Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks
Ensuring reliable data transmission in all Vehicular Ad-hoc Network (VANET) segments is paramount in modern vehicular communications. Vehicular operations face unpredictable network conditions which affect routing protocol adaptiveness. Several solutions have addressed those challenges, but each has...
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10518068/ |
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| author | Olivia Nakayima Mostafa I. Soliman Kazunori Ueda Samir A. Elsagheer Mohamed |
| author_facet | Olivia Nakayima Mostafa I. Soliman Kazunori Ueda Samir A. Elsagheer Mohamed |
| author_sort | Olivia Nakayima |
| collection | DOAJ |
| description | Ensuring reliable data transmission in all Vehicular Ad-hoc Network (VANET) segments is paramount in modern vehicular communications. Vehicular operations face unpredictable network conditions which affect routing protocol adaptiveness. Several solutions have addressed those challenges, but each has noted shortcomings. This work proposes a centralised-controller multi-agent (CCMA) algorithm based on Software-Defined Networking (SDN) and Delay-Tolerant Networking (DTN) principles, to enhance VANET performance using Reinforcement Learning (RL). This algorithm is trained and validated with a simulation environment modelling the network nodes, routing protocols and buffer schedules. It optimally deploys DTN routing protocols (Spray and Wait, Epidemic, and PRoPHETv2) and buffer schedules (Random, Defer, Earliest Deadline First, First In First Out, Large/smallest bundle first) based on network state information (that is; traffic pattern, buffer size variance, node and link uptime, bundle Time To Live (TTL), link loss and capacity). These are implemented in three environment types; Advanced Technological Regions, Limited Resource Regions and Opportunistic Communication Regions. The study assesses the performance of the multi-protocol approach using metrics: TTL, buffer management,link quality, delivery ratio, Latency and overhead scores for optimal network performance. Comparative analysis with single-protocol VANETs (simulated using the Opportunistic Network Environment (ONE)), demonstrate an improved performance of the proposed algorithm in all VANET scenarios. |
| format | Article |
| id | doaj-art-c87e04b5d6a04a0e8b04194d7ab56217 |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-c87e04b5d6a04a0e8b04194d7ab562172025-01-30T00:04:32ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302024-01-01572173610.1109/OJVT.2024.339663710518068Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular NetworksOlivia Nakayima0https://orcid.org/0009-0008-8378-7369Mostafa I. Soliman1https://orcid.org/0000-0002-4386-8235Kazunori Ueda2https://orcid.org/0000-0002-3424-1844Samir A. Elsagheer Mohamed3https://orcid.org/0000-0003-4388-1998Department of Computer Science and Engineering, Egypt–Japan University of Science and Technology, New Borg El-Arab City, EgyptDepartment of Computer Science and Engineering, Egypt–Japan University of Science and Technology, New Borg El-Arab City, EgyptDepartment of Computer Science and Engineering, Waseda University, Tokyo, JapanDepartment of Computer Science and Engineering, Egypt–Japan University of Science and Technology, New Borg El-Arab City, EgyptEnsuring reliable data transmission in all Vehicular Ad-hoc Network (VANET) segments is paramount in modern vehicular communications. Vehicular operations face unpredictable network conditions which affect routing protocol adaptiveness. Several solutions have addressed those challenges, but each has noted shortcomings. This work proposes a centralised-controller multi-agent (CCMA) algorithm based on Software-Defined Networking (SDN) and Delay-Tolerant Networking (DTN) principles, to enhance VANET performance using Reinforcement Learning (RL). This algorithm is trained and validated with a simulation environment modelling the network nodes, routing protocols and buffer schedules. It optimally deploys DTN routing protocols (Spray and Wait, Epidemic, and PRoPHETv2) and buffer schedules (Random, Defer, Earliest Deadline First, First In First Out, Large/smallest bundle first) based on network state information (that is; traffic pattern, buffer size variance, node and link uptime, bundle Time To Live (TTL), link loss and capacity). These are implemented in three environment types; Advanced Technological Regions, Limited Resource Regions and Opportunistic Communication Regions. The study assesses the performance of the multi-protocol approach using metrics: TTL, buffer management,link quality, delivery ratio, Latency and overhead scores for optimal network performance. Comparative analysis with single-protocol VANETs (simulated using the Opportunistic Network Environment (ONE)), demonstrate an improved performance of the proposed algorithm in all VANET scenarios.https://ieeexplore.ieee.org/document/10518068/Delay-tolerant networksperformance analysisreinforcement learningsimulatorsoftware-defined networkingvehicular ad-hoc networks |
| spellingShingle | Olivia Nakayima Mostafa I. Soliman Kazunori Ueda Samir A. Elsagheer Mohamed Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks IEEE Open Journal of Vehicular Technology Delay-tolerant networks performance analysis reinforcement learning simulator software-defined networking vehicular ad-hoc networks |
| title | Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks |
| title_full | Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks |
| title_fullStr | Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks |
| title_full_unstemmed | Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks |
| title_short | Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks |
| title_sort | combining software defined and delay tolerant networking concepts with deep reinforcement learning technology to enhance vehicular networks |
| topic | Delay-tolerant networks performance analysis reinforcement learning simulator software-defined networking vehicular ad-hoc networks |
| url | https://ieeexplore.ieee.org/document/10518068/ |
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