IEEE Project Abstract

Internet of Things (IoT) connects physical, cyber and human spaces. Event-based system is one of the corner stones to help IoT achieve real-time monitoring, context-awareness and intelligent control. In the era of big data, the huge amount and high complexity of event inference rule pose a great challenge to traditional event-based system in its efficiency, especially resources-constrained IoT edge systems. This paper proposes a high-efficiency joint event inference model for real-time context awareness and decision-making in IoT edge systems. We define different kinds of redundancy relations between event inference models and propose a description mechanism, named event containing graph, to support multi-pattern optimization. Three operation single-pattern event inference models, Merge, Failure and Output are defined respectively. The joint inference model is established by merging sharing patterns, constructing failure transitions and conditional output to eliminate inter-model redundancies. Experimental results prove that the joint model consumes less computational resources and provides higher performance than other benchmarks. It also verifies and proves that joint model has better optimization effect when processing large number of complex events. Especially in edge computing environment, joint inference model improves the real-time performance and significantly reduces the energy consumption in data transmission from edges to data center.