Research Article Open Access

Route Optimization using Hybrid GRU Learning Model for SDN and Edge-Based VANET Topology

Savitha Kumar1 and Chandrasekar Chinnasamy2
  • 1 Department of Computer Science, Government Arts College, Udumalpet, India
  • 2 Department of Computer Science, Government Arts and Science College for Women, Coimbatore, India


Intelligent Transportation System (ITS) offersoutstanding features, including security applications and emergency alerts.Unfortunately, ITS limits traffic control services, adaptability, andadjustability due to the traffic volume. Hence, expanding the standard VehicularAd hoc Networks (VANET) framework is a requirement. As a result, in the latestdays, the concept of Software-Defined Networking based Vehicular Ad hocNetworks (SDN-VANETs) has drawn considerable attention, creating VANETssmarter. The SDN-VANETs design is capable of addressing the aforementionedVANET issues. The integrated (analytically) SDN architecture is customizableand it also contains domain knowledge about the VANET architecture. Packetforwarding is a fundamental challenge in VANET wherein a router, as in the formof an RSU, determines the next hop of every signal in the pipeline to provideit to its recipient as fast as possible. Reinforcement Learning (RL) is used todevelop autonomous routing protocol rules; however, the limitation of RL'sdepth prevents it from representing more comprehensively dynamic networkconditions, restricting its true value. In this research, we present a VANETinfrastructure based on SDN + EDGE with a new Deep Reinforcement Learning (DRL)route optimization framework, "Gated Recurrent Reinforcement Learning(GRRL)" neural network, whereby each router has its hybrid GRU + FeedForward Neural Network (NN) for learning and making decisions in an entirelydistributed environment. The GRRL collects routing characteristics fromvaluable information about huge backlog packets and previous operations,substantially approximating the weighting scheme of Q-learning. We also enableevery route to connect with its immediate neighbors regularly such that a morecomprehensive view of network topology may be integrated. Trial findingsdemonstrated that the multi-agent GRRL strategy could achieve a delicatebalance between congestion awareness and the fastest routes, considerablyreducing packet transmission time in general network topologies compared to itscompetitors.

Journal of Computer Science
Volume 18 No. 8, 2022, 743-756


Submitted On: 3 May 2022 Published On: 26 August 2022

How to Cite: Kumar, S. & Chinnasamy, C. (2022). Route Optimization using Hybrid GRU Learning Model for SDN and Edge-Based VANET Topology. Journal of Computer Science, 18(8), 743-756.

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  • SDN
  • ML
  • Reinforcement Learning
  • GRU
  • Edge Computing