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Knowledge-Graph-Based IoTs Entity Discovery Middleware for Nonsmart Sensor
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AI
DOI：10.1109/TII.2023.3292540.png)
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Abstract
En 中文
Internet-of-Things (IoTs) entity discovery plays an important role in the Industrial IoTs, especially with the rapidly increasing and updating of IoT sensors in the industrial environment driven by the era of Industry 4.0 and intelligent manufacturing. However, large numbers of nonsmart sensors are required in the industrial environment, causing IoT entity discovery challenges. Unlike the smart sensor, the nonsmart sensor with limited computation and communication ability is hard to discover and recognize by traditional IoT platforms. Aiming at the challenge, this work proposes a novel IoT entity discovery middleware for nonsmart sensor discovery in the industrial environment. The proposed middleware combines both sensor knowledge graphs and sensor data values to build an IoT entity discovery and recognition model. A knowledge-data fused learning network is proposed for the model to identify the data type, function, and other information of the nonsmart sensor. At last, a prototype middleware with the discovery and recognition model is produced to implement nonsmart sensor discovery. In the experimental evaluations, the prototype middleware tests various nonsmart sensors and achieves 87.6% recognition accuracy. In real-world case studies, the prototype middleware proves the feasibility and effectiveness of nonsmart sensor discovery in the industrial environment.
Keywords:
Internet of Things
Knowledge graphs
Middleware
Ontologies
Logic gates
Feature extraction
Data models
Internet of Things (IoTs)
IoTs entity discovery
IoTs middleware
knowledge graph
machine learning
nonsmart sensor
representation learning
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