Channel Congestion Research Articles

Dynamic clustering based risk aware congestion control technique for vehicular network

In order to improve road safety and offer extra services to cars and their users, vehicular ad hoc networks, or VANETs, are essential parts of intelligent transportation systems (ITS). The primary aim of this research is to suggest and assess a new approach for mitigating network congestion in VANET technology through the implementation of dynamic grouping of vehicles for safety (DGVS). To do this, virtual regions are created around cars using the DBSCAN and K Mean method. This allows vehicles to communicate directly with others within the same DGVS, eliminating the need to broadcast messages across the entire network.The ultimate goal is to drastically decrease traffic while preserving vital data transfer for VANET traffic control and safety. The suggested technique determines the optimal transmission rate in accordance with the existing channel circumstances, yielding a balanced performance concerning both packet delivery and channel congestion. With this novel method, cars may only talk to other vehicles that are in the same DGVS, thus there’s no need to broadcast messages to the whole network. With the use of this technique, the efficacy of DGVS in reducing VANET congestion and enhancing network performance will be thoroughly assessed. The study’s conclusions demonstrate how well the suggested dynamic grouping of vehicles for safety (DGVS) technique works to reduce VANET congestion. It was shown through simulation-based studies that, in comparison to current approaches, DGVS dramatically decrease network congestion, resulting in notable gains in network performance and decreased communication delay. Additionally, the study found a number of possible uses for DGVS in the transportation industry, such as emergency response, traffic management, and accident prevention.

Investigation on cellular LTE C-V2X network serving vehicular data traffic in realistic urban scenarios

The need for reliable vehicular communication is an essential precondition of future Cooperative Intelligent Transportation Systems (C-ITS). The C-ITS applications and their diverse requirements necessitate a hybrid approach that combines ad hoc and cellular networks to support seamless and robust connectivity. While the research showed tremendous potential, the successful adaptation of the hybrid approach depends on the optimal understanding of its impact on network and protocol performance in an actual reference scenario. This work, which has practical implications for the field, focuses on creating a realistic representation of the dual-link-enabled Hybrid Vehicular Network (HVN) in the simulator. Several enhancements were implemented into a network simulator, including multiple Radio Access Technologies (multi-RAT) support, models for path loss predictions, static subscribers, background interference, and SINR throughput mapping within a multi-cell environment. We then presented a parameterized data traffic steering algorithm that enables switching between Dedicated Short-Range Communication (DSRC) and Long Term Evolution (LTE) RAT based on channel congestion and cell load conditions. The simulation result shows the high potential of a dual-link-enabled hybrid approach and its implications on both networks under realistic vehicular networking conditions. The investigations reveal that an intelligent decision to steer traffic can target balanced connectivity and highly reliable vehicular communication at a reduced network load.

Enhancing energy efficiency in tyre pressure and temperature monitoring systems

This study addresses the pivotal challenge of enhancing power efficiency in tyre pressure and temperature monitoring systems (TPMS) for heavy vehicles and trailers. Employing field-programmable gate arrays (FPGA) and adaptive channel frequency hopping in bluetooth low energy (BLE) communication, the research focuses on mitigating power consumption issues specific to heavy vehicles with multiple tyres. The proposed solution incorporates strategic BLE channel blocking and adaptive frequency hopping on the FPGA platform to alleviate channel congestion and interference, ultimately reducing TPMS power consumption. The FPGA's adaptability tailors frequency hopping strategies to automotive TPMS nuances, optimizing channel selection and minimizing energy-intensive processes. Empirical results showcase a significant reduction in power consumption, with the TPMS operating at 100 MHz during active mode consuming 66 mW, dropping to 11 mW in sleep mode, and reaching 0 mW in hibernate mode for the majority of operational time. This research establishes a practical FPGA-based approach for power optimization in commercial TPMS, promising heightened reliability, safety improvements, and environmental impact reduction in the automotive sector.

Comparative analysis of clustering algorithm and improved algorithm application in shipping congestion

In recent years, due to the lack of reasonable planning of ship operation routes, shipping congestion accidents have been increasing, seriously restricting port development and channel operation, posing a great threat to ship navigation safety and greatly restricting the development prospects of the shipping industry. This paper aims to compare and analyze the application of the clustering algorithm and its improved algorithm in solving port and waterway congestion problems. By analyzing the literature on the use of clustering algorithms to solve shipping congestion problems, and comparing the advantages and disadvantages of multiple clustering algorithms in solving problems, this paper concludes that Partition-based Methods are more suitable for identifying port congestion and the improved Fuzzy DBSCAN algorithm for identifying channel congestion. The research in this paper will help to select clustering algorithms when solving shipping congestion problems in the future.

