Congestion In Vehicular Ad Hoc Networks Research Articles

Prediction of Traffic Congestion in Vehicular Ad-Hoc Networks Employing Extreme Deep Learning Machines (Edrlm)

Vehicular Ad-Hoc Networks (VANETs) represent a crucial component of intelligent transportation systems (ITS), enabling vehicles to communicate with each other and with roadside infrastructure. Predicting traffic congestion in VANETs is essential for enhancing road safety, optimizing traffic flow, and improving overall transportation efficiency. Traditional machine learning methods have shown promise in this domain; however, they often fall short in handling the complex, high-dimensional data typical of VANETs. To address these challenges, this study employs Extreme Deep Learning Machines (EDRLM), an advanced deep learning technique, for traffic congestion prediction. The EDRLM framework leverages the strengths of deep neural networks and extreme learning machines, offering a robust and scalable solution for processing the dynamic and heterogeneous data in VANETs. By integrating feature extraction, selection, and prediction into a unified model, EDRLM can capture intricate patterns and temporal dependencies within traffic data. The proposed model is trained and validated using real-world VANET datasets, incorporating various traffic parameters such as vehicle speed, density, and inter-vehicular distances. Our experimental results demonstrate that EDRLM outperforms conventional machine learning algorithms in terms of prediction accuracy, computational efficiency, and robustness to noise and missing data. The model's ability to provide timely and precise congestion predictions can facilitate proactive traffic management strategies, including dynamic routing and adaptive traffic signal control, ultimately leading to reduced travel times and enhanced road safety. This study underscores the potential of EDRLM in transforming traffic management in VANETs, paving the way for more intelligent and adaptive ITS solutions. Future research directions include exploring hybrid models combining EDRLM with other advanced machine learning techniques and expanding the framework to accommodate emerging vehicular communication technologies such as 5G and Internet of Things (IoT) devices.

A systematic review of congestion control in internet of vehicles and vehicular ad hoc networks: Techniques, challenges, and open issues

SummaryThe Internet of Vehicles (IoV) integrates various technologies to enable real‐time data exchange between vehicles, pedestrians, roadside units, unmanned aerial vehicles, road‐added things, and so forth. IoV is an improved internet‐enabled vehicular ad hoc network (VANET), which is similarly affected by data traffic aggravated by time‐variant network density and the network's topological and informational changes. A high density of simultaneous vehicular communication leads to channel saturation and data congestion, which leads to rapid increases in data loss, packet latency, and lower service quality. Due to the importance of real‐time data communications in high dynamic networks, we used the systematic literature review (SLR) approach to identify 54 articles up to May 2023 to discuss the ways to deal with data traffic congestion in VANETs and IoV networks as well as existing and upcoming issues in this area.

Traffic management in vehicular adhoc networks using hybrid deep neural networks and mobile agents

<p>The traffic congestion in vehicular adhoc networks (VANETs) is a vital problem due to its dynamic increase in traffic loads. VANETs undergo inefficient routing capability due to its increasing traffic demands. This has led to the need for intelligent transport system (ITS) to assist VANETs in enabling suitable traffic loads between vehicles and road side units (RSU). Most conventional systems offer distributed solution to manage traffic congestion but fail to regulate real-time traffic flows. In this paper, a dynamic traffic control in VANETs is offered by combining deep neural network (DNN) with mobile agents (MA). An experimental analysis is carried out to test the efficacy of the DNN-MA against conventional machine learning and a deep learning routing algorithm in VANETs. DNN-Mal is validated under various traffic congestion metrics like latency, percentage delivery ratio, packet error rate, and throughput. The results show that the proposed method offers reduced energy consumption and latency.</p>

Data congestion in VANETs: research directions and new trends through a bibliometric analysis

