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Abstract A vehicular ad‐hoc network (VANET) is derived from a mobile ad‐hoc network that is a part of less infrastructure network design. Vehicular communication in VANET can be achieved using vehicle‐to‐infrastructure (V2I) and vehicle‐to‐vehicle (V2V) communication. A vehicle communicates with other vehicles through onboard units while communicat...
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This study proposes an innovative integration of the Car-to-Car Network-Hierarchical deep neural network (CtCNET-HDRNN) model with Fifth generation (5G) and Dedicated Short-Range Communications (DSRC) systems, streamlining computational efficiency in edge computing. CtCNET-HDRNN is a specialized deep learning model designed for vehicular communicat...
Citations
... The research [14] suggests an improved cluster-based lifetime protocol that prioritizes network routing stability and average performance. A fuzzy inference method based on the Sugeno model is used to evaluate the CH using input parameters like local distance, residual energy, concentration, node degree, and distance from the base station. ...
5G-based Vehicular Ad-Hoc Networks (5G-VANETs) combine the advancements of 5G wireless communication technology with the capabilities of VANETs. These networks leverage the high data rates, low latency, and massive device connectivity provided by 5G to enable efficient and reliable communication among vehicles and infrastructure. Implementing a VANET environment in real-world scenarios poses significant challenges. This paper focuses on improving the Quality of Service (QoS) in 5G-based VANETs through the implementation of an optimized approach to the routing protocol. The chosen routing protocol for this study is Optimized Link State Routing (OLSR). To optimize the protocol, Cuckoo Search Optimization (CSO) and Simulated Annealing (SA) techniques are applied. The simulation is conducted using the ManhattanGrid mobility model, which provides a realistic representation of the vehicular environment. Various test scenarios are created by varying the vehicular density and vehicle speeds. The performance evaluation of the suggested optimized techniques is carried out using three key metrics: Packet Delivery Ratio (PDR), throughput, and End-to-End Delay (E2ED). The results of the simulation demonstrate that the CSO technique outperforms other optimization techniques in terms of enhancing QoS in 5G-based VANETs.
... According to Naeem et al. [36], the proposed method can improve network routing stability and average transfer rate. The Sugeno model fuzzy inference system evaluates Cluster Heads (CH) considering the distance to the base station, concentration, node degree, local distance, and residual energy. ...
... Mobility models and communication models, such as 3G/4G LTE and IEEE 802.11p, are available from Veins to simulate a variety of protocols and technologies used in vehicle networks. The simulator supports various routing protocols, including DSR, Greedy Perimeter Stateless Routing (GPSR), and AODV [36]. The proposed work involves 10 to 100 vehicles ranging from (9) 10 to 100 kmph. ...
... On the other hand, some researches use various optimization algorithms for improving routing strategies in VANETs. Authors in [19] propose an enhanced cluster-based lifetime protocol that maximizes both the network's routing stability and average throughput. The proposed protocol specifically emphasizes the efficient transfer of packets through cluster heads (CHs) selected using the fuzzy inference system (FIS). ...
Using a multi-hop routing protocol for unicast vehicle-to-vehicle (V2V) communications is crucial to facilitate data relaying from one vehicle to a distant one. The dynamic behavior of vehicular ad-hoc networks (VANETs) and unstable relays often cause frequent disconnections or switchovers, leading to higher latency, diminished reliability, and increased resource use. The stability of these routes depends not just on link connectivity, but also on the availability of adequate resources. Although numerous studies have explored traditional VANET routing protocols to tackle these issues, they often neglect the critical aspect of resource availability. In this study, we focus on a V2V routing method that considers resource availability, assuming the use of a geo-based resource allocation framework. In the proposed resource-aware approach, we utilize deep learning to predict vehicle trajectories and traffic load in various areas. The proposed resource-aware routing protocol aims at improving both spectrum efficiency and route stability by employing distance between vehicles, traffic load, and resource availability of areas to manage emergency messages (EMs) more efficiently and reduce unnecessary rebroadcasting and congestion. Our simulation results reveal that our proposed method surpasses traditional protocols in terms of packet reception ratio (PRR), latency, and average hop count.