Machine Learning-Based Risk-Aware Congestion Control Scheme for Minimization of Information Loss in Dense VANET Environment

Vehicular Ad-Hoc Network is one of the most growing research fields and has become a promising topic for researchers. Day by day, vehicle density increases, causing very serious issues like road accidents and traffic jams. These issues can be minimized by a periodic exchange of awareness messages among the vehicles. Traffic density increases transmission of awareness messages, which causes channel congestion and degrades safety services, so congestion control schemes must ensure that the channel load should be below a particular threshold, boost the quality of services and minimize information loss and delay in the network. This paper proposes a machine learning-based risk-aware congestion control (MLB-RACC) scheme which is an efficient congestion control scheme based on the modified [Formula: see text] -means machine learning algorithms. MLB-RACC technique processes the input data and helps to improve the quality of services in the automotive industry. It works in three phases: detecting congestion, clustering of vehicles and controlling the broadcasting of awareness messages for decreasing the channel load. MLB-RACC scheme works by grouping the nearest vehicles and the number of groups or clusters is decided on the basis of transmission range/transmission power. The group member selects adaptive transmission rate by looking at the channel busy ratio (CBR). In this technique, message generation rate is controlled at two levels: first is at the clustering level and second is during the checking of CBR. The scheme is implemented through MATLAB, NS2 and the SUMO platform and provides evidence for the minimization of information loss by analyzing the throughput, packet loss and end-to-end delay in comparison to ordinary decentralized congestion control (DCC) technique.

Predicting CAM generation times through machine learning for cellular V2X communication

In the narrow Intelligent Transportation System (ITS) band, avoiding wireless channel congestion is essential. Reporting vehicle kinematics in the Cooperative Awareness Message (CAM) only when there are notable changes in vehicle dynamics is a standardized approach to reducing bandwidth usage of periodic CAM messages that are exchanged between vehicles, and is called the CAM generation rule. However, in cellular vehicle-to-everything (V2X) communication, aperiodicity due to frequent omissions of periodic CAM raises problems of resource waste and instability in resource scheduling. The problem can be solved by reserving a resource only for actual CAM transmission times in the future. This article demonstrates that a neural network-based scheme can predict the next CAM generation times at an average accuracy of over 94%, which can be utilized for resource reservation under the CAM generation rule.

Adaptive fault-tolerant control on complex networks with semi-Markov jump topology under random packet loss

ABSTRACT In this paper, semi-Markov process and adaptive fault-tolerant law are used to study the synchronization of complex networks in the presence of topology mutations and actuator failures. Firstly, the semi-Markov jump process, which is more practical than the traditional Markov jump process, is used to simulate the topology change of complex networks. Secondly, by designing adaptive fault-tolerant controller, the influence of actuator fault and interference on the system is eliminated. Then, considering the random packet loss caused by network channel congestion and limited bandwidth resources, a control strategy is developed to achieve convergence of the synchronisation error system. Finally, a numerical example and a practical example of Chua's circuit are given to verify the validity of the proposed theory.

Efficient Clustering of Visible Light Communications in VANET

The deployment of vehicular ad hoc network (VANET) is crucial to the development of autonomous vehicles. Radio frequency (RF) technology has been employed to transmit messages between vehicles and infrastructure in VANET. However, the limited RF bands may cause interference when vehicles transmit messages in a high-density environment. Moreover, when numerous vehicles transmit messages to the infrastructure at the same time, the simultaneous transmissions may cause channel congestion. While the issue of signal interference can be solved by the techniques of Visible Light Communication (VLC), vehicle clustering can be employed to improve the transmission performance of vehicles. VLC is an emerging technology with the advantage of immunity to electromagnetic interference. The technique of vehicle clustering categorizes vehicles into different sets, where each set has a leader for intra-cluster messaging. In this work, we present a clustering algorithm for VANET based on VLC. Our algorithm estimates the positions of vehicles based on their current movements. Then, it selects cluster heads based on the number of neighboring vehicles and generates clusters. We evaluate the performance of our scheme for both urban and highway scenarios. The simulation results show that the proposed algorithm can minimize the number of clusters and improve the transmission data rate for vehicles.

