Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services
The Internet of Things (IoT) is a form of Internet-based distributed computing that allows devices and their services to interact and execute tasks for each other. Consequently, the footprint of the IoT is increasing and becoming more complex to the highest degree. This has also given birth to new I...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10224512/ |
| _version_ | 1827860248440864768 |
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| author | Mohammad Asad Abbasi Yen-Lin Chen Abdullah Ayub Khan Zulfiqar A. Memon Nouman M. Durrani Jing Yang Chin Soon Ku Lip Yee Por |
| author_facet | Mohammad Asad Abbasi Yen-Lin Chen Abdullah Ayub Khan Zulfiqar A. Memon Nouman M. Durrani Jing Yang Chin Soon Ku Lip Yee Por |
| author_sort | Mohammad Asad Abbasi |
| collection | DOAJ |
| description | The Internet of Things (IoT) is a form of Internet-based distributed computing that allows devices and their services to interact and execute tasks for each other. Consequently, the footprint of the IoT is increasing and becoming more complex to the highest degree. This has also given birth to new IoT-enabled applications and services. Efficient service interaction and management also call for understanding and analyzing the nature of IoT services. Further, IoT services must be characterized into various classes, and different service-related attributes must be considered for the classification. This article assesses the requirements of heterogeneous IoT services by examining their interactions. Principally, heterogeneous IoT and their service interactions are targeted. The research work performs classification of IoT services into various classes. Services are classified on the basis of various attributes. The attributes reflect different characteristics of the services. This research enables improved utilization of IoT services through efficient classification of available resources using machine learning methods. To demonstrate service classification applicability, the SVM, voting classifier, and decision tree have been applied in a service-oriented environment along with different types of services. All the services in the data set were analyzed and divided into five classes. Moreover, the decision tree performed well and achieved higher accuracy values in all classes. However, the overall prediction and classification of the decision tree model were observed to be good and satisfactorily high. |
| first_indexed | 2024-03-12T13:21:32Z |
| format | Article |
| id | doaj.art-8b9ad255d07b41b69038030cbd7ba167 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T13:21:32Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8b9ad255d07b41b69038030cbd7ba1672023-08-25T23:01:20ZengIEEEIEEE Access2169-35362023-01-0111890248903710.1109/ACCESS.2023.330660710224512Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT ServicesMohammad Asad Abbasi0Yen-Lin Chen1https://orcid.org/0000-0001-7717-9393Abdullah Ayub Khan2https://orcid.org/0000-0003-2838-7641Zulfiqar A. Memon3Nouman M. Durrani4https://orcid.org/0000-0001-6135-3924Jing Yang5Chin Soon Ku6https://orcid.org/0000-0003-0793-3308Lip Yee Por7https://orcid.org/0000-0001-5865-1533Department of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, PakistanDepartment of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, TaiwanDepartment of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences (NUCES)-FAST, Islamabad, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences (NUCES)-FAST, Islamabad, PakistanDepartment of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Computer Science, Universiti Tunku Abdul Rahman, Kampar, MalaysiaDepartment of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaThe Internet of Things (IoT) is a form of Internet-based distributed computing that allows devices and their services to interact and execute tasks for each other. Consequently, the footprint of the IoT is increasing and becoming more complex to the highest degree. This has also given birth to new IoT-enabled applications and services. Efficient service interaction and management also call for understanding and analyzing the nature of IoT services. Further, IoT services must be characterized into various classes, and different service-related attributes must be considered for the classification. This article assesses the requirements of heterogeneous IoT services by examining their interactions. Principally, heterogeneous IoT and their service interactions are targeted. The research work performs classification of IoT services into various classes. Services are classified on the basis of various attributes. The attributes reflect different characteristics of the services. This research enables improved utilization of IoT services through efficient classification of available resources using machine learning methods. To demonstrate service classification applicability, the SVM, voting classifier, and decision tree have been applied in a service-oriented environment along with different types of services. All the services in the data set were analyzed and divided into five classes. Moreover, the decision tree performed well and achieved higher accuracy values in all classes. However, the overall prediction and classification of the decision tree model were observed to be good and satisfactorily high.https://ieeexplore.ieee.org/document/10224512/Classificationheterogeneitydecision treeSVMservice-oriented environment |
| spellingShingle | Mohammad Asad Abbasi Yen-Lin Chen Abdullah Ayub Khan Zulfiqar A. Memon Nouman M. Durrani Jing Yang Chin Soon Ku Lip Yee Por Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services IEEE Access Classification heterogeneity decision tree SVM service-oriented environment |
| title | Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services |
| title_full | Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services |
| title_fullStr | Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services |
| title_full_unstemmed | Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services |
| title_short | Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services |
| title_sort | enabling iot service classification a machine learning based approach for handling classification issues in heterogeneous iot services |
| topic | Classification heterogeneity decision tree SVM service-oriented environment |
| url | https://ieeexplore.ieee.org/document/10224512/ |
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