... Domain-specific clustering protocols focus on substantial performance enhancement of specific domain applications. The objective is to determine the areas where clustering schemes can escalate the performance of the network [36]. There are various domains of vehicular networks where clustering intends to attain elevated levels of efficiency, like routing, channel access mechanisms, security, resource management, and optimization. ...
... Efficient clustering protocols optimize different routing decisions by mitigating the effects of high mobility and a dynamic environment. The cluster-based routing protocols claim to achieve high performance in terms of reliability and scalability as compared to traditional routing protocols such as [36,37]. ...
... Cluster size, vehicle density, and bias are taken into consideration as system parameters. An enhanced clustering-based routing protocol is proposed by Afia et al. [36]. The protocol primarily focuses on improving network stability and average throughput using the Sugeno model fuzzy inference system. ...
Vehicular Adhoc Network (VANET) suffers from the loss of perilous data packets and disruption of links due to the fast movement of vehicles and dynamic network topology. Moreover, the reliability of the vehicular network is also threatened by malicious vehicles and messages. The malicious vehicle can promulgate fake messages to the node to misguide it, which may result in the loss of precious lives. In this situation, maintaining efficient, reliable, and secure communication among automobiles is of extreme importance, especially for a densely populated network. One of the remedies is vehicular clustering, which can effectively perform in a high-density network. However, secure cluster formation and cluster optimization are important factors to consider during the clustering process because non-optimal clusters may incur high end-to-end communication delays and produce overhead on the network. In addition, malicious nodes and packets reduce passenger and driver safety, increase road accidents, and waste passenger and driver time. To this end, we employ Arithmetic Optimization Algorithm (AOA) to design a secure intelligent clustering named AOACNET. AOA is used to achieve optimality of vehicular clusters. During cluster formation, the algorithm prevents unauthentic nodes from becoming cluster members by taking into consideration the performance value of each automobile. The vehicle’s performance value is based on the record of data transmission. If a vehicle transmits a fake message, it will receive a penalty of (-1), and in the case of transmitting a legitimate message, a reward of (+1) will be assigned to the vehicle. Initially, all the vehicles have equal performance value which either increase or decrease based on communication with their peers. The vehicles will become cluster members only if their performance value is greater than the threshold value (0). AOACNET is tested in MATLAB using various evaluation metrics (i.e., number of clusters, load balancing, computational time, network overhead and delay). The simulation results show that the proposed algorithm performs up to 25% better than the similar contenders in terms of designated optimization objectives.
... The Friedman Test is evaluated using Eq. (29) where FT stands for Friedman test statistic, N denotes the number of groups ( N = 3 ) and R i Signifies the sum of the rank of ith group. ...
A vehicular ad hoc network (VANET) includes groups of stationary or moving vehicles linked by a wireless network. The significant usage of VANET is to offer comfort and safety to drivers in road environments. VANET provides a communication framework that aids in minimizing accidents. Also, sharing data in VANET is time-sensitive and necessitates vigorous and quick network link formation. However, appropriate routing is critical to avoid the streaming issues that occur in VANET applications. This research area is of great concern to the researchers, and this work intends to propose a new Cluster-based VANET routing model with steps like (a) optimal cluster head selection (CHS) and (b) appropriate gateway selection for data transmission. Here, the optimal CHS takes place considering the Packet delivery ratio (PDR), mean routing load, end-to-end (e2e) Delay, Throughput, and packet overhead. This paper introduced a new Customized hunger’s foraging honey badger with a Dynamic multi-objective non-sorted genetic algorithm (CHFHB-DMNSGA). Also, the gateway selection is done under two objectives: Vehicular gateway mobility and receiver signal strength, respectively. Finally, the paper presented a comparative assessment to validate the effectiveness of the adopted algorithm CHFHB-DMNSGA over the other algorithms like R-NSGA-II, NSGA-III, NSGA-II, HBA, HGS, ABC, FF, CSO, ACO, LA, MHB-GA, GA-FF, and HFCHBO.