Snake Optimization Technique for Spectrum Handoff in Cluster based Cognitive Radio Network

In Cognitive Radio (CR) networks, the use of Secondary Users (SU) in the spectrum has an undesirable effect on spectrum handoff, which causes a handoff delay. The handoff procedure can result in service outages and considerable transmission quality delays, making it a regular source of concern for the SU. An effective spectrum handoff strategy that utilizes the Spectrum Binary Snake Optimization (SBSO) algorithm and the M/G/1 queuing model has been proposed in this study. The use of Cluster Based Cooperative Spectrum Sensing improves SU performance and reduces channel congestion. In order to report the active and inactive channels in the spectrum, the cluster head is connected to the SU base station, and a decision report is subsequently generated by the fusion center. With the use of a bitwise and mutation operator format, SBSO reduces the overall service time required for handoff in the approach that is being proposed. The proposed methodology also provides a framework for observing how primary user activity and spectrum handoff delays behave in the presence of potential interruptions in a CR network. The simulation model of the proposed work optimizes the packet delivery ratio with the three benchmark functions, and provides optimal handoff, and is compared to SBSO and other models that offer a better trade off over delay achievement.

Event-triggered-based [formula omitted] control for Markov jump cyber-physical systems against denial-of-service attacks

This paper is concerned with the event-triggered-based H∞ control problem for Markov jump cyber-physical systems against denial-of-service attacks. The uniform upper and lower bounds are used to describe the characteristics of aperiodic denial-of-service attacks. An event-triggered scheme is introduced to reduce the waste of resources and avoid communication channel congestion. Based on multi-Lyapunov functions, sufficient conditions are proposed to guarantee the asymptotic mean-square stability and H∞ performance of the switched closed-loop Markov jump cyber-physical systems. By solving linear matrix inequalities, the event-triggered-based controller gain matrices, and H∞ performance index are obtained. In the simulation, an F-404 aircraft engine system is employed to clarify the validity of the proposed event-triggered-based controller design approach in face of denial-of-service attacks.

Balanced Off-Chain Payment Channel Network Routing Strategy Based On Weight Calculation

AbstractOff-chain payment channel network is an effective solution to deal with low throughput and high load on the blockchain. However, the quality of the routing scheme will directly affect the performance of the off-chain network system. Existing routing schemes cannot select an appropriate transaction path according to the actual needs of user nodes. And they may also lead to network congestion, channel imbalance and node centralization. This paper proposes a new balanced routing selection scheme based on weight calculation called BRBW, which builds a weight model by Analytic Hierarchy Process. BRBW comprehensively considers the channel capacity, handling fee and path length to improve transaction success rate, reduce channel congestion and maintain the long-term sustainability of the payment channel. It uses a modified maximum flow algorithm to find paths with sufficient capacity and selects the transaction path for a large payment through linear programming. Finally, we do some experiments and compare with state-of-the-art approaches in the test environment. Simulation results show that under the same network environment, the payment success rate of the BRBW is above 75%, and the channel utilization rate is about 10% higher than other schemes.

A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET

Vehicular ad hoc networks (VANETs) are crucial components of intelligent transportation systems (ITS) aimed at enhancing road safety and providing additional services to vehicles and their users. To achieve reliable delivery of periodic status information, referred to as basic safety messages (BSMs) and event-driven alerts, vehicles need to manage the conflicting requirements of situational awareness and congestion control in a dynamic environment. To address this challenge, this paper focuses on controlling the message transmission rate through a Markov decision process (MDP) and solves it using a novel reinforcement learning (RL) algorithm. The proposed RL approach selects the most suitable transmission rate based on the current channel conditions, resulting in a balanced performance in terms of packet delivery and channel congestion, as shown by simulation results for different traffic scenarios. Additionally, the proposed approach offers increased flexibility for adaptive congestion control through the design of an appropriate reward function.

TRUCON: Blockchain-Based Trusted Data Sharing With Congestion Control in Internet of Vehicles

The Internet of vehicles (IoV) has a substantial impact on traffic efficiency improvement and accidents avoidance. Due to restricted resources, vehicles must share observed data with RSUs and other vehicles to execute some time-tolerant computing tasks. However, data provided by vehicles cannot always be trusted due to the presence of attackers. Fake messages could have catastrophic ramifications, such as vehicle collisions. Furthermore, extensive data sharing might cause channel congestion, resulting in the loss of vital messages during delivery. To overcome the aforementioned issues, we propose TRUCON, a blockchain-based trusted data sharing mechanism with congestion control in IoV. Firstly, we propose a Kademlia algorithm-based traffic data forwarding method to control channel congestion state. By adjusting the bucket size and distance threshold, source vehicles can limit the number of reference vehicles forwarded. Secondly, we present a cuckoo filter-based traffic data deduplication and discrimination approach. To avoid repetitive sharing, vehicles and RSUs can check their local filters to verify if the current data report has been shared. Based on the foregoing, we propose a blockchain-based trust management mechanism with congestion control. RSUs serve as full nodes while vehicles are light nodes in the blockchain. Finally, we develop a trust management prototype system with congestion control that incorporates both on-chain and off-chain parts. It signifies that our scheme is both feasible and effective.

