Alleviate Network Congestion Research Articles

Reliability impact of dynamic thermal line rating and electric vehicles on wind power integrated networks

Wind power, as a sustainable clean energy source, has been widely used in power systems. However, the intermittency of wind power poses a threat to the reliability of these systems. One solution to address this issue is the integration of electric vehicle (EV) scheduling. By using EVs with Vehicle-to-Grid (V2G) technology, grid reliability can be improved: EVs can charge when there is excess wind power and discharge when there is a shortage. Another technology that enhances wind power system reliability is Dynamic Thermal Line Rating (DTLR). The DTLR system increases transmission line capacity based on actual weather conditions, helping to alleviate network congestion. Despite the benefits of these technologies, their coordinated operation has not been jointly modeled for reliability purposes before. This study presents, for the first time, a reliability model for the coordinated operation of these two technologies and introduces a new reliability index, the Smart Regulation Reliability Index (SRRI). Simulations were conducted using the IEEE 24-bus reliability test system. The results show that the coordinated operation of these technologies reduces Expected Energy Not Supplied (EENS) by nearly 28.8 % and increases SRRI by about 12.9 %.

AI/ML-based services and applications for 6G-connected and autonomous vehicles

AI and ML emerge as pivotal in overcoming the limitations of traditional network optimization techniques and conventional control loop designs, particularly in addressing the challenges of high mobility and dynamic vehicular communications inherent in the domain of connected and autonomous vehicles (CAVs). The survey explores the contributions of novel AI/ML techniques in the field of CAVs, also in the context of innovative deployment of multilevel cloud systems and edge computing as strategic solutions to meet the requirements of high traffic density and mobility in CAV networks. These technologies are instrumental in curbing latency and alleviating network congestion by facilitating proximal computing resources to CAVs, thereby enhancing operational efficiency also when AI-based applications require computationally-heavy tasks. A significant focus of this survey is the anticipated impact of 6G technology, which promises to revolutionize the mobility industry. 6G is envisaged to foster intelligent, cooperative, and sustainable mobility environments, heralding a new era in vehicular communication and network management. This survey comprehensively reviews the latest advancements and potential applications of AI/ML for CAVs, including sensory perception enhancement, real-time traffic management, and personalized navigation.

Quality of Service and Congestion Control in Software-Defined Networking Using Policy-Based Routing

Managing queuing delays is crucial for maintaining Quality of Service (QoS) in real-time media communications. Customizing traditional routing protocols to meet specific QoS requirements—particularly in terms of minimizing delay and jitter for real-time media—can be both complex and time-intensive. Furthermore, these protocols often encounter challenges when adapted for vendor-specific hardware implementations. To address these issues, this paper leverages the programmable features of Software-Defined Networking (SDN) to simplify the process of achieving user-defined QoS, bypassing the limitations of traditional routing protocols. In this work, we propose a policy-based routing module that integrates with traditional routing protocols to ensure QoS for real-time media flows. QoS is achieved by rerouting the flow along a new low-latency path calculated by the proposed module when the queuing delay exceeds a certain threshold. The experimental results demonstrate that the proposed solution significantly enhances the performance of traditional routing protocols within an SDN framework, effectively reducing the average end-to-end delay by 80% and total packet loss by 73%, while also improving jitter and alleviating network congestion.

Sampled-Data H∞ Dynamic Output-Feedback Control for NCSs With Successive Packet Losses and Stochastic Sampling.

The problem of sampled-data H∞ dynamic output-feedback control for networked control systems with successive packet losses (SPLs) and stochastic sampling is investigated in this article. The aim of using sampled-data control techniques is to alleviate network congestion. SPLs that occur in the sensor-to-controller (S-C) and controller-to-actuator (C-A) channels are modeled using a packet loss model. Additionally, it is assumed that stochastic sampling follows a Bernoulli distribution. A model is established to capture the stochastic characteristics of both the SPL model and stochastic sampling. This model is crucial as it allows us to determine the probability distribution of the sampling interval between successive update instants, which is essential for stability analysis. An exponential mean-square stability condition for the constructed equivalent discrete-time stochastic system, which also guarantees the prescribed H∞ performance, is established by incorporating probability theory. The desired controller is designed using a step-by-step synthesis approach, which may offer lower design conservatism compared to some existing methods. Finally, our designed approach using a networked F-404 engine system model is validated and its merits relative to existing results are discussed. The proposed method is finally validated by employing a networked model of the F-404 engine system. Furthermore, the advantages of our method are presented in comparison to previous results.

Triplex event-triggered recursive quantizer-based networked control system under communication delay and packet dropouts

In light of network congestion, communication delay, and packet dropouts, a triplex event-triggered recursive quantizer-based networked control system (TERQNCS) is proposed in this paper. To constrain quantization error while alleviating network congestion, the recursive quantizer is constructed in the TERQNCS. Considering it is insufficient to adequately alleviate network congestion only with either the quantizer or the event trigger, a triplex event-triggered mechanism is designed through the recursive quantizer. For revealing the impacts of communication delay and packet dropouts, four Bernoulli random variables are adopted to model the network channels in the TERQNCS. Then, the model-based predictive controller is designed by virtue of the predictive states, estimated states, and Bernoulli-based model. The proposed TERQNCS can achieve the desired system performance under communication delay and packet dropouts while adequately alleviating network congestion and constraining quantization error. The stability of the TERQNCS is proved by theoretical analysis. Besides, the advantages of the TERQNCS are substantiated by a tunnel diode circuit networked control system.

