电子学报 ›› 2020, Vol. 48 ›› Issue (1): 35-43.DOI: 10.3969/j.issn.0372-2112.2020.01.005

• 学术论文 • 上一篇    下一篇

智能环境下基于雾计算的推理节点优化分配研究

汪成亮, 黄心田   

  1. 重庆大学计算机学院, 重庆 400044
  • 收稿日期:2018-01-16 修回日期:2019-04-21 出版日期:2020-01-25 发布日期:2020-01-25
  • 通讯作者: 汪成亮
  • 作者简介:黄心田 女,1994年8月出生,重庆人.硕士,主要研究方向为物联网、规则推理计算.E-mail:lpxhxt@163.com
  • 基金资助:
    国家自然科学基金(No.61672115);重庆市社会事业与民生保障科技创新专项(No.cstc2017shmsA30003)

Study on Optimal Allocation of Inference Nodes for Fog Computing in Smart Environment

WANG Cheng-liang, HUANG Xin-tian   

  1. College of Computer Science, Chongqing University, Chongqing 400044, China
  • Received:2018-01-16 Revised:2019-04-21 Online:2020-01-25 Published:2020-01-25

摘要: 智能环境传统的规则推理机制中,网关内布置的推理机从各种传感器中获取推理所需数据并与规则库相匹配,承担整个推理工作.本文利用Rete算法将规则构建为推理网络,并结合雾计算的概念将Rete推理节点分配至环境内配置的智能节点中协同推理以减轻网关负载,由此推理节点的分配成为关键,分配不合理将导致资源利用不平衡及响应延迟.本文利用活动影响下规则触发的规律设计了活动聚类算法CoA(Clustering of Activities)对活动聚类后分别建立其推理网络,计算出智能节点之间的最短路径后将结果代入针对其层次延迟性而设计的分配算法AAoRN(Allocation Algorithm of Rete Inference Nodes),从而将推理节点最优分配至各个智能节点.理论分析和实验结果表明,本文机制在有效利用智能节点资源的同时降低了大致55%的延迟.

关键词: 智能环境, 规则推理, Rete算法, 雾计算, 活动模式

Abstract: Under traditional rule-based reasoning in smart environment,the inference engine deployed in gateway collects data from various sensors to match the rules,which undertakes whole reasoning work.In current research,we use Rete algorithm to construct an inference network by rules and then allocate the Rete inference nodes to smart nodes for collaborative reasoning based on fog computing,therefore,the allocation mechanism becomes crucial.In this paper,we utilize the regularity of rules triggered under the influence of activities to design an algorithm CoA(Clustering of Activity),which clusters activities and respectively constructs the inference networks,subsequently,we calculate the shortest path between smart nodes and substitute the results into AAoRN(Allocation Algorithm of Rete Inference Nodes),which is proposed to overcome hierarchical delay for optimally allocating each inference node.Theoretical analysis and experimental results show that the proposed mechanism efficiently utilizes the resources and has reduced the delay by about 55%.

Key words: smart environment, rule-based reasoning, Rete algorithm, fog computing, activity pattern

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