Communication-Efficient Federated Learning With Gradual Layer Freezing

Federated learning (FL) is a collaborative, privacy-preserving method for training deep neural networks at the edge of the Internet of Things (IoT). Despite the many advantages, existing FL implementations suffer high communication costs that prevent adoption at scale. Specifically, the frequent model updates between the central server and the many end nodes are a source of channel congestion and high energy consumption. This letter tackles this aspect by introducing federated learning with gradual layer freezing (FedGLF), a novel FL scheme that gradually reduces the portion of the model sent back and forth, relieving the communication bundle yet preserving the quality of the training service. The results collected on two image classification tasks learned with different data distributions prove that FedGLF outperforms conventional FL schemes, with data volume savings ranging from 14% to 59% or up to 2.5% higher accuracy.

Efficient Congestion Control With Information Loss Minimization for Vehicular Ad Hoc Networks

The reduction of beacon message rates is of great significance to network congestion control in Vehicular Ad Hoc Networks (VANETs). However, this may cause large traffic information loss and serious traffic accidents. In this paper, we propose an efficient congestion control protocol, which not only alleviates channel congestion but also minimizes information loss by adaptively adjusting the beacon rates. Unlike conventional methods, this protocol investigates beacon transmission from a perspective of information loss, and adopts different beacon rates for different vehicles. First, we design an information loss factor to quantify the information deviation between the original information generated by a vehicle and the actual information received by its neighbor vehicles. Then, based on the information loss factor, we transform the beacon rate adjustment problem into an information loss minimization problem, which is addressed by a greedy beacon-rate adjustment algorithm. In addition, based on the derived different beacon rates, we design a periodicity-based time-slot selection scheme to allocate idle time slots to different vehicles, in order to alleviate transmission collisions. Extensive simulation results show the advantages of our solution in terms of multiple metrics.

Detection and Reporting of Wireless Channel Congestion and Interference in Connected Vehicle Networks

Connected vehicle (CV) wireless networks based on dedicated short-range communications (DSRC), European Telecommunications Standards Institute (ETSI) intelligent transportation systems (ITS-G5), and C-V2X technologies are susceptible to interference from both unintentional emitters and non-CV devices that may be authorized to share the same or adjacent bands. Such interference may lead to unreliable communication and disruption of CV services with a particular impact on safety-related applications. Surprisingly, considering the safety-critical nature of CV applications, there is no simple mechanism for detecting congestion or interference in such networks over wide areas. To address this gap, we propose and demonstrate that both interference and congestion in DSRC and ETSI ITS-G5 networks can be detected simply and inexpensively using capabilities that are already incorporated into the IEEE 802.11p standard, specifically the flags and statistics generated mostly in the physical layer (physical layer convergence procedure and physical medium dependent) state machines. Such a capability could be realized through a relatively minor software upgrade but would resolve a longstanding but underappreciated concern that CV networks are vulnerable to both congestion and a variety of short-range interferers but lack the capability to detect or report this. Although our focus was on DSRC and ITS-G5, similar considerations apply to related schemes such as C-V2X.

Dynamic Beacon Distribution Mechanism for Internet of Vehicles: An Analytical Study

