A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things
Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is us...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
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Hindawi
2022
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| Online Access: | https://eprints.ums.edu.my/id/eprint/33312/1/A%20Neighborhood%20and%20Machine%20Learning-Enabled%20Information%20Fusion%20Approach%20for%20the%20WSNs%20and%20Internet%20of%20Medical%20Things.pdf https://eprints.ums.edu.my/id/eprint/33312/2/A%20Neighborhood%20and%20Machine%20Learning-Enabled%20Information%20Fusion%20Approach%20for%20the%20WSNs%20and%20Internet%20of%20Medical%20Things1.pdf |
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| author | Zard Ali Khan Sheneela Naz Rahim khan Jason Teo Abdullah Ghani Mohammed Amin Almaiah |
| author_facet | Zard Ali Khan Sheneela Naz Rahim khan Jason Teo Abdullah Ghani Mohammed Amin Almaiah |
| author_sort | Zard Ali Khan |
| collection | UMS |
| description | Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of non-neighbor is not compared at all. 'ese algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches. |
| first_indexed | 2024-03-06T03:17:59Z |
| format | Article |
| id | ums.eprints-33312 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:17:59Z |
| publishDate | 2022 |
| publisher | Hindawi |
| record_format | dspace |
| spelling | ums.eprints-333122022-07-17T02:01:18Z https://eprints.ums.edu.my/id/eprint/33312/ A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things Zard Ali Khan Sheneela Naz Rahim khan Jason Teo Abdullah Ghani Mohammed Amin Almaiah TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of non-neighbor is not compared at all. 'ese algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches. Hindawi 2022 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/33312/1/A%20Neighborhood%20and%20Machine%20Learning-Enabled%20Information%20Fusion%20Approach%20for%20the%20WSNs%20and%20Internet%20of%20Medical%20Things.pdf text en https://eprints.ums.edu.my/id/eprint/33312/2/A%20Neighborhood%20and%20Machine%20Learning-Enabled%20Information%20Fusion%20Approach%20for%20the%20WSNs%20and%20Internet%20of%20Medical%20Things1.pdf Zard Ali Khan and Sheneela Naz and Rahim khan and Jason Teo and Abdullah Ghani and Mohammed Amin Almaiah (2022) A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things. Computational Intelligence and Neuroscience, 2022 (511237). pp. 1-14. ISSN 1687-5265 https://www.hindawi.com/journals/cin/2022/5112375/ https://doi.org/10.1155/2022/5112375 https://doi.org/10.1155/2022/5112375 |
| spellingShingle | TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Zard Ali Khan Sheneela Naz Rahim khan Jason Teo Abdullah Ghani Mohammed Amin Almaiah A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things |
| title | A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things |
| title_full | A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things |
| title_fullStr | A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things |
| title_full_unstemmed | A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things |
| title_short | A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things |
| title_sort | neighborhood and machine learning enabled information fusion approach for the wsns and internet of medical things |
| topic | TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television |
| url | https://eprints.ums.edu.my/id/eprint/33312/1/A%20Neighborhood%20and%20Machine%20Learning-Enabled%20Information%20Fusion%20Approach%20for%20the%20WSNs%20and%20Internet%20of%20Medical%20Things.pdf https://eprints.ums.edu.my/id/eprint/33312/2/A%20Neighborhood%20and%20Machine%20Learning-Enabled%20Information%20Fusion%20Approach%20for%20the%20WSNs%20and%20Internet%20of%20Medical%20Things1.pdf |
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