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

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 2.00000mJ, latency values of 15.61668s, 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 15.64318s, and energy consumption to 2.00000mJ. These outcomes demonstrate the effectiveness of our proposed method. Thus, the proposed system is more efficient than existing systems.

AI-Based Efficient Wireless Technologies and Infrastructure-Based Networks With VANET For Smart Transportation High Performance

“…A prior understanding of unusual behavior is not required for the proposed method. In addition, Simmross-Wattenberg et al in [16] utilized a stable method to fit network traffic as well as a threshold-based method to find unusual behaviors. Although numerous cutting-edge algorithms for anomaly detection is proposed, none of them are directly applicable to VANETs [17].…”

Intelligent analytics algorithms in breach detection systems for securing VANETs and data for smart transportation management

“…This growth has led to the creation of new smart applications [3]. The automotive industry urgently requires the development of effective protocols that are fully compatible with current technologies as well as trends [4]. Vehicle communication security scenarios and threats in Figure 1 follow.…”

“…Following formula is utilized to find z-th transmission link's available bandwidth for the time period (t, t) by eq. (3,4).…”

“…The development of self-contained autonomous vehicles, telematics-equipped vehicles, enhanced intelligent transportation systems, and other illogical concepts can now be realized through the use of intelligence techniques. However, the world is flooded with data in the age of Industry 4.0 [4]. For instance, a large amount of data is generated by online social networks like Facebook and Twitter.…”

An Extended Review Concerning the Relevance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors

“…Furthermore, the survey effort that is included in [ 69 ] created interesting perspectives on related scientific problems. Moreover, the authors of [ 70 , 71 ] proposed interesting data transmission models relative to next-generation radio networks, while [ 72 ] described a versatile data communication channels management system, which can be used in a variety of real-world use case scenarios, including vehicular ad hoc networks (VANET).…”