A scalable rule engine system for trigger-action application in large-scale IoT environment

Abstract

The development of IoT techniques and the automation enabled by trigger-action platforms have brought great convenience to human life. However, with time the proliferating IoT devices will eventually overwhelm such platforms. Specifically, the ever-increasing huge amount of linkage rules significantly challenge the response rate as well as the efficiency of conflict rule detection, which will lead to terrible user experience and disastrous security issues. In this work, we develop a rule engine system for large-scale trigger-action applications. The key insight is that the linkage rules can be executed independent from the central cloud platform, which only require edge-side gateways and an efficient addressing strategy. The platform-side maintains an global image consisting of mappings between rules and gateway addresses, while edge-side rule engine is designed based on an open source tool, i.e., Drools. The implemented RETE algorithm helps to carry out large-scale linkage rules. In addition, we design a lightweight yet effective conflict detection algorithm that categorizes those rules into several types to downgrade the computation complexity. Experiments prove that the system can reduce the resource consumption of the platform-side and remarkably enhance the response speed. Moreover, our system also achieve a high efficacy when detecting conflicts.