Research And Optimization Of Smart Home System Based On Web Of Things

With the rapid development of Internet of Things and intelligent hardware industry,smart home has drawn a lot attention of domestic and foreign scientific as well as technical organizations and technology enthusiasts,relevant products have gradually been into the market and accepted by consumers.At present,most smart home systems can only achieved some simple control operation of home appliances,but performance poorly in terms of user defined rules.There are some drawbacks:the system takes a long time when dealing with a large number of rules;lack of capacity of learning users' abnormal behaviors;and the rule set of the system is hard to update,etc.Research and optimization of this article aims at solving the above problems.First,investigation and research on WoT technology,intelligent home systems architecture,rules engine and other related technology are conducted.Based on the survey three algorithms are carried out to improve the performance of smart home.By adding a loop buffer and dynamic link to the Rete algorithm,the Rete+ algorithm is implemented to improve the time performance of the rule matching;online perceptron algorithm to learn the abnormal behavior of users;and finding hidden rules by mining the association rules through practical cases.While proposing the optimizations,experiments were carried out based on WoT smart home environment.Experiments were design to test the above algorithms with real environment perception information,user information and user behavior log as experimental data.And the results show that Rete+,online perceptron algorithm,and the association rule mining algorithm show superior performance,and realize the optimization of the smart home system.At the end of this paper,the work of this paper is summarized,and the direction of improvement is put forward.