Limited resources and harsh deployment environments may cause raw observations collected by sensor nodes to have poor data quality and reliability, which will influence the accuracy of the analysis and decision making in wireless sensor networks (WSNs). Therefore, anomaly detection must be implemented on the data collected by nodes. Support vector data description based on spatiotemporal and attribute correlations (STASVDD) can efficiently detect outliers. A novel optimization method based on STASVDD (N-STAS-VDD) is put forward in this paper. The proposed method considers that outliers can independently occur in each attribute when the collected data vectors are independent and identically distributed in WSNs. The proposed method applies the concept of core-sets to reduce the computational complexity of the quadratic programming problem in STAS-VDD, consequently reducing the energy consumption of resources-constrained WSNs. In addition, comparing the distributed and centralized detection approach of this method, the results show that the distributed approach has better performance because it relieves the communication burden. Extensive experiments were performed on both synthetic and real WSNs datasets. Results revealed that N-STASVDD achieves low time complexity and high detection accuracy.
Services such as searching, transforming and sharing digital educational resources can be effectively realized through metadata. With the rapid development of internet, digital educational resources have the features of wide distribution, large amount, diverse platform and different standards. Therefore, internet users are supposed to switch or map the metadata of different standards or patterns, i. e. to realize a kind of metadata interoperation, the main technological methods of which are metadata switching, OAf, metadata reuse and metadata norm control, etc. However, all these methods defects in staying on a specific or abstract grammatical level. A new method, ontology-based metadata interoperation model, can take corresponding measures from semantic and grammatical perspective respectively to successfully solve the problem of interoperation on metadata semantic level.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.