IoT(lnternet of Things) products developed based on different operating systems have certain differences, which pose a challenge to the deployment of products. In order to ensure the stable operation of IoT products, it is necessary to conduct a comprehensive performance evaluation, covering adaptability and compatibility. In this paper, the professional testing tools of the IPv6(lnternet Protocol version 6) are used to comprehensively and systematically test the products of the IoT with different operating systems. On the one hand, it aims to improve the standardization of products at the protocol level and ensure that products strictly comply with the relevant requirements of the IPv6 protocol; On the other hand, by constructing a test environment and message interaction, the adaptability and compatibility of products are deeply solved, thus improving the overall performance and user experience of products.

Some extensions of fuzzy sets such as interval-valued fuzzy sets, intuitionistic fuzzy sets, interval-valued intuitionistic fuzzy sets, type n-fuzzy sets, and neutrosophic sets provided powerful and practical tools for dealing with uncertainty in decision-making problems. Neutrosophic set is defined with three-dimensional membership functions to describe the degrees of truth, indeterminacy, and falsity. Neutrosophic set theory is a useful instrument to handle incomplete, inconsistent, and indeterminate information. In this paper, we first propose a modified score function for ranking single-valued neutrosophic numbers. Then, we suggest a TOPSIS method based on the proposed function for decision-making under group recommendation. The method is applied to deal with the hotel location selection problem, where the decision values of the attributes for alternatives and the weights of the attributes are given by decision-makers based on single-valued neutrosophic sets. Finally, numerical experiments are done. They show that the given method is more efficient as well as more reasonable tool for decision-making in contrast to the other existing methods.

The API of a Web service restricts the types of queries that the service can answer. For example, a Web service might provide a method that returns the songs of a given singer, but it might not provide a method that returns the singers of a given song. If the user asks for the singer of some specific song, then the Web service cannot be called - even though the underlying database might have the desired piece of information. This asymmetry is particularly problematic if the service is used in a Web service orchestration system. In this paper, we propose to use on-the-fly information extraction to collect values that can be used as parameter bindings for the Web service. We show how this idea can be integrated into a Web service orchestration system. Our approach is fully implemented in a prototype called SUSIE. We present experiments with real-life data and services to demonstrate the practical viability and good performance of our approach.

The proliferation of knowledge-sharing communities and the advances in information extraction have enabled the construction of large knowledge bases using the RDF data model to represent entities and relationships. However, as the Web and its latently embedded facts evolve, a knowledge base can never be complete and up-to-date. On the other hand, a rapidly increasing suite of Web services provide access to timely and high-quality information, but this is encapsulated by the service interface. We propose to leverage the information that could be dynamically obtained from Web services in order to enrich RDF knowledge bases on the fly whenever the knowledge base does not suffice to answer a user query.