WSN-IoT Forecast: Wireless Sensor Network Throughput Prediction Framework in Multimedia Internet of Things
Accurate throughput predictions can significantly improve the quality of experience (QoE), where QoE denotes a network’s capacity to provide satisfactory service. By increasing the results of good throughput predictions, the best strategy can be planned for managing data transmission networks with...
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| Format: | Article |
| Language: | English |
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ITB Journal Publisher
2023-12-01
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| Series: | Journal of ICT Research and Applications |
| Subjects: | |
| Online Access: | https://journals.itb.ac.id/index.php/jictra/article/view/20248 |
| _version_ | 1797336328999796736 |
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| author | Rosa Eliviani Yoanes Bandung |
| author_facet | Rosa Eliviani Yoanes Bandung |
| author_sort | Rosa Eliviani |
| collection | DOAJ |
| description |
Accurate throughput predictions can significantly improve the quality of experience (QoE), where QoE denotes a network’s capacity to provide satisfactory service. By increasing the results of good throughput predictions, the best strategy can be planned for managing data transmission networks with the aim of better and faster data transmission, thereby increasing QoE. Consequently, this paper investigates how to predict the throughput of wireless sensor networks utilizing multimedia data. First, we conducted a comparative analysis of relevant prior research on the topic of throughput prediction in Multimedia Internet of Things (Multimedia IoT). We developed a throughput prediction framework for wireless sensor networks based on what we learned from these studies using machine learning. The Throughput Prediction Framework identifies historical throughput data and employs these traits to predict throughput. In the final phase, multiple camera nodes and local servers are utilized to test a framework for throughput prediction. Our analysis demonstrates that WSN-IoT predictions are quite precise. For a 1-second time breakdown, the average absolute percentage error for all investigated scenarios ranges from 1 to 8 percent.
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| first_indexed | 2024-03-08T08:52:44Z |
| format | Article |
| id | doaj.art-e0b197691a5a4aa5a685184f6fb38b5b |
| institution | Directory Open Access Journal |
| issn | 2337-5787 2338-5499 |
| language | English |
| last_indexed | 2024-03-08T08:52:44Z |
| publishDate | 2023-12-01 |
| publisher | ITB Journal Publisher |
| record_format | Article |
| series | Journal of ICT Research and Applications |
| spelling | doaj.art-e0b197691a5a4aa5a685184f6fb38b5b2024-02-01T08:17:01ZengITB Journal PublisherJournal of ICT Research and Applications2337-57872338-54992023-12-0117310.5614/itbj.ict.res.appl.2023.17.3.4WSN-IoT Forecast: Wireless Sensor Network Throughput Prediction Framework in Multimedia Internet of ThingsRosa Eliviani0Yoanes Bandung1School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Jalan Ganesha No. 10, Bandung 40132, Indonesia School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Jalan Ganesha No. 10, Bandung 40132, Indonesia Accurate throughput predictions can significantly improve the quality of experience (QoE), where QoE denotes a network’s capacity to provide satisfactory service. By increasing the results of good throughput predictions, the best strategy can be planned for managing data transmission networks with the aim of better and faster data transmission, thereby increasing QoE. Consequently, this paper investigates how to predict the throughput of wireless sensor networks utilizing multimedia data. First, we conducted a comparative analysis of relevant prior research on the topic of throughput prediction in Multimedia Internet of Things (Multimedia IoT). We developed a throughput prediction framework for wireless sensor networks based on what we learned from these studies using machine learning. The Throughput Prediction Framework identifies historical throughput data and employs these traits to predict throughput. In the final phase, multiple camera nodes and local servers are utilized to test a framework for throughput prediction. Our analysis demonstrates that WSN-IoT predictions are quite precise. For a 1-second time breakdown, the average absolute percentage error for all investigated scenarios ranges from 1 to 8 percent. https://journals.itb.ac.id/index.php/jictra/article/view/20248frameworkInternet of Things (IoT)multimediathroughput predictionwireless sensor network |
| spellingShingle | Rosa Eliviani Yoanes Bandung WSN-IoT Forecast: Wireless Sensor Network Throughput Prediction Framework in Multimedia Internet of Things Journal of ICT Research and Applications framework Internet of Things (IoT) multimedia throughput prediction wireless sensor network |
| title | WSN-IoT Forecast: Wireless Sensor Network Throughput Prediction Framework in Multimedia Internet of Things |
| title_full | WSN-IoT Forecast: Wireless Sensor Network Throughput Prediction Framework in Multimedia Internet of Things |
| title_fullStr | WSN-IoT Forecast: Wireless Sensor Network Throughput Prediction Framework in Multimedia Internet of Things |
| title_full_unstemmed | WSN-IoT Forecast: Wireless Sensor Network Throughput Prediction Framework in Multimedia Internet of Things |
| title_short | WSN-IoT Forecast: Wireless Sensor Network Throughput Prediction Framework in Multimedia Internet of Things |
| title_sort | wsn iot forecast wireless sensor network throughput prediction framework in multimedia internet of things |
| topic | framework Internet of Things (IoT) multimedia throughput prediction wireless sensor network |
| url | https://journals.itb.ac.id/index.php/jictra/article/view/20248 |
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