The Design and Implementation of a Semantic-Based Proactive System for Raw Sensor Data: A Case Study for Laboratory Environments

Abstract

Abstract— In the last decade, raw sensor data from sensor-based systems, the area of use of which has increased considerably, pose a fundamentally new set of research challenges, including structuring, sharing, and management. Although many different academic studies have been conducted on the integration of sets of data emerging from different sensor-based systems until present, these studies have generally focused on the integration of data as syntax. Studies on the semantic integration of data are limited, and still, the area of the study mentioned have problems that await solutions. In this article; parameters (CO2, TVOC, CO, PM2.5, PM10, Temperature, Humidity, Light), affecting laboratory analysis results and threatening the analyst's health, were measured in laboratory environments selected as “use cases”, and semantic-based information management framework was created for different sensor-based systems. Classical machine learning methods, and regression approaches which are frequently used for such sensor data, have been applied to the proposed sensor ontology and it has been measured that machine learning algorithm performs better on ontological sensor data. The most efficient algorithms in terms of accuracy and time were selected, and integrated into the proposed proactive approach, in order to take the selected laboratory environment’s condition under control.

Keywords

• Sensor ontology
• Semantic sensor web
• Machine learning
• Prediction on stream data
• Supervised learning.

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