Managing Energy Consumption of Devices with Multiconnectivity by Deep Learning and Software-Defined Networking
Multiconnectivity allows user equipment/devices to connect to multiple radio access technologies simultaneously, including 5G, 4G (LTE), and WiFi. It is a necessity in meeting the increasing demand for mobile network services for the 5G and beyond wireless networks, while ensuring that mobile operat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/18/7699 |
| _version_ | 1797577129619095552 |
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| author | Ramiza Shams Atef Abdrabou Mohammad Al Bataineh Kamarul Ariffin Noordin |
| author_facet | Ramiza Shams Atef Abdrabou Mohammad Al Bataineh Kamarul Ariffin Noordin |
| author_sort | Ramiza Shams |
| collection | DOAJ |
| description | Multiconnectivity allows user equipment/devices to connect to multiple radio access technologies simultaneously, including 5G, 4G (LTE), and WiFi. It is a necessity in meeting the increasing demand for mobile network services for the 5G and beyond wireless networks, while ensuring that mobile operators can still reap the benefits of their present investments. Multipath TCP (MPTCP) has been introduced to allow uninterrupted reliable data transmission over multiconnectivity links. However, energy consumption is a significant issue for multihomed wireless devices since most of them are battery-powered. This paper employs software-defined networking (SDN) and deep neural networks (DNNs) to manage the energy consumption of devices with multiconnectivity running MPTCP. The proposed method involves two lightweight algorithms implemented on an SDN controller, using a real hardware testbed of dual-homed wireless nodes connected to WiFi and cellular networks. The first algorithm determines whether a node should connect to a specific network or both networks. The second algorithm improves the selection made by the first by using a DNN trained on different scenarios, such as various network sizes and MPTCP congestion control algorithms. The results of our extensive experimentation show that this approach effectively reduces energy consumption while providing better network throughput performance compared to using single-path TCP or MPTCP Cubic or BALIA for all nodes. |
| first_indexed | 2024-03-10T22:03:38Z |
| format | Article |
| id | doaj.art-b997f262bfac468aaa2e767b09d4e1a0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T22:03:38Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b997f262bfac468aaa2e767b09d4e1a02023-11-19T12:52:59ZengMDPI AGSensors1424-82202023-09-012318769910.3390/s23187699Managing Energy Consumption of Devices with Multiconnectivity by Deep Learning and Software-Defined NetworkingRamiza Shams0Atef Abdrabou1Mohammad Al Bataineh2Kamarul Ariffin Noordin3Department of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, Al-Ain P.O. Box 15551, Abu Dhabi, United Arab EmiratesDepartment of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, Al-Ain P.O. Box 15551, Abu Dhabi, United Arab EmiratesDepartment of Electrical and Communication Engineering, College of Engineering, United Arab Emirates University, Al-Ain P.O. Box 15551, Abu Dhabi, United Arab EmiratesDepartment of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, MalaysiaMulticonnectivity allows user equipment/devices to connect to multiple radio access technologies simultaneously, including 5G, 4G (LTE), and WiFi. It is a necessity in meeting the increasing demand for mobile network services for the 5G and beyond wireless networks, while ensuring that mobile operators can still reap the benefits of their present investments. Multipath TCP (MPTCP) has been introduced to allow uninterrupted reliable data transmission over multiconnectivity links. However, energy consumption is a significant issue for multihomed wireless devices since most of them are battery-powered. This paper employs software-defined networking (SDN) and deep neural networks (DNNs) to manage the energy consumption of devices with multiconnectivity running MPTCP. The proposed method involves two lightweight algorithms implemented on an SDN controller, using a real hardware testbed of dual-homed wireless nodes connected to WiFi and cellular networks. The first algorithm determines whether a node should connect to a specific network or both networks. The second algorithm improves the selection made by the first by using a DNN trained on different scenarios, such as various network sizes and MPTCP congestion control algorithms. The results of our extensive experimentation show that this approach effectively reduces energy consumption while providing better network throughput performance compared to using single-path TCP or MPTCP Cubic or BALIA for all nodes.https://www.mdpi.com/1424-8220/23/18/7699multipath TCPsoftware-defined networkingenergy consumptionneural networkscongestion controlwireless |
| spellingShingle | Ramiza Shams Atef Abdrabou Mohammad Al Bataineh Kamarul Ariffin Noordin Managing Energy Consumption of Devices with Multiconnectivity by Deep Learning and Software-Defined Networking Sensors multipath TCP software-defined networking energy consumption neural networks congestion control wireless |
| title | Managing Energy Consumption of Devices with Multiconnectivity by Deep Learning and Software-Defined Networking |
| title_full | Managing Energy Consumption of Devices with Multiconnectivity by Deep Learning and Software-Defined Networking |
| title_fullStr | Managing Energy Consumption of Devices with Multiconnectivity by Deep Learning and Software-Defined Networking |
| title_full_unstemmed | Managing Energy Consumption of Devices with Multiconnectivity by Deep Learning and Software-Defined Networking |
| title_short | Managing Energy Consumption of Devices with Multiconnectivity by Deep Learning and Software-Defined Networking |
| title_sort | managing energy consumption of devices with multiconnectivity by deep learning and software defined networking |
| topic | multipath TCP software-defined networking energy consumption neural networks congestion control wireless |
| url | https://www.mdpi.com/1424-8220/23/18/7699 |
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