In the last decade, with the arrival of the 5G communication technology and the increasing numbers of vehicles being connected to the internet, conventional vehicle ad-hoc networks (VANETs) are evolving towards the internet of vehicles (IoV), which makes the co-existence of IEEE 802.11p and 5G-based technologies very important for the design of a heterogeneous IoV system that takes advantage of both. The IEEE 802.11p standard is still the best candidate to support direct communications for safety critical services. In fact, both the ETSI ITS-G5 and the IEEE 1609 standard families adopt the IEEE 802.11p standard as a medium access control (MAC) mechanism, and they require vehicles to exchange periodic awareness messages to avoid dangerous situations. When the density of vehicles increases, the MAC layer will suffer from radio channel congestion problems, and this may affect the various VANET applications, especially safety applications. Therefore, the decentralized congestion control (DCC) mechanism has been specified by ETSI to mitigate the channel congestion; this was achieved by adapting the transmission parameters, such as the transmit power and data-rate. However, many research studies have demonstrated limitations and a low performance of DCC, especially when the channel load is extremely high. To deal with this, in this paper, we investigate a new promising technique, called the transmission timing control (TTC), to control the channel load for periodic cooperative awareness. It consists of spreading the transmissions over time in order to avoid contention on the transmission channel. The objective of the paper is to propose an analytical study to calculate the probability of successful transmission using TTC. The demonstrated results show the efficiency of our timing control-enabled scheme to deal with the channel load on top of different conditions.

Analysis of Congestion Control Mechanisms for Cooperative Awareness in IoV Environments

Traditional vehicle ad hoc networks (VANETs) have evolved toward the Internet of Vehicles (IoV) during the past ten years with the introduction of 5G communication technology and the growing number of vehicles linked to the Internet. The coexistence of IEEE 802.11p and 5G becomes critical to build a heterogeneous IoV system that benefits from both technologies, being that the IEEE 802.11p standard remains the best option for direct communications and safety-critical applications. The IEEE 1609 standard family and the ETSI ITS-G5 standard family both use the IEEE 802.11p standard as a MAC mechanism. To avoid dangerous situations, vehicles require the periodic exchange of awareness messages. With the increase in vehicle density, the MAC layer will suffer from radio channel congestion problems, which in turn will have a negative impact on the safety application requirements. Therefore, the decentralized congestion control (DCC) mechanism has been specified by ETSI to mitigate channel congestion; this was achieved by adapting transmission parameters such as transmit power and data rate. However, several studies have demonstrated that DCC has drawbacks and suffers from poor performance when the channel load is very high. This paper investigates a new promising DCC technique called transmission timing control (TTC), to reduce the channel load for periodic cooperative awareness. It consists of spreading the transmissions over time to avoid contention on the transmission channel. The objective of the paper is to propose an analytical study to calculate the probability of successful transmission using TTC. Obtained results showed a convergence between the applied experiments and our mathematical model, achieving an error margin of only 5%, which confirms the validity of the equation proposed.

Prioritization-Driven Congestion Control in Networks for the Internet of Medical Things: A Cross-Layer Proposal

Real-life implementation of the Internet of Things (IoT) in healthcare requires sufficient quality of service (QoS) to transmit the collected data successfully. However, unsolved challenges in prioritization and congestion issues limit the functionality of IoT networks by increasing the likelihood of packet loss, latency, and high-power consumption in healthcare systems. This study proposes a priority-based cross-layer congestion control protocol called QCCP, which is managed by communication devices' transport and medium access control (MAC) layers. Unlike existing methods, the novelty of QCCP is how it estimates and resolves wireless channel congestion because it does not generate control packets, operates in a distributed manner, and only has a one-bit overhead. Furthermore, at the same time, QCCP offers packet scheduling considering each packet's network load and QoS. The results of the experiments demonstrated that with a 95% confidence level, QCCP achieves sufficient performance to support the QoS requirements for the transmission of health signals. Finally, the comparison study shows that QCCP outperforms other TCP protocols, with 64.31% higher throughput, 18.66% less packet loss, and 47.87% less latency.

Learning-Based Off-Chain Transaction Scheduling in Prioritized Payment Channel Networks

Payment channel network (PCN) is one of the promising solutions for scalable blockchains since it shows great potential in improving blockchain network throughput. However, the growing number of transactions and the payment-channel sharing of concurrent transactions can lead to channel congestion. Although many studies have proposed different solutions to solve this problem, they ignore a fact that applications may have different transaction rate requirements at different times. In this paper, we propose a priority-aware PCN to meet the requirements of those transactions. Senders in priority-aware PCNs can specify the priority of their transactions by paying a corresponding forwarding fee on each hop along the transaction path. However, capacity competition occurs on the shared hops. Moreover, we propose a multi-agent DQN-based priority assignment algorithm to address the competition issue and design a PCN simulator for performance evaluation. Simulation results show that our solution can guarantee a high throughput of transactions and assign priorities appropriately to balance the transaction rate and forwarding fee cost. The experimental results demonstrate that the priority scheduling scheme can achieve higher transaction throughput and success ratio than other scheduling methods in a congested PCN environment.