Service Discovery And Selection Based On Dynamic QoS In IoT

With the rapid growth in Io T services, Io T services have become "services ocean", and users are faced to the challenge that how to discovery and choose services from the ocean. Many research teams increase the investment on the study of service discovery and selection. How to find the list of similar services that meet users’ requirements from the large number of services, and how to choose the most optimal services from the list is the problem. The existing service discovery algorithms are mainly based on the calculation of similarity or keywords matching, the integrity of the result should be improved. And the existing selection algorithms based on Qo S are mainly based on the objective attributes, which could not correct the users’ selection dynamically with the users’ use, and the accuracy is limited. So this article is studied from the following three aspects.Firstly, because of the existing service description files are essentially WSDL files, before the research of service discovery based on ontology semantic distance, this paper studies mapping from WSDL files to the semantic description files(eg, OWL-S).Secondly, this paper puts forward the service discovery method based on ontology semantic distance. In order to find the set of similar services from a lot of services accurately and completely, we use the ontology describing language OWL-S which has the function of semantic reasoning and the original keywords extend to the semantic reasoning judgment. The important idea of service discovery algorithm research is the matching between the service user requests and the existing services. This paper presents a matching algorithm based on semantic distance, and use the OWLS-TC3 to build simulation experiments to verify that the algorithm effectively improves the success rate and the intergrity of the service discovery.Finally, this paper presents a dynamic service selection algorithm based on Qo S. The algorithm based on considering the Qo S static objective properties, joins the research of the subjective attributes of the Qo S, and transforms from static attribute data computation to dynamic calculation. In addition, it also adds weights to improve the accuracy and reliability of the algorithm. The simulation experiments show that, based on the set of similar services, the success rate and the accuracy of the algorithm are much better than other similar algorithms.