An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication

Gagan Preet Kour Marwah, Anuj Jain, Praveen Kumar Malik, Manwinder Singh, Sudeep Tanwar (), Calin Ovidiu Safirescu (), Traian Candin Mihaltan, Ravi Sharma and Ahmed Alkhayyat
Additional contact information
Gagan Preet Kour Marwah: School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar 144411, India
Anuj Jain: School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar 144411, India
Praveen Kumar Malik: School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar 144411, India
Manwinder Singh: School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar 144411, India
Sudeep Tanwar: Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India
Calin Ovidiu Safirescu: Environment Protection Department, Faculty of Agriculture, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Calea Mănăștur 3-5, 400372 Cluj-Napoca, Romania
Traian Candin Mihaltan: Faculty of Building Services, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
Ravi Sharma: Centre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, Dehradun 248001, India
Ahmed Alkhayyat: College of Technical Engineering, The Islamic University, Najaf 54001, Iraq

Mathematics, 2022, vol. 10, issue 21, 1-31

Abstract: The vehicular ad hoc network, VANET, is one of the most popular and promising technologies in intelligent transportation today. However, VANET is susceptible to several vulnerabilities that result in an intrusion. This intrusion must be solved before VANET technology can be adopted. In this study, we suggest a unique machine learning technique to improve VANET’s effectiveness. The proposed method incorporates two phases. Phase I detects the DDoS attack using a novel machine learning technique called SVM-HHO, which provides information about the vehicle. Phase II mitigates the impact of a DDoS attack and allocates bandwidth using a reliable resources management technique based on the hybrid whale dragonfly optimization algorithm (H-WDFOA). This proposed model could be an effective technique predicting and utilizing reliable information that provides effective results in smart vehicles. The novel machine learning-based technique was implemented through MATLAB and NS2 platforms. Network quality measurements included congestion, transit, collision, and QoS awareness cost. Based on the constraints, a different cost framework was designed. In addition, data preprocessing of the QoS factor and total routing costs were considered. Rider integrated cuckoo search (RI-CS) is a novel optimization algorithm that combines the concepts of the rider optimization algorithm (ROA) and cuckoo search (CS) to determine the optimal route with the lowest routing cost. The enhanced hybrid ant colony optimization routing protocol (EHACORP) is a networking technology that increases efficiency by utilizing the shortest route. The shortest path of the proposed protocol had the lowest communication overhead and the fewest number of hops between sending and receiving vehicles. The EHACORP involved two stages. To find the distance between cars in phase 1, EHACORP employed a method for calculating distance. Using starting point ant colony optimization, the ants were guided in phase 2 to develop the shortest route with the least number of connections to send information. The relatively short approach increases protocol efficiency in every way. The pairing of DCM and SBACO at H-WDFOA-VANET accelerated packet processing, reduced ant search time, eliminated blind broadcasting, and prevented stagnation issues. The delivery ratio and throughput of the H-WDFOA-packet VANET benefitted from its use of the shortest channel without stagnation, its rapid packet processing, and its rapid convergence speed. In conclusion, the proposed hybrid whale dragonfly optimization approach (H-WDFOA-VANET) was compared with industry standard models, such as rider integrated cuckoo search (RI-CS) and enhanced hybrid ant colony optimization routing protocol (EHACORP). With the proposed method, throughput could be increased. The proposed system had energy consumption values of 2.00000 mJ, latency values of 15.61668 s, and a drop at node 60 of 0.15759. Additionally, a higher throughput was achieved with the new method. With the suggested method, it is possible to meet the energy consumption targets, delay value, and drop value at node 60. The proposed method reduces the drop value at node 80 to 0.15504, delay time to 15.64318 s, and energy consumption to 2.00000 mJ. These outcomes demonstrate the effectiveness of our proposed method. Thus, the proposed system is more efficient than existing systems.

Keywords: VANET; support vector machine; rider optimization algorithm; cuckoo search; whale optimization algorithm; dragonfly; DDoS attack (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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