India.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
一个新的密度感知自适应集群 (DAAC) 协议通过改进集群头的选择和形成来优化无线传感器网络 (WSN). 这导致网络寿命显著延长,并在密集环境中增强数据传输.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 无线通信无线通信
背景情况:
- 传统的集群算法,如LEACH和HEED,由于负载分布不平衡和高度争议,与密集的无线传感器网络 (WSNs) 斗争.
- 现有的方法往往导致密集地区的集群头 (CHs) 过载,稀疏地区的CHs使用不足,导致频繁的CH变化和网络效率降低.
- 这种低效率在需要稳定的网络运行的动态实时环境中尤其存在问题.
研究的目的:
- 开发一个密度感知自适应集群 (DAAC) 协议,以优化密集WSN中的CH选择和集群形成.
- 通过将节点密度和链接质量纳入CH选择指标来解决传统算法的局限性.
- 在密集的WSN部署中增强整体网络寿命,数据包交付比率和吞吐量.
主要方法:
- 开发了DAAC协议,利用剩余能量,局部节点密度和链路质量作为统一的CH检测指标.
- 实施了最小的CH间距离约束,以防止CH拥挤,并用于集群形成的多因素成本函数.
- 集成的动态重组由CH能量耗尽或负载密度显著变化引发,并使用动态Voronoi电池 (VCs) 进行干扰感知覆盖.
主要成果:
- DAAC显示,网络寿命比 LEACH 提高了 20.53%,比 HEED 提高了 32.51%.
- 该协议实现了数据包交付比率的平均增加8.14% (vs. LEACH) 和25.68% (vs. 这就是HEED).
- 总吞吐量封装出现了显著的改进:与LEACH相比提高了140.15%,与HEED相比提高了883.51%.
结论:
- DAAC有效地优化了密集的WSN中的CH选择和集群形成,超过了传统的算法,如LEACH和HEED.
- 该协议的自适应性和沃罗诺伊细胞的使用有助于改善网络寿命,数据可靠性和整体性能.
- 对于密集的WSN,DAAC提供了一个强大的解决方案,在极密集的场景中实现高效的层次扩展和二次 CH.
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