Machine learning for QoS and security enhancement of RPL in IoT-Enabled wireless sensors
Internet of Things (IoT) networks rely on wireless sensors for data collection and transmission, making them vulnerable to security threats that undermine their Quality of Service (QoS). The Routing Protocol for Low-Power and Lossy Networks (RPL) is crucial for efficient data transmission in IoT net...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | Sensors International |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666351124000111 |
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| author | Abubakar Wakili Sara Bakkali Ahmed El Hilali Alaoui |
| author_facet | Abubakar Wakili Sara Bakkali Ahmed El Hilali Alaoui |
| author_sort | Abubakar Wakili |
| collection | DOAJ |
| description | Internet of Things (IoT) networks rely on wireless sensors for data collection and transmission, making them vulnerable to security threats that undermine their Quality of Service (QoS). The Routing Protocol for Low-Power and Lossy Networks (RPL) is crucial for efficient data transmission in IoT networks, but its performance can be significantly degraded by attacks such as Rank, Sinkhole and Wormhole attacks. These threats disrupt network integrity by manipulating routing information, attracting traffic through malicious nodes and tunneling data to malicious endpoints. This paper presents a novel machine learning-based framework to enhance RPL's security and QoS. Our approach integrates a random forest model for precise traffic classification, a reinforcement learning module for dynamic and adaptive routing, and a modified RPL objective function that incorporates classification outcomes into routing decisions. Simulations demonstrate that our framework significantly improves network throughput, reduces latency, and enhances packet delivery ratios while maintaining low jitter. Furthermore, it achieves a high detection rate, minimal false positives, and swift response to security incidents, thereby robustly securing the RPL protocol and enhancing QoS in IoT-enabled wireless sensor networks. The findings of this research offer substantial contributions to the field, providing a comprehensive solution to strengthen RPL against prevalent security threats. |
| format | Article |
| id | doaj-art-446e137890ea420c99b4190a391b5d38 |
| institution | Kabale University |
| issn | 2666-3511 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Sensors International |
| spelling | doaj-art-446e137890ea420c99b4190a391b5d382025-01-04T04:57:10ZengKeAi Communications Co., Ltd.Sensors International2666-35112024-01-015100289Machine learning for QoS and security enhancement of RPL in IoT-Enabled wireless sensorsAbubakar Wakili0Sara Bakkali1Ahmed El Hilali Alaoui2Corresponding author.; Euromed University of Fez, UEMF, MoroccoEuromed University of Fez, UEMF, MoroccoEuromed University of Fez, UEMF, MoroccoInternet of Things (IoT) networks rely on wireless sensors for data collection and transmission, making them vulnerable to security threats that undermine their Quality of Service (QoS). The Routing Protocol for Low-Power and Lossy Networks (RPL) is crucial for efficient data transmission in IoT networks, but its performance can be significantly degraded by attacks such as Rank, Sinkhole and Wormhole attacks. These threats disrupt network integrity by manipulating routing information, attracting traffic through malicious nodes and tunneling data to malicious endpoints. This paper presents a novel machine learning-based framework to enhance RPL's security and QoS. Our approach integrates a random forest model for precise traffic classification, a reinforcement learning module for dynamic and adaptive routing, and a modified RPL objective function that incorporates classification outcomes into routing decisions. Simulations demonstrate that our framework significantly improves network throughput, reduces latency, and enhances packet delivery ratios while maintaining low jitter. Furthermore, it achieves a high detection rate, minimal false positives, and swift response to security incidents, thereby robustly securing the RPL protocol and enhancing QoS in IoT-enabled wireless sensor networks. The findings of this research offer substantial contributions to the field, providing a comprehensive solution to strengthen RPL against prevalent security threats.http://www.sciencedirect.com/science/article/pii/S2666351124000111RPLIoTWireless sensor networksMachine learningQoSSecurity |
| spellingShingle | Abubakar Wakili Sara Bakkali Ahmed El Hilali Alaoui Machine learning for QoS and security enhancement of RPL in IoT-Enabled wireless sensors Sensors International RPL IoT Wireless sensor networks Machine learning QoS Security |
| title | Machine learning for QoS and security enhancement of RPL in IoT-Enabled wireless sensors |
| title_full | Machine learning for QoS and security enhancement of RPL in IoT-Enabled wireless sensors |
| title_fullStr | Machine learning for QoS and security enhancement of RPL in IoT-Enabled wireless sensors |
| title_full_unstemmed | Machine learning for QoS and security enhancement of RPL in IoT-Enabled wireless sensors |
| title_short | Machine learning for QoS and security enhancement of RPL in IoT-Enabled wireless sensors |
| title_sort | machine learning for qos and security enhancement of rpl in iot enabled wireless sensors |
| topic | RPL IoT Wireless sensor networks Machine learning QoS Security |
| url | http://www.sciencedirect.com/science/article/pii/S2666351124000111 |
| work_keys_str_mv | AT abubakarwakili machinelearningforqosandsecurityenhancementofrpliniotenabledwirelesssensors AT sarabakkali machinelearningforqosandsecurityenhancementofrpliniotenabledwirelesssensors AT ahmedelhilalialaoui machinelearningforqosandsecurityenhancementofrpliniotenabledwirelesssensors |