Vehicular Ad hoc Networks (VANETs) become increasingly popular in academia and manufacturing businesses. The VANETs domain attracts massive attention from various authors all over the world on a large scale. However, substantial research efforts are expected in the VANETs field to solve the data congestion problem. For this, it is vital to state the current status of research in this domain. As the research publications have substantially increased since 2009, a bibliometric analysis is necessary for researchers to understand actual results and findings in this area. This paper examines and analyzes the status of research trends between 2010 and 2019 for the domain “Data congestion in VANETs” by applying various parameters. As extracted from the Scopus database till December 31, 2019, a total of 11,109 publications are associated with the VANETs domain. Moreover, 434 publications among the collection are related to data congestion in the VANETs field. Finally, a software tool named the VOSviewer is used to create and envision the selected field’s bibliometric networks. This analysis paper will help the researchers to catch the research trends of data congestion in VANETs.

A novel clustering technique to stop congestion occur vehicular ad-hoc networks using node density based on received signal strength

Vehicular Ad Hoc Network (VANET) has received better consideration from academic researcher and automobile companies in modern years. Meanwhile, Congestion control remains the major area of research in VANET due to its characteristics such as high mobility, high vehicle density, and unbounded network size, frequent exchange of information, rapidly changing network topology, and lack of central management. Congestion in the VANET broadcast channel may occur, because of number of vehicles in a relatively tiny area raises, the communications channel becomes exceed by beacon message, and event-driven messages. Researchers have suggested a number of answers to control these issues and also to reduce congestion in VANET atmosphere. Congestion in VANET occurs when the vehicles are in the dense part while the network node is carrying more data than it can handle. More vehicles in cluster area are attempting to broadcast simultaneously in dense condition, it will reduce Packet Delivery Ratio and thus congestion will occur. We proposed density based dynamic clustering (DBDC) which finds the high dense region in the cluster and do sub-clustering. First, we find Average Vehicle Density Threshold Value using Received Signal Strength (RSS) to determine the high dense part. Performance result shows our approach is better in all aspects comparing with VWCA clustering approach and also provides high cluster stability.

Decreasing Traffic Congestion in VANETs Using an Improved Hybrid Ant Colony Optimization Algorithm

Vehicular Ad-hoc Network (VANET) is a definitive form of mobile ad-hoc network (MANET), which delivers data communication in a vehicular environment, using wireless transmission. Its fundamental goal is to increase the service quality of Intelligent Transportation Systems (ITS), such as road safety, logistics, and environmental kindliness, as well as information interchange. Smart cities are encountering problematic traffic congestion, particularly in developing countries. This paper presents an Improved Hybrid Ant Colony Optimization (IHACO) algorithm for decreasing congestion in smart cities. The objective of the proposed scheme is to choose a best routing path during rush hours by providing an optimal path. The scheme also introduces the IHACO algorithm to improve QoS for ITS. This algorithm (IHACO) differs from other algorithms, such as particle swarm optimization (PSO), in terms of pheromone update processes, which makes it more efficient. Also, the ant colony hybrid routing protocol (ACOHRP) protocol is introduced to improve the service quality of intelligent traffic systems (ITS). It delivers superlative efficiency through a better origination of packet delivery ratio, throughput, and end-to-end delay. Simulation-based testing is performed using Matlab simulation. It was found that traffic congestion time decreased gradually when using IHACO, unlike with other algorithms. The computed results demonstrated that the IHACO algorithm offers improved performance in terms of reliability, period, distance, and throughput, compared with different algorithms presented in this paper.

