Abstract
Aiming at enhancing the communication and information security between the next generation of Industrial Internet of Things (Nx-IIoT) sensor networks, it is critical to aggregate heterogeneous sensor data in the sensor ontologies by establishing semantic connections in diverse sensor ontologies. Sensor ontology matching technology is devoted to determining heterogeneous sensor concept pairs in two distinct sensor ontologies, which is an effective method of addressing the heterogeneity problem. The existing matching techniques neglect the relationships among different entity mapping, which makes them unable to make sure of the alignment’s high quality. To get rid of this shortcoming, in this work, a sensor ontology extraction method technology using Fuzzy Debate Mechanism (FDM) is proposed to aggregate the heterogeneous sensor data, which determines the final sensor concept correspondences by carrying out a debating process among different matchers. More than ever, a fuzzy similarity metric is presented to effectively measure two entities’ similarity values by membership function. It first uses the fuzzy membership function to model two entities’ similarity in vector space and then calculate their semantic distance with the cosine function. The testing cases from Bibliographic data which is furnished by the Ontology Alignment Evaluation Initiative (OAEI) and six sensor ontology matching tasks are used to evaluate the performance of our scheme in the experiment. The robustness and effectiveness of the proposed method are proved by comparing it with the advanced ontology matching techniques.
Highlights
In the research era of the generation of Industrial Internet of ings (Nx-IIoT), the network technology and intelligent computing has become a huge technical model for the government to establish a smart world [1, 2]
To advance the relevant work, we propose a mechanism for sensor ontology matching with the Fuzzy Debate Mechanism (FDM) based ontology alignment extraction technique, which aims to extract the correct sensor ontology matching pairs in different alignments generated by different basic matching measures
There are two technical routes: ontology meta-matching (OMM) techniques and ontology entity matching techniques. e ontology entity matching techniques try to determine the entity correspondence set between two ontologies directly, while the OMM techniques try to solve the problem of aggregate different similarity measures with appropriate weights [15]. ere are plenty of popular technical approaches in computing intelligence (CI) to solve OMM problems, e.g., machine learning (ML), evolutionary computing (EC), and swarm intelligence (SI)
Summary
In the research era of the generation of Industrial Internet of ings (Nx-IIoT), the network technology and intelligent computing has become a huge technical model for the government to establish a smart world [1, 2]. Xiong et al [3] proposed a LightPrivacy scheme to achieve the tradeoff between user’s personalization privacy protection and the availability of task data in mobile group awareness, whose computational efficiency was significantly improved Later, they further presented an ATG framework, which was both effective and efficient, and suitable for IoT Mobile Edge Crowd Sensing (MECS) [4]. Lin et al [5] proposed an Ant Colony Optimization (ACO) approach to protect information by the transaction deletion, which was able to reduce the side effects while keeping the overall computing cost low In this fashion, a large number of physical objects embedded with sensors devices exchange information through heterogeneous networks in various applications such as the smart grid, electronic medical. One of the cutting-edge research institutions in this field is Ontology Alignment
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