... In an effort to improve the network's average throughput and routing stability, Naeem et al. [24] proposed an improved cluster-based lifetime technique named ECBLTR. In order to assess the cluster head (CH), parameters including concentration, local distance, node degree, residual energy, and distance from the base station are fed into the Sugeno model fuzzy inference system. ...
... But before that, we check the communication cost for priority traffic and ordinary traffic (Emergency vehicles and Traditional vehicles) by considering the fixed length message scenario. Furthermore, we used the mandatory messages (starting from initiation and ending at communication termination) to compute the communication cost in the context of energy consumption for the proposed paradigm in the presence of traditional protocols such as Ad hoc On-Demand Distance Vector (AODV) [35], Destination-Sequenced Distance Vector (DSDV) [36], and Low-Energy Adaptive Clustering Hierarchy (LEACH) [37]. For our calculations, we took into account a message length of 512 bytes and a Transmission Energy Consumption Rate (TSCR) of 0.002 watts. ...
... Similarly, we considered a scenario to calculate the total communication cost in the context of exchanging the total number of bits for different numbers of messages for our prototype and comparative protocols. The result statistics are shown in Table I. 1) Comparative Protocols Communication Cost : In this subsection, we analyze the latency results of comparative protocols as discussed in references [35][36] and [37]. Our observation from the operational scenario of these protocols indicates that they tend to consume more energy in the context of communication costs when transmitting data from the same vehicles to the destination location. ...
Quality of Service (QoS) plays a pivotal role in numerous delay-sensitive applications that range from general to specific such as the Internet of Medical Things (IoMT), Industrial Internet of Things (IIoT), Unnamed Aerial Vehicles (UAVs), Industrial Automation, and Cooperative Internet of Vehicles (C-IoV), etc. Every application has numerous contributions to human daily life activities, but here in this work, we focused on the C-IoV in the context of QoS metrics. Even though the literature suggested several techniques to address the QoS issues in this emerging technology, but we have not come across a single article that addresses this issue in a cooperative environment, considering the impact of communication congestion and contention by taking into account emergency vehicles and traditional vehicles. Given that, in this paper, we introduce a hybrid framework known as the Traffic Congestion and Priority-Aware Algorithm (TCPAA). This innovative paradigm leverages the capabilities of computer vision, Deep Neural Networks (DNN) and Dijkstra algorithm to strategically incorporate the transmission channels and network entities with an objective to improve the QoS metrics in emergency vehicles. Initially, we developed a dataset with computer vision algoritms “real-time (OpenCV ”Background Subtraction”) to evaluate and chose the best machine learning algorithms among random forest, support vector machine (SVM), k-means clustering, and DNN. Based on the result statistics, we select DNN, and classified vehicles into two classes: Emergency and traditional vehicles to train the model. Subsequently, we set standard for two type of communications such as regular and prioritized traffic. We incorporate a micro base station (μBS) in the network for prioritized traffic to facilitate congestion-free communication of emergency vehicles, while the Dijkstra algorithm is used to managed the communication of traditional vehicles. Considering the nature of operation of future autonomous vehicles, we managed most of the decisions processes at the client-side by categorizing the traffic based on the vehicle requirements. Through reliable client-side management, the high performance and accuracy of TC-PAA underscore its efficiency compared to established field-proven schemes. Adhering to reliability metrics such as latency, packet loss ratio, communication cost, data availability, and traffic priority, the proposed model improves QoS metrics in high-demanding areas of IoV networks.
... System Model Naeem et al., (2023) The proposed system model has n mobile nodes that are free to roam about the network, such as N_1,N_2,. . .,N_n. ...