Channel-Aware Congestion Control in Vehicular Cyber-Physical Systems

In vehicular cyber-physical systems, cars are connected to create a mobile network called a vehicular ad hoc network (VANET) to perform various functions, including improved awareness of the surrounding environment. Moving vehicles continually broadcast beacon signals containing information such as position, heading, acceleration, steering angle, vehicle size, and accident notification. However, channel congestion in dense traffic conditions adversely affects network performance. To resolve congestion in VANETs, several works in the literature have studied congestion control. However, they have considered packet loss only as an indication of channel congestion regardless of channel condition. In this paper, we present a channel-aware congestion control algorithm (CACC) that controls the transmission power and data rate. We take into account the received signal strength (RSS) when diagnosing packet loss to determine channel conditions, such as severe fading or channel congestion. In the case of severe fading, we decrease the data rate for a more robust modulation and coding scheme. Additionally, we adjust the transmission power to maintain a desirable packet error rate. Our simulation results show that CACC significantly outperforms other distributed congestion control algorithms by reducing the packet loss rate and increasing the packet delivery ratio.

A Method to Implement Scheduling and Prioritizing of Messages in VANET’s

Vehicular Adhoc Network (VANET) could be a technology that focuses on increasing convenience and safety of passengers and drivers. Vehicular Adhoc Network’s applications will suffer from severe performance degradation in high density state of affairs because of channel congestion.Congestion management ought to be taken into consideration to improvise safety, performance & dependability of VANETs. during this paper, we have a tendency to projected a technique DySch that assign the priorities to service and safety messages supported the message content (static factor), message size and Dynamic Factor(network state) . In DySch strategy, messages is scheduled severally, heuristically and dynamically. The investigation of performance is done by mistreatment urban and main road situations whereas the packet ratio, average delay, average output, and queuing delay are thought-about. as compared to the simplest standard methods, DySch strategy Simulation results shows that the performance of VANETs is improved. to manage congestion in VANETs, the projected strategy will help in improvising safety & dependability by assigning a priority as higher to the security messages.

Current Research on Congestion Control Schemes in VANET: A Practical Interpretation

The change in lifestyle of human beings has increased the trend of purchasing multiple vehicles in same family. This has ultimately led to the hike in total number of on road moving vehicles. The prime concern including offering the best features to a vehicle is to offer a network that is non-congested even with this huge rise in vehicles. Therefore, a congestion control scheme is required in Vehicular ad hoc network (VANET) that ensures timely distribution of messages in the case of unusual activities or surprising events in network. Moreover, the congestion control scheme should also ensure that all the vehicles get correct information and will be able to access correct path in event of congestion. This research work focus on the existing controlling schemes for congestion in VANET. A detailed discussion is made initially on the existing schemes. Later, a comparative analysis is made on existing schemes by evaluating their performance on basis of computational overhead, latency and the throughput of packets. For simulating the network OMNeT++ is used along with SUMO. The results and discussions made at end help the novice to identify the best existing scheme for controlling the congestion in VANET.

EVAC-AV: The Live Road Surveillance Control Scheme Using an Effective-Vision-Area-Based Clustering Algorithm With the Adaptive Video-Streaming Technique

A new type of safety service in vehicular ad hoc networks (VANETs) is live road surveillance (LRS) video streaming. However, video streaming is a high-bandwidth-demanded service and causes network congestion if there are many vehicles that stream video in the wireless network at the same time. Clustering is an efficient technique to relieve network congestion in VANET because it can reduce the number of vehicles to transmit data. The adaptive video-streaming technology can also relieve the aforementioned problem based on monitoring the wireless network's utilization situation. This paper proposes to provide the LRS service using an effective-vision-area-based clustering algorithm with the adaptive video-streaming technique (EVAC-AV) based on the effective vision of the cameras in vehicles. EVAC-AV can construct clusters such that a driver X can join a suitable cluster to get the LRS service from the appropriate vehicle ahead of X to have additional vision. Additionally, the adaptive video-streaming technique is adopted to have an adaptive LRS video quality in EVAC-AV based on the IEEE 802.11p wireless network's utilization condition. In this paper, we compare the proposed EVAC-AV control scheme for LRS service with the following: 1) other LRS service schemes that did not adopt the clustering mechanism and 2) other clustering algorithms in terms of different performance metrics to prove EVAC-AV's effectiveness.