Semantic Modeling Framework And Composition Technology Research For IoT Services

As an emerging technology,Io T(Internet of Things)has been widely used in many fields,such as smart power grids,smart homes,and industrial automation.However,due to the natural heterogeneity of Io T systems,many Io T systems will gradually be chimneyed,and the related data cannot be shared.Io T services provide an effective solution to this heterogeneous problem by combining service computing and Io T technologies.To achieve efficient utilization of Io T services,abstract modeling and analysis of them are essential.Traditional Web service modeling and analysis methods cannot be directly applied to Io T services,so targeted research is needed.In this paper,semantic modeling of Io T services and related combinatorial techniques are investigated to address the above issues,with the following specific work and innovations:(1)An ontology-based semantic modeling framework for Io T services is constructed.The framework distinguishes traditional entities into physical entities and information entities,adds states to entity attributes,and reflects the dependence of Io T services on the real world through the interaction between entity attributes and Io T services.And based on this framework,the knowledge graph of Io T services is constructed using Neo4 j graph database to establish the relationship between services and improve the efficiency of subsequent service combinations.(2)An Io T service combination model is constructed and a combination algorithm is designed.The model utilizes ontology reasoning to achieve service matching and uses an improved graph planning algorithm to solve the service combination problem with the following improvements: first,the service relationships are stored in advance using Neo4 j,which effectively reduces the query time in the graph planning algorithm.Secondly,combining ontology inference rules with the Skyline algorithm,the services are screened once to remove the less competitive services.Finally,the Qo S of the final combined service is further improved by considering the attribute matching degree and Qo S in the reverse search process of graph planning to speed up the search process and pruning the services in the solution path for secondary screening.(3)Based on the above research,the effectiveness and practicality of the Io T service modeling framework and combination techniques proposed in this thesis are verified using a service combination scenario.The performance of the relevant combination algorithm is also verified.The results show that the combination method proposed in this paper can improve the execution efficiency and Qo S of the combined services to a certain extent while meeting the functional requirements of the services.