A TAS-Model-Based Algorithm for Rule Redundancy Detection and Scene Scheduling in Smart Home Systems

In smart home systems, users enjoy the comfort and convenience by rule subscription and execution. However, with the increase in the complex and the number of rules, there is an increased risk of redundancy within the process of rule customization and execution. Obviously, redundancy will add weight to the system, affect the administrative operation, and reduce the system efficiency. To address the above-mentioned issues, a formal model Trigger-Actuator-Status for rules is devised. Such rules are defined as a tuple, which contains triggers, actuators, and states. Then, rules are processed with the methods of classification, combination, and analytics to generate redundant types so as to describe the redundancy relationship among rules. After that, a redundancy detection algorithm is proposed according to the determined relationship. Besides, in order to eliminate and avoid the occurrence of redundancy, redundancy for scenarios based on rule redundancy is detected and solved. The experimental results show that the proposed scheme can provide more accurate classification results and identify part or full redundancy within and among rules. Meanwhile, our scheme enables redundancy elimination in the phase of rule setting and scenario setup as well as redundancy avoidance in the stage of rule execution and scenario start-up, which significantly reduces the number of rules to greatly enhance the efficiency of the systems.