DMLAR: Distributed Machine Learning-Based Anti-Collision Algorithm for RFID Readers in the Internet of Things
Radio Frequency Identification (RFID) is considered as one of the most widely used wireless identification technologies in the Internet of Things. Many application areas require a dense RFID network for efficient deployment and coverage, which causes interference between RFID tags and readers, and r...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/11/7/107 |
| _version_ | 1797406910959321088 |
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| author | Rachid Mafamane Mourad Ouadou Hajar Sahbani Nisrine Ibadah Khalid Minaoui |
| author_facet | Rachid Mafamane Mourad Ouadou Hajar Sahbani Nisrine Ibadah Khalid Minaoui |
| author_sort | Rachid Mafamane |
| collection | DOAJ |
| description | Radio Frequency Identification (RFID) is considered as one of the most widely used wireless identification technologies in the Internet of Things. Many application areas require a dense RFID network for efficient deployment and coverage, which causes interference between RFID tags and readers, and reduces the performance of the RFID system. Therefore, communication resource management is required to avoid such problems. In this paper, we propose an anti-collision protocol based on feed-forward Artificial Neural Network methodology for distributed learning between RFID readers to predict collisions and ensure efficient resource allocation (DMLAR) by considering the mobility of tags and readers. The evaluation of our anti-collision protocol is performed for different mobility scenarios in healthcare where the collected data are critical and must respect the terms of throughput, delay, overload, integrity and energy. The dataset created and distributed by the readers allows an efficient learning process and, therefore, a high collision detection to increase throughput and minimize data loss. In the application phase, the readers do not need to exchange control packets with each other to control the resource allocation, which avoids network overload and communication delay. Simulation results show the robustness and effectiveness of the anti-collision protocol by the number of readers and resources used. The model used allows a large number of readers to use the most suitable frequency and time resources for simultaneous and successful tag interrogation. |
| first_indexed | 2024-03-09T03:33:34Z |
| format | Article |
| id | doaj.art-7b1ac3bc805e4de583cf3918729f0cec |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-09T03:33:34Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-7b1ac3bc805e4de583cf3918729f0cec2023-12-03T14:51:56ZengMDPI AGComputers2073-431X2022-06-0111710710.3390/computers11070107DMLAR: Distributed Machine Learning-Based Anti-Collision Algorithm for RFID Readers in the Internet of ThingsRachid Mafamane0Mourad Ouadou1Hajar Sahbani2Nisrine Ibadah3Khalid Minaoui4LRIT Laboratory, Faculty of Science, Mohammed V University, Rabat 1014, MoroccoLRIT Laboratory, Faculty of Science, Mohammed V University, Rabat 1014, MoroccoLRIT Laboratory, Faculty of Science, Mohammed V University, Rabat 1014, MoroccoLRIT Laboratory, Faculty of Science, Mohammed V University, Rabat 1014, MoroccoLRIT Laboratory, Faculty of Science, Mohammed V University, Rabat 1014, MoroccoRadio Frequency Identification (RFID) is considered as one of the most widely used wireless identification technologies in the Internet of Things. Many application areas require a dense RFID network for efficient deployment and coverage, which causes interference between RFID tags and readers, and reduces the performance of the RFID system. Therefore, communication resource management is required to avoid such problems. In this paper, we propose an anti-collision protocol based on feed-forward Artificial Neural Network methodology for distributed learning between RFID readers to predict collisions and ensure efficient resource allocation (DMLAR) by considering the mobility of tags and readers. The evaluation of our anti-collision protocol is performed for different mobility scenarios in healthcare where the collected data are critical and must respect the terms of throughput, delay, overload, integrity and energy. The dataset created and distributed by the readers allows an efficient learning process and, therefore, a high collision detection to increase throughput and minimize data loss. In the application phase, the readers do not need to exchange control packets with each other to control the resource allocation, which avoids network overload and communication delay. Simulation results show the robustness and effectiveness of the anti-collision protocol by the number of readers and resources used. The model used allows a large number of readers to use the most suitable frequency and time resources for simultaneous and successful tag interrogation.https://www.mdpi.com/2073-431X/11/7/107RFIDIoTmachine learningcollisionMAC layerwireless sensor network |
| spellingShingle | Rachid Mafamane Mourad Ouadou Hajar Sahbani Nisrine Ibadah Khalid Minaoui DMLAR: Distributed Machine Learning-Based Anti-Collision Algorithm for RFID Readers in the Internet of Things Computers RFID IoT machine learning collision MAC layer wireless sensor network |
| title | DMLAR: Distributed Machine Learning-Based Anti-Collision Algorithm for RFID Readers in the Internet of Things |
| title_full | DMLAR: Distributed Machine Learning-Based Anti-Collision Algorithm for RFID Readers in the Internet of Things |
| title_fullStr | DMLAR: Distributed Machine Learning-Based Anti-Collision Algorithm for RFID Readers in the Internet of Things |
| title_full_unstemmed | DMLAR: Distributed Machine Learning-Based Anti-Collision Algorithm for RFID Readers in the Internet of Things |
| title_short | DMLAR: Distributed Machine Learning-Based Anti-Collision Algorithm for RFID Readers in the Internet of Things |
| title_sort | dmlar distributed machine learning based anti collision algorithm for rfid readers in the internet of things |
| topic | RFID IoT machine learning collision MAC layer wireless sensor network |
| url | https://www.mdpi.com/2073-431X/11/7/107 |
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