Validation of VANET message dissemination algorithms otherwise vulnerable to broadcast storms in urban contexts

AlQahtani, Osama; Sheldon, Frederick T.

doi:10.1002/ett.4312

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…The simulation experiments of the proposed BCOA‐NCT and the benchmarked CAMONET, GOACT, and CACONET approaches are conducted using Matlab version 8.5.0 installed over the system with configuration of core i5 2.2 GHz processor and the capacity of 4 GB RAM 30–34 . The complete experiments are conducted with four different road segment sizes ranging from 1 km × 1 km to 4 km × 4 km of network size.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The simulation experiments of the proposed BCOA-NCT and the benchmarked CAMONET, GOACT, and CACONET approaches are conducted using Matlab version 8.5.0 installed over the system with configuration of core i5 2.2 GHz processor and the capacity of 4 GB RAM. [30][31][32][33][34] The complete experiments are conducted with four different road segment sizes ranging from 1 km × 1 km to 4 km × 4 km of network size. The number of vehicular nodes used for simulation varies from 10 to 60 with the range of transmission being adjusted from 100 to 600 m. The velocity of the vehicular nodes is dynamically changed from 80 to 120 km/h with the nodes moving in a bidirectional way along the x-axis.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

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Border Collie optimization algorithm‐based node clustering technique in vehicular ad hoc networks

Godi¹,

Janakiraman²

2022

Trans Emerging Tel Tech

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In vehicular ad hoc networks (VANETs), the nodes are vehicles and these vehicles communicate with one another on the road. The vehicular nodes on the road move continuously and it introduces dynamic changes in the topology of the network. When the node density in the vehicular network is high, then it is still more challenging as these situations introduce different challenges for network scalability and hurdles the process of determining an optimal route in the vehicular network. At this juncture, clustering protocols are often utilized for resolving the issue of network scalability and the factors that hinders process of identifying optimal routes in vehicular network. In this article, Border Collie optimization algorithm‐based node clustering technique (BCOA‐NCT) is proposed for achieving optimal cluster head selection that minimizes network overhead in uncertain node density situations. This BCOA‐NCT is proposed for facilitating equal concentration on both exploitation and exploration during the process of clustering. It utilized the strategy of eyeing for preventing the algorithm from being stuck into local optima during the cluster head selection process. The simulation experiments of the proposed BCOA‐NCT and the benchmarked schemes confirmed better throughput of 6.19%, minimized end‐to‐end delay of 7.24%, reduced mean energy consumptions of 6.89%, and reduction in the number of beacon messages utilized by 8.26% under the impact of different density of vehicular node. The outcome of the results indicated that the proposed BCOA‐NCT on par with the baseline approaches is capable enough in handling the issue of scalability and better estimation of optimal routes under the increase of node density in the vehicular network.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Border Collie optimization algorithm‐based node clustering technique in vehicular ad hoc networks

Godi¹,

Janakiraman²

2022

Trans Emerging Tel Tech

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“…There are several types of communication in VANET: (1) Vehicle to Vehicle communication (V2V), in which vehicles exchange information between vehicles; (2) Vehicle to infrastructure communication (V2I), in which vehicles exchange messages with road infrastructure; and Vehicle to everything communication (V2X), in which vehicles exchange messages with various devices in the environment. 3 Hence, vehicles gather data about the road and other vehicles in the network. The purpose of developing VANETs is to improve the traffic control system, prevent accidents, enable safer driving, and enhance user experience during driving.…”

Section: Introductionmentioning

confidence: 99%

“…VANET is a promising, emerging technology used to give humans a safer and more comfortable driving experience. There are several types of communication in VANET: (1) Vehicle to Vehicle communication (V2V), in which vehicles exchange information between vehicles; (2) Vehicle to infrastructure communication (V2I), in which vehicles exchange messages with road infrastructure; and Vehicle to everything communication (V2X), in which vehicles exchange messages with various devices in the environment 3 . Hence, vehicles gather data about the road and other vehicles in the network.…”

Section: Introductionmentioning

confidence: 99%

Decentralized prediction and reputation approach in vehicular networks

Najafi

Khoukhi

Lemercier

2022

Trans Emerging Tel Tech

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A vehicle ad-hoc network (VANET) is an essential component of the intelligent transportation system. In VANET, vehicle-to-vehicle communication plays a significant role in providing a secure network and safe exchange of information between nodes. However, VANET is prone to critical risks, threats, and attacks due to its unique characteristics and dynamic topology. Trust is one of the most critical aspects of security. So, this concept has already proved its accuracy and efficiency in various wireless applications. To ensure trust in vehicular communications, we propose a decentralized prediction and reputation approach that includes two models. First, we introduce a reputation model based on the Bayesian theorem and watchdog concept. We investigate the vehicle's behavior in the network to measure the vehicle's trust rate. Then, we present a prediction model to anticipate the vehicle's state and detect malicious vehicles. Based on this approach, vehicles can avoid interacting with unreliable vehicles and ignore incoming messages from them to reduce the effect of malicious nodes in the network. Our extensive simulations show the performance and accuracy of our approach. Also, the certainty rate remains between 91% and 99% regardless of the simulation scenario's duration. This simulation also demonstrates our classification's certainty, even under different network situations.

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