智能环境下分布式Rete算法

针对智能环境中基于Rete的规则推理引擎需要将数据集中到sink节点，导致传感器网络中数据传输量过大的问题，建立了Rete网络代价模型，并提出了最小传输代价的Rete分布的算法（MCoRDS）。该算法通过统计Rete网络中子模式对事实数据的依赖，发现大部分子模式在对应事实数据采集Sensor附近便具备了计算推理条件，故将Rete网络中的子模式规则分布到最早汇集其所需所有事实数据的Sensor中，即可避免事实数据进一步往sink节点的传输，从而大量减少传感器网络中的数据传输量。对比将Rete网络放置在sink节点的集中式推理进行了4组仿真实验。其中第4组实验，传感器网络总跳数由85000减至8036，减少约90.5%；其余组实验传输跳数也有一定的减少。实验结果表明，最小代价的Rete分布具有更小的数据传输量，在规则触发频率低、规则规模较大的情况下尤甚。

Distributed Rete algorithm in smart environment

Concerning the problem that rule-based inference engine in smart environment needs to centralize data to sink node resulting in excessive data transmission in sensor network, a Minimum Transmission Cost of Rete Distribution Scheme algorithm (MCoRDS) based on Rete network cost model was proposed. Through the dependence statistics of sub-rule patterns in Rete Network on fact data, it was found that many sub-rule patterns could be reasoned nearby the source data collected sensor. Data transmission to sink node could be cut down and the data transmission of whole sensor network was decreased by distributing sub-rule patterns of Rete network into the sensor which firstly collected all the source data for it. Compared to centralized inference which places the Rete network in the sink node, 4 experiments were conducted. In the 4th experiment, total sensor network hops was reduced from 85000 to 8036, about 90.5% reduction, the other experiments had some reduction too. The experimental results show that MCoRDS has lower data transmission, especially in the case of large-scale rules and low frequency rule trigger.