Open Access
Research Article
Semantic Web of Things for pollution measurement and validation interoperability using AI Techniques
Nikita MalikUniversity School of Information, Communication & Technology Guru Gobind Singh Indraprastha University; Department of Computer Applications Maharaja Surajmal InstituteUniversity School of Information, Communication & Technology Guru Gobind Singh Indraprastha UniversityDelhi, IndiaView full profile → , Sanjay Kumar Malikskmalik@ipu.ac.inUniversity School of Information, Communication & Technology Guru Gobind Singh Indraprastha UniversityUniversity School of Information, Communication and Technology Guru Gobind Singh Indraprastha UniversityDwarka, Delhi, 110078, IndiaView full profile → , *Vanita JainCorresponding authorvanitajain@fot.du.ac.inDepartment of Electronic Science University of DelhiDepartment of Electronics & Communication Engineering Faculty of Technology University of Delhi Maharishi Kanad BhawanDelhi, 110007, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 08 Nov 2023
- Published Online:
- 27 Apr 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1517
- Pages:
- 765–784
Abstract
In response to the growing IoT device diversity, efforts are underway to better integrate data, applications, and services. The Semantic Web, known for its simplicity in integration, has the potential to improve data interpretation and interoperability. In this research, a pollution management model is used, combining the Semantic Web of Things (SWoT) and Artificial Intelligence (AI), to create smarter cities, providing real-time environmental information. The dataset has been sourced from Aarhus City, Denmark, and the study outlines Semantic Web Technologies (SWTs) in IoT frameworks, including common ontologies for IoT-based architecture. The dataset’s relationship between various gases/pollutants is analyzed using correlation matrix. Machine learning methods like Multi-Layer Perceptron (MLP) with Sigmoid, ReLU, Tanh, Maxout, Swish hybrid activation functions are employed, with results assessed using Root Mean Squared Error (RMSE) and Mean Squared Error (MSE). A comparison of errors for different activation functions is also performed and the findings reveal good results when comparing actual and predicted values in the proposed model.
Keywords
Subject Classifications
(2010) 68T30
References
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