This research addresses critical gaps in Mobile Ad hoc Networks (MANETs) by proposing a hybrid secure cluster-based routing algorithm, focusing on enhancing network security, robustness, and reliability through multipath routing. Methodologically, the approach integrates Convolutional Neural Networks (CNN) for optimal path routing and Emperor Penguin Optimization (EPO) for clustering, introducing a novel combination for efficient cluster head selection. A novel contribution lies in the development of a prediction technique utilizing a trust assessment algorithm to calculate direct trust ratings at each node, incorporating fuzzy values between zero and one. Trust values are further influenced by node performance, adding a dynamic dimension to the trust evaluation process. Key novelties include the emphasis on energy efficiency, network longevity, remaining energy, security level, bandwidth, and packet delivery ratio as evaluation criteria. The proposed CNN-EPO model demonstrates superior results compared to traditional routing protocols, achieving a remarkable 95% energy efficiency, a heightened security level of 99%, and a throughput reaching up to 8 Mbps. Additionally, the Packet Delivery Ratio (PDR) attains close to 99% and routing overhead remains below 0.5, ensuring efficiency in challenging network scenarios with 50 adversaries. In summary, this research contributes a comprehensive solution to MANET challenges, introducing a novel hybrid routing algorithm, incorporating advanced methodologies for path optimization and clustering. These outcomes highlight how important the suggested strategy is to improve the existing state of the art in MANETs.
... Tracking limits are established based on the central point of the coordinate system, with two circles encompassing the outermost boundaries of the circular region 19 . Within this area, vehicles v1, v2, v3, and v4 navigate and move. ...
In recent years Intelligent Transportation System (ITS) has been growing interest in the development of vehicular communication technology. The traffic in India shows considerable fluctuations owing to the static and dynamic characteristics of road vehicles in VANET (Vehicular Adhoc Network). These vehicles take up a convenient side lane position on the road, disregarding lane discipline. They utilize the opposing lane to overtake slower-moving vehicles, even when there are oncoming vehicles approaching. The primary objective of this study is to minimize injuries resulting from vehicle interactions in mixed traffic conditions on undivided roads. This is achieved through the implementation of the Modified Manhattan grid topology, which primarily serves to guide drivers in the correct path when navigating undivided roads. Furthermore, the Fuzzy C-Means algorithm (FCM) is applied to detect potential jamming attackers, while the Modified Fisheye State Routing (MFSR) Algorithm is employed to minimize the amount of information exchanged among vehicles. Subsequently, the Particle Swarm Optimization (PSO) algorithm is developed to enhance the accuracy of determining the coordinates of jamming attackers within individual clusters. The effectiveness of the outcomes is affirmed through the utilization of the Fuzzy C-Means algorithm, showcasing a notable 30% reduction in the number of attackers, along with the attainment of a 70% accuracy rate in this research endeavor.
... An evaluation of these trade-offs is necessary to establish the feasibility and effectiveness of the GAACO technique in different VANET traffic scenarios, which might potentially make it a valuable addition to VANET routing protocols. The Enhanced Cluster-Based Lifetime Protocol (ECBLTR) optimizes the average throughput and enhances the stability of routing networks in Vehicular Ad Hoc Networks [17]. The protocol assesses Cluster Heads (CH) based on factors such as concentration, residual energy, node degree, and local distance from the Base Station (BS) utilizing the Sugeno approach fuzzy inference system. ...
Rapid development in Information Technology (IT) has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle (V2V) transmission. Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data. The communication occurs directly between V2V and Base Station (BS) units such as the Road Side Unit (RSU), named as a Vehicle to Infrastructure (V2I). However, the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time. Therefore, the scheme of an effectual routing protocol for reliable and stable communications is significant. Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment. Therefore, this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing (FOA-EECPCR) technique in VANETS. The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET. To accomplish this, the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy, distance, and trust level. For the routing process, the Sparrow Search Algorithm (SSA) is derived with a fitness function that encompasses two variables, namely, energy and distance. A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method. The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.
