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This is a moment of heavy necessity for a dependable internet connection in the modern world, which is used to engage in business dealings, communicate with other people, entertain oneself, and lead a daily life. Therefore, a Wi-Fi 6 router must have an internal wire-free connection within a house or business. However, as they depend on the weather and are installed in ways that expose them to infiltration, they are vulnerable. The increased demand for Internet services for business, communication, and leisure activities due to poor weather puts added stress on the network, slowing down the speeds and increasing congestion. The bottom line is to prioritize traffic and lower latency and convergence time to improve network performance under less-than-ideal weather conditions. This will be realized using highly technical approaches such as the mini-batch gradient descent optimization algorithm for network optimization and the V Gradient Geocast Routing Protocol for effective routing. The proposed enhanced congestion avoidance model with V Gradient Geocast Routing Protocol in 6G networks under unfavorable weather conditions attains an accuracy of 98.85%.

This paper presents “Proactive Congestion Notification” (PCN), a congestion-avoidance technique for distributed deep learning (DDL). DDL is widely used to scale out and accelerate deep neural network training. In DDL, each worker trains a copy of the deep learning model with different training inputs and synchronizes the model gradients at the end of each iteration. However, it is well known that the network communication for synchronizing model parameters is the main bottleneck in DDL. Our key observation is that the DDL architecture makes each worker generate burst traffic every iteration, which causes network congestion and in turn degrades the throughput of DDL traffic. Based on this observation, the key idea behind PCN is to prevent potential congestion by proactively regulating the switch queue length before DDL burst traffic arrives at the switch, which prepares the switches for handling incoming DDL bursts. In our evaluation, PCN improves the throughput of DDL traffic by 72% on average.

Finding reliable and efficient routes is a persistent problem in megacities. To address this problem, several algorithms have been proposed. However, there are still areas of research that require attention. Many traffic-related problems can be resolved with the help of smart cities that incorporate the Internet of Vehicles (IoV). On the other hand, due to rapid increases in the population and automobiles, traffic congestion has become a serious concern. This paper presents a heterogeneous algorithm called ant-colony optimization with pheromone termite (ACO-PT), which combines two state-of-the-art algorithms, pheromone termite (PT) and ant-colony optimization (ACO), to address efficient routing to improve energy efficiency, increase throughput, and shorten end-to-end latency. The ACO-PT algorithm seeks to provide an effective shortest path from a source to a destination for drivers in urban areas. Vehicle congestion is a severe issue in urban areas. To address this issue, a congestion-avoidance module is added to handle potential overcrowding. Automatic vehicle detection has also been a challenging issue in vehicle management. To address this issue, an automatic-vehicle-detection (AVD) module is employed with ACO-PT. The effectiveness of the proposed ACO-PT algorithm is demonstrated experimentally using network simulator-3 (NS-3) and Simulation of Urban Mobility (SUMO). Our proposed algorithm is compared with three cutting-edge algorithms. The results demonstrate that the proposed ACO-PT algorithm is superior to earlier algorithms in terms of energy usage, end-to-end delay, and throughput.

Aiming to alleviate traffic congestion, many congestion avoidance and traffic optimization systems have been proposed recently. However, most of them suffer from three main problems. Firstly scalability: they rely on a centralized server, which has to perform intensive communication and computational tasks. Secondly unpredictability: they use smartphones and other sensors to detect the congested roads and warn upcoming vehicles accordingly. In other words, they are used to solve the problem rather than avoiding it. Lastly, infrastructure dependency: they assume the presence of pre-installed infrastructures such as roadside unit (RSU) or cellular 3G/4G networks. Motivated by the above-mentioned reasons, in this paper, we proposed a fully distributed and infrastructure-less congestion avoidance and traffic optimization system for VANET (Vehicular Ad-hoc Networks) in urban environments named DIFTOS (Distributed Infrastructure-Free Traffic Optimization System), in which the city map is divided into a hierarchy of servers. The vehicles that are located in the busy road intersections play the role of servers, thus DIFTOS does not rely on any centralized server and does not need internet connectivity or RSU or any kind of infrastructure. As far as we know, in the literature of congestion avoidance using VANET, DIFTOS is the first completely infrastructure-free congestion avoidance system. The effectiveness and scalability of DIFTOS have been proved by simulation under different traffic conditions.

Intersection management is a sophisticated event in the intelligent transportation system due to a variety of behavior for traffic participants. This paper primarily overviews recent studies on the scenes of intersection, aiming at improving the efficiency or guaranteeing the safety when vehicles pass the crossing. These studies are respectively surveyed from the perspectives of efficiency and safety. Firstly, recent contributions to efficiency-oriented, intersection management overviews from four scenes, including congestion avoidance, green light optimized speed advisory (GLOSA), trajectory planning, and emergency vehicle priority preemption control. Furthermore, the studies on intersection collision detection and abnormal information warning are surveyed in the safety category. The corresponding algorithms for velocity and route management presented in the surveyed works are discussed.

