SDN VANET Research Topics

SDN-VANET research topics are one of the novel topics to be followed in this research. It combines the properties of both SDN and VANET to increase the network control and management, allowing dynamic resource allocation for the increased interaction between vehicles. The below we provide are several important descriptions related to the SDN-VANET environment:

  1. Define SDN-enabled VANET environment

At the starting stage we first see the definition for SDN-enabled VANET environment, A Software Defined Networking (SDN)-enabled VANET environment denotes the networking frameworks by employing the wireless technologies namely Wi-Fi-or the devoted short interactions and merges the standards of SDN with the dynamics of vehicular interactions. Here the SDN is implemented to improve the control, management and optimization of interactions through VANET. There are three main components for VANET architectures namely TA (Trusted Authority), On Board Unit (OBU) and RSUs (Road side unit).

  1. What is SDN-enabled VANET environment?

Next to the definition of SDN-enabled VANET environment we see the detailed description of this. It combines the principles of SDN into VANET and it improves the network control and management, and for enhanced interaction among vehicles the dynamic resource allocation is allowed. In this the focused control of SDN framework optimizes linkage and acceptance in VANETs. There are three types of VANET architecture namely Hybrid architecture, pure ad-hoc networks and pure cellular/WLAN networks.

  1. Where SDN-enabled VANET environment used?

After the detailed description of SDN-enabled VANET environment we discuss where to employ. This environment has the possibility to alter the transportation systems and creating them more effectively, protective and well linked. To make smart and adjustable transportation system, the applications rely on the strong combination of SDN principles over VANET technology is utilized.

  1. Why SDN-enabled VANET environment technology proposed? , previous technology issues

In this research we proposed the SDN-enabled VANET environment method and it overcomes some existing VANET technology difficult and restriction. Now the difficult are accepted by the traditional VANET and are reduced or addressed by combining with SDN principles. Some of the existing technology problems are Emergency Communication, Dynamic Network Topology, Resource Allocation and Traffic Management, Scalability and Management Complexity, Quality of Service (QoS) Assurance and Security and Privacy Concerns. Several challenges that are overcame by the traditional VANET are Privacy control, Improved Scalability, Centralized Control, Optimized Resource Allocation, Reduce Network Overhead, Adaptability, Security Enhancement and Dynamic Traffic Management.

  1. Algorithms / protocols

Here we provide some algorithms that are relevant to this research proposal SDN-VANET environment are Improved GoogleNet (IGNet), Enhanced Support Vector Machine (ESVM), Upgraded Kerberos Authentication Protocol (UPKAP) and Sooty Tern Optimization Algorithm (STOA) are some of the methods to be employed in this research.

  1. Comparative study / Analysis

In comparative analysis we compare various methods to obtain the correct results. We compare the methods like

  • The ESVM model combines the capacity of data preprocessing, rule generation into a SVM framework and parameter selection, and we can apply this to solve real world issues.
  • UPKAP is a protocol for validating the service requirements among the hosts through a non-secure network, namely internet.
  • STOA is a method that is adjusted for separating the nodules through the fine features, that aims in enhancing diagnostic accuracy and it also uses a Local Binary Pattern (LBP) for defining the corresponding feature recovery from nodules.
  1. Simulation results / Parameters

SDN-VANET environment is proposed in this research, this can utilize various algorithms by obtaining the better findings and here we provide some metrics to improve its result are Attack Detection Rate, Authentication Delay (s) and Malicious Traffic (bytes/sec) are compared with the Number of vehicles and the Authentication Rate is compared with the Number of Authentication Requests.

  1. Dataset LINKS / Important URL

The proposed SDN-VANET addresses several existing technology issues, simulated on a best environment and we offer some links to be employed for the clarification of SDN-VANET related concepts:

  1. SDN-enabled VANET environment Applications

Some of the applications to be utilized for the SDN-VANET environment are Collision Avoidance, Public Transportation Management, Traffic Management and Optimization, Roadside Unit (RSU) Management, Traffic Flow Prediction and Analysis and Emergency Services and Alerts are the applications of these environments.

  1. Topology for SDN-enabled VANET environment

Topology to be utilized in the SDN-VANET environments is possible for them are Dual Level Access Policy Validation & Revocation, Distributed Upgraded KAP based Entity Authentication and the Two-Fold Cyber Attack Detection and Mitigation are the topologies used here.

  1. Environment in SDN-enabled VANET environment

The environment of an SDN-VANET environment denotes the framework in which the network works, consisting the components, infrastructure, and surroundings that impact its functionality.

  1. Simulation tools

The SDN-VANET environment incorporates the following software requirements for this research. This environment is developed by using the tools Omnet++ 4.6, SUMO 0.19.0. The environment is proposed by implementing the C++ programming language and is developed by the environment windows 10[64-bit].

Parameters Values
Version of OMNET++ OMNET++ 4.6
Version of SUMO SUMO 0.19.0
Number of vehicles 100
Blockchain node 1
Number of ERSU 4
Controller 3
Log collector 1
Vehicle acceleration 3.5m/sec2
Packet interval 2s
Generated packet number 1024
Packet size 512
No. of packets ~5000
End-to-End delay 1ms
Data rate Max 2Tbps
Simulation time 500s
Transmission power 3.4mW
Rate of transmission 6 Mbps
Bandwidth 30 MHz
Simulation area 2750m×2250m


  1. Results

SDN-VANET environment enhances the network control and management and it allows dynamic resource allocation for improving the interaction among vehicles. In this research we overcome several existing difficult and compare several parameters or metrics to obtain the possible outcome.

