Exploring Performance Trade-offs in Tactical Edge Networks

Sridharan, Vignesh; Motani, Mehul; Jalaian, Brian; Suri, Niranjan

doi:10.1109/mcom.001.2000303

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…Since developing new VANET protocols has proven to been hard [5], work has been done to overcome these challenges with the incorporation of Delay-Tolerant Networking (DTN) [6], Software-Defined Networking (SDN) [7] and Machine Learning technologies [8]. These technologies utilise various DTN protocols like Spray and Wait [9], [10], Probability Based Spray and Wait (PBSW) [11], Epidemic [12], Probability Routing Protocol using History of Encounters and Transitivity (PRoPHET) [13], [14], PRoPHETv2 [15], [16], MaxProp [17] and Rapid Adaptive Routing Protocol for Intermittently Connected Delay Tolerant Networks (RAPID) [18] in an ad-hoc manner in order to reliably transmit information from source to destination. However, these protocols also have limitations and thus only work very well in specific use cases such as if the movement of the communicating nodes is predictable or if the network resources required to buffer the transmitted data are sufficient [19].…”

Section: Introductionmentioning

confidence: 99%

Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks

Nakayima,

Soliman,

Ueda

et al. 2024

IEEE Open J. Veh. Technol.

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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.

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