Alleviating traffic congestion is one of the main challenges for the Internet of Vehicles (IoV) in smart cities. Many congestion pricing systems have been proposed recently. However, most of them focus on punishing the vehicles that use certain roads during peak hours, neglecting the proven fact that rewards can encourage drivers to follow the rules. Therefore, in this paper, we propose a new congestion pricing system based on reward and punishment policies for the IoV in a smart city environment, where the vehicles are rewarded for voluntarily choosing to take an alternative path to alleviate traffic congestion. The proposed system is implemented using vehicular ad hoc networks, which eliminate the need for installing a costly electronic toll collection system. We propose a new virtual currency called T-Coin (traffic coin), that is used to reward the vehicles for their positive attitude. T-Coin is also used in the tender between vehicles to manage the road reservation process. The proposed system uses dynamic pricing to adapt to peak-hour traffic congestion. Using simulated traffic on a real map of Beijing city, we prove the usefulness of T-Coin as a traffic congestion pricing system.

The occurrence of congestion has an extremely deleterious impact on the performance of Wireless Sensor Networks (WSNs). This article presents a novel protocol, named COALA (COngestion ALleviation and Avoidance), which aims to act both proactively, in order to avoid the creation of congestion in WSNs, and reactively, so as to mitigate the diffusion of upcoming congestion through alternative path routing. Its operation is based on the utilization of an accumulative cost function, which considers both static and dynamic metrics in order to send data through the paths that are less probable to be congested. COALA is validated through simulation tests, which exhibit its ability to achieve remarkable reduction of loss ratios, transmission delays and energy dissipation. Moreover, the appropriate adjustment of the weighting of the accumulative cost function enables the algorithm to adapt to the performance criteria of individual case scenarios.

Traffic congestion experience in urban areas has negative impact on our daily lives by consuming our time and resources. Intelligent Transportation Systems can provide the necessary infrastructure to mitigate such challenges. In this paper, we propose a novel and scalable solution to model, store and control traffic data based on range query data structures (K-ary Interval Tree and K-ary Entry Point Tree) which allows data representation and handling in a way that better predicts and avoids traffic congestion in urban areas. Our experiments, validation scenarios, performance measurements and solution assessment were done on Brooklyn, New York traffic congestion simulation scenario and shown the validity, reliability, performance and scalability of the proposed solution in terms of time spent in traffic, run-time and memory usage. The experiments on the proposed data structures simulated up to 10,000 vehicles having microseconds time to access traffic information and below 1.5 s for congestion free route generation in complex scenarios. To the best of our knowledge, this is the first scalable approach that can be used to predict urban traffic and avoid congestion through range query data structure traffic modelling.

In recent years, a new paradigm of Content-Centric Networks has emerged, which employs the prefix content name for addressing in the context of Named Data Networking (NDN). In conventional networks, subscribers send interest packets, and publishers generate related content and publish it to the requesters. In NDN, Caching Routers (CRs), as well as publishers, can also propagate data packets toward the requesters. The NDN network flow has two opposite sides: (1) the interest packet flow, known as interest forwarding on the upstream side, (2) the content flow, known as data publishing, on the downstream side. High traffic volume can be created by the increasing number of requested packets and associated data that are leading to bottlenecks in some parts of the networks. Therefore, congestion control and avoidance are significant issues in NDN. A considerable number of congestion control methods employ forwarding interest rate adjustment coming through the forwarding side. Nevertheless, the congestion can be prevented on the data publishing side using an efficient cache placement method. Congestion avoidance is a process for controlling congestion and balancing traffic loads to make the network effective. In this paper, a Dynamic Cache Placement (DCP) method is proposed to provide congestion avoidance by dynamically relocating the content of the CRs according to the traffic volume pattern and the link capacity. The DCP method distributes the popular data to the network regions with less traffic load and more accessible routers to balance the congested routers’ traffic load. The DCP is implemented in the ndnSIM network simulator, and its performance is compared to the conventional method. Simulation results show that DCP is a fair and robust cache placement design, which successfully avoids congestion inflows with highly varying traffic demand. The proposed techniques employed in the DCP method improve the network performance in dynamic circumstances of the network.

The aim of this paper is to determine the performance enhancement of the network for eliminating congestion through the use of the benefits of algorithms. These algorithms can be used in hindering the congestion which may occur within the network that relies on a Transmission Control Protocol (TCP) protocol. This paper specifically focuses on demonstrating the function of TCP, and most especially the four (slow start, congestion avoidance, fast retransmit and fast recovery algorithms) that are applied in the control of congestion. In the study, the varying effects of connection link from PPP DS1 to PPP DS3 on the network's performance are investigated.