SDN VANET Research Ideas:

The Succeeding are the research topics that are related to the SDN-VANET environment. These research topics are useful when the queries to be arise during this research:

  1. Intrusion Detection System for SDN based VANETs Using A Deep Belief Network, Decision Tree, and ToN -IoT Dataset
  2. Quantitative Survey on Software Defined Networks (SDN) in VANETs
  3. Optimization of RBF-SVM Kernel Using Grid Search Algorithm for DDoS Attack Detection in SDN-Based VANET
  4. Software Defined Network Framework & Routing Protocol Based on VANET Technology
  5. Transformer Based Traffic Flow Forecasting in SDN-VANET
  6. An authentication approach in SDN-VANET architecture with Rider-Sea Lion optimized neural network for intrusion detection
  7. Design and development of a hybrid (SDN + SOM) approach for enhancing security in VANET
  8. SDCast: A Software-Defined Networking Based Clustered Routing Protocol for Vehicular Ad-Hoc Networks
  9. VANET Handoff from IEEE 80.11p to Cellular Network Based on Discharging with Handover Pronouncement Based on Software Defined Network (DHP-SDN)
  10. Implementation of a Fuzzy-Based Testbed for Assessment of Neighbor Vehicle Processing Capability in SDN-VANETs
  11. Implementation of a Fuzzy-Based Testbed for Coordination and Management of Cloud-Fog-Edge Resources in SDN-VANETs
  12. A Comparison Study of Two Fuzzy-based Systems for Assessment of Fog Computing Resources in SDN-VANETs
  13. Towards Secure SDN-Based VANETs Paradigm
  14. F-SDNGR: Fog with SDN-Based Geographical Routing Enhances the Reliable Routing in Vehicular Ad Hoc Network (VANET)
  15. Privacy preservation and security management in VANET based to Software Defined Network
  16. Survey on Security Attacks in Software Defined VANETs
  17. Modified POX Controller for Enhancing Quality of Experience of Multimedia Streaming in a Realistic Software Defined Vehicular Ad Hoc Networks
  18. Fog Computing- and Software Defined Network-Based Routing Protocol for Vehicular Ad-hoc Network
  19. A Novel Routing Protocol for Hierarchical Software Defined Vehicular Adhoc Network
  20. Performance Evaluation of Machine Learning Algorithms applied in SD-VANET for Efficient Transmission of Multimedia Information
  21. A survey on software-defined vehicular networks (SDVNs): a security perspective
  22. Toward the Design of an Efficient and Secure System Based on the Software-Defined Network Paradigm for Vehicular Networks
  23. Development of Control-Plane Switch Migration Testbed Using Mininet-WiFi for Software-Defined Vehicular Network
  24. Congestion-Aware Routing in Software Defined Vehicular Networks
  25. ALPS: An Adaptive Link-State Perception Scheme for Software-Defined Vehicular Networks
  26. AC-SDVN: An Access Control Protocol for Video Multicast in Software Defined Vehicular Networks
  27. Dynamic Assignment of Software-Defined Controllers in Vehicular Networks: An Evolutionary Game Approach
  28. Deep Active Learning Intrusion Detection and Load Balancing in Software-Defined Vehicular Networks
  29. Dynamic Service-Orientation for Software-Defined In-Vehicle Networks
  30. A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks
  31. Resource Cooperation in MEC and SDN based Vehicular Networks
  32. Cybertwin-Driven DRL-Based Adaptive Transmission Scheduling for Software Defined Vehicular Networks
  33. ICDRP-F-SDVN: An innovative cluster-based dual-phase routing protocol using fog computing and software-defined vehicular network
  34. SDN-assisted technique for traffic control and information execution in vehicular adhoc networks
  35. ICDRP-F-SDVN: An innovative cluster-based dual-phase routing protocol using fog computing and software-defined vehicular network
  36. Resource Provisioning for Mitigating Edge DDoS Attacks in MEC-Enabled SDVN
  37. Het-SDVN: SDN-Based Radio Resource Management of Heterogeneous V2X for Cooperative Perception
  38. Collaborative Intrusion Detection System for SDVN: A Fairness Federated Deep Learning Approach
  39. Reliable and Scalable Routing Under Hybrid SDVN Architecture: A Graph Learning Based Method
  40. TFMD-SDVN: a trust framework for misbehavior detection in the edge of software-defined vehicular network
  41. SDVN-Based Smart Data Dissemination Model for High-Speed Road Networks
  42. Building an SDVN Framework for RSU-Centric Cooperative Perception with Heterogeneous V2X
  43. A Multi-objective Optimization Approach for SDVN Controllers Placement Problem
  44. A Highway Routing Algorithm Based on SDVN
  45. A Fuzzy approach for load balancing in Blockchain-based Software Defined Vehicular Networks
  46. Connectivity-based Fog Structure Management for Software-defined Vehicular Networks
  47. Prediction and detection model for hierarchical Software-Defined Vehicular Network
  48. Intelligent and resizable control plane for software defined vehicular network: a deep reinforcement learning approach
  49. CBACS: A Privacy-Preserving and Efficient Cache-Based Access Control Scheme for Software Defined Vehicular Networks
  50. Real-time cooperative data routing and scheduling in software defined vehicular networks