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基于共识的聚类和数据聚合在多代理系统的分散网络中的数据聚合
Joshua Julian Damanik1, Ming Chong Lim1, Hyeon-Mun Jeong1
1Aerospace Engineering Department, Korea Advanced Institute of Science & Technology, Daejeon, South Korea.
PeerJ. Computer science
|September 14, 2023
概括
本研究介绍了针对多代理系统的去中心化数据聚合算法. 它通过使用信任值在集群网络中提高了准确性,从而实现了高效的 COUNT 和 SUM 操作.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 分布式系统 分布式系统
背景情况:
- 多代理系统 (MAS) 对各种应用至关重要,但在集群环境中优化大型异质连接网络方面面临挑战.
- 集群MAS中的分散规划算法需要准确的集群信息和来自其他集群的噪音补偿.
研究的目的:
- 为集群多代理系统提出一种新的去中心化数据聚合算法.
- 在分散的环境中提高 COUNT 和 SUM 聚合操作的准确性和效率.
主要方法:
- 一个分散的数据聚合算法,使用共识方法进行 COUNT 和 SUM 运算.
- 引入一个可信度值,以便准确的集群级聚合.
- 包括一个校正参数来优化解决方案的准确性和计算时间.
主要成果:
- 该算法以可接受的准确性和趋同时间证明了对聚合数据的趋同.
- 在涉及大型,稀疏网络和有限带宽的模拟中成功评估.
- 验证信托价值机制,以提高聚合精度.
结论:
- 拟议的算法有效地解决了聚类多代理系统中的数据聚合挑战.
- 开发的工具为复杂的多代理多任务环境中强大的去中心化任务分配提供了基础.
- 这项工作有助于在网络系统中推进高效准确的去中心化计算.
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