PROJECT TITLE:

Efficient Rule Engine for Smart Building Systems

ABSTRACT:

In sensible building systems, the automatic management of devices depends on matching the sensed atmosphere data to customized rules. With the event of wireless sensor and actuator networks (WSANs), low-value and self-organized wireless sensors and actuators will enhance smart building systems, but produce abundant sensing knowledge. So, a rule engine with ability of economical rule matching is the inspiration of WSANs based smart building systems. However, traditional rule engines mainly concentrate on the advanced processing mechanism and omit the amount of sensing knowledge, which aren't appropriate for large scale WSANs based sensible building systems. To address these problems, we tend to build an economical rule engine. Specifically, we style an atomic event extraction module for extracting atomic event from data messages, and then build a -network to amass the atomic conditions for parsing the atomic trigger events. Taking the atomic trigger events as the key set of MPHF, we tend to construct the minimal excellent hash table that will filter the bulk of the unused atomic event with O (1) time overhead. Moreover, a rule engine adaption theme is proposed to minimize the rule matching overhead. We tend to implement the proposed rule engine during a sensible smart building system. The experimental results show that the rule engine can perform efficiently and flexibly with high data throughput and giant rule set.


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