Impact of hybridized rider optimization with cuckoo search algorithm on optimal VANET routing

Gnanasekar, Tony Santhosh; Samiappan, Dhandapani

doi:10.1002/dac.4954

Cited by 3 publications

(1 citation statement)

References 47 publications

(52 reference statements)

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“…This phenomenon has a negative impact on network performance, especially on the packet delivery ratio 16 . Moreover, the natural characteristics of VANETs are limited by many factors such as transmission range coverage, limited bandwidth and link capacity 17 . This can also cause a high packet loss rate and routing performance degradation.…”

Section: Introductionmentioning

confidence: 99%

Efficient deployment of roadside units in vehicular networks using optimization methods

Lehsaini

Gaouar

Nebbou

2022

Int J Communication

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Summary Nowadays, vehicles have become more and more intelligent and equipped with highly sophisticated systems. This allows them to communicate with each other and with the roadside units (RSUs). Furthermore, to ensure efficient data dissemination in vehicular ad hoc networks (VANETs), it is recommended that a Vehicle‐to‐Infrastructure (V2I) architecture be chosen where RSUs will be installed at intersections. Nevertheless, it is not advisable to place an RSU at each intersection because of their high cost. It is therefore appropriate to reduce the number of RSUs by choosing locations at intersections that maximize the surface covered of the urban area and minimize the area of overlapping zones. Moreover, deploying an optimal number of RSUs in an urban area meeting the above requirements is an NP‐hard problem since the number of combinations is very high when the number of intersections is very large. For this purpose, we used metaheuristic approaches. The first approach is represented by the standard version of genetic algorithms (GA‐Basic) and its improved version (GA‐Improved) while the second approach is based on the standard version of simulated annealing (SA‐Basic) and its improved version (SA‐Improved). The proposed approaches are evaluated over OMNET++ simulator. The results obtained showed that the GA‐Improved approach deploys a reduced number of RSUs (37.5%) while guaranteeing acceptable routing performance compared to the GA‐Basic, SA‐Basic, and SA‐Improved approaches which deploy 46.25%, 65%, and 52.50%, respectively, for the same routing performance.

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Section: Introductionmentioning

confidence: 99%

Efficient deployment of roadside units in vehicular networks using optimization methods

Lehsaini

Gaouar

Nebbou

2022

Int J Communication

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Intelligent Humming Bird Optimization Based Distance Aware Routing Scheme for Vehicular Networks

Abed,

Shnawa,

Sheel

et al. 2023

2023 6th International Conference on Engineering Technology and Its Applications (IICETA)

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

et al. 2022

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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.

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Impact of hybridized rider optimization with cuckoo search algorithm on optimal VANET routing

Cited by 3 publications

References 47 publications

Efficient deployment of roadside units in vehicular networks using optimization methods

Efficient deployment of roadside units in vehicular networks using optimization methods

Intelligent Humming Bird Optimization Based Distance Aware Routing Scheme for Vehicular Networks

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

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