Hierarchical control for collaborative electric vehicle charging to alleviate network congestion and enhance EV hosting in constrained distribution networks

This paper introduces a novel hierarchical architecture aimed at enhancing coordination between distribution system operators and electric vehicle aggregators in order to minimize Electric Vehicle (EV) charging costs for users while optimizing EV hosting capacity to alleviate network congestion. Real-world distribution networks are employed to evaluate EV charging strategies and their impact on medium and low-voltage networks. Two distinct EV charging optimization strategies are proposed to ensure fair power allocation among EV Aggregators (EVAs), alleviating congestion while managing EV charging power efficiently. Results demonstrate that the proposed collaborative EV charging effectively flattens the load curve, reducing peak power and avoiding grid congestion. The main findings underscore the importance of incentivizing EV flexibility to support Distribution System Operator (DSO) objectives beyond static tariffs. Furthermore, a battery degradation model is introduced into the optimization problem, reducing high currents and capacity decay. Despite capturing a higher mean electricity price, the total cost of EV charging is reduced.

Mobility-aware proactive video caching based on asynchronous federated learning in mobile edge computing systems

With the rapid advance of the Internet of Things (IoT) and the surging user-centric smart devices, video services such as short videos, real-time video streaming, and Virtual Reality (VR) gaming are emerging. Traditional cloud-centric caching is no longer able to satisfy the low-latency needs of mobile user groups. Predicting the most popular content in advance and caching it on edge devices such as small base stations (SBS) is a promising solution to alleviate network congestion and supply better Quality of Service (QoS) for mobile users. However, frequently changing popular content, limited edge caching resources, privacy of user data, and user mobility pose significant challenges to edge video caching. To tackle these challenges, we propose an efficient asynchronous federated learning (AFL)-based mobility-aware proactive video caching scheme (AF-MPVC), which significantly improves the overall network performance while preventing the risk of user data leakage. First, we model the proactive video caching problem in mobile edge computing systems and consider the impact of user mobility on AFL, representing the problem as an autoencoder (AE) model loss minimization problem. Then, a proactive video caching algorithm is proposed based on a filtering model, which uses the hidden features obtained from the trained AE model to compute the user similarity to find the popular videos that are most likely to be accessed by users and combine them with the most frequently requested videos. Meanwhile, we take into account the short duration of high mobility users staying on the current SBS and cache some videos to neighboring edge servers to have more redundant space to store more less popular videos. Finally, experiments on three real-world datasets demonstrate that the presented caching scheme outperforms other baseline caching schemes with respect to cache hit rate and latency reduction.

An approach for offloading with multi-hop considerations in an RSU signal overlay setting

In recent years, significant advancements in vehicle technology have spurred growing interest in Vehicular Ad hoc Networks (VANETs). This interest is driven by concerns for road safety and the need to alleviate network congestion, leading to the emergence of Intelligent Transport Systems (ITS). ITS focuses on improving road traffic management and safety through the utilization of wireless and mobile network communication technologies. VANETs play a pivotal role within the realm of ITS, facilitating tasks such as enhancing road safety, traffic monitoring, and ensuring passenger comfort by mitigating accidents and congestion. These objectives rely on the timely and accurate delivery of data to vehicle agents and relevant authorities, facilitated by reliable VANETs and Road Signal Units (RSUs). Achieving this necessitates identifying optimal routes with minimal distance, high radio access, and quality-awareness levels. To address these objectives, this study proposes the utilization of the Congestion Network with Predicted K-means multi-hop RSU algorithm (CN-MHMR) to enhance vehicular networking and communication. This algorithm facilitates efficient node transfer from base nodes to destination nodes via the shortest and energy-efficient paths, thereby enabling viable and reliable vehicular communications. The performance of the proposed model was evaluated based on various metrics, including energy consumption, throughput, delay, packet delivery ratio, accuracy, precision, and recall values.

SRA-E-ABCO: terminal task offloading for cloud-edge-end environments

AbstractThe rapid development of the Internet technology along with the emergence of intelligent applications has put forward higher requirements for task offloading. In Cloud-Edge-End (CEE) environments, offloading computing tasks of terminal devices to edge and cloud servers can effectively reduce system delay and alleviate network congestion. Designing a reliable task offloading strategy in CEE environments to meet users’ requirements is a challenging issue. To design an effective offloading strategy, a Service Reliability Analysis and Elite-Artificial Bee Colony Offloading model (SRA-E-ABCO) is presented for cloud-edge-end environments. Specifically, a Service Reliability Analysis (SRA) method is proposed to assist in predicting the offloading necessity of terminal tasks and analyzing the attributes of terminal devices and edge nodes. An Elite Artificial Bee Colony Offloading (E-ABCO) method is also proposed, which optimizes the offloading strategy by combining elite populations with improved fitness formulas, position update formulas, and population initialization methods. Simulation results on real datasets validate the efficient performance of the proposed scheme that not only reduces task offloading delay but also optimize system overhead in comparison to baseline schemes.

Demonstration-guided deep reinforcement learning for coordinated ramp metering and perimeter control in large scale networks

Effective traffic control methods have great potential in alleviating network congestion. Particularly, in an urban network consisting of heterogeneous roads (e.g., freeways and urban roads), how to integrate and coordinate control policies on different roads is a critical issue in large-scale networks. This study addresses this question from two aspects: modeling and control. From the modeling aspect, we formulate the hybrid traffic modeling in heterogeneous networks with the Asymmetric Cell Transmission Model (ACTM) for freeways and the generalized bathtub model for urban roads. For the control aspect, this study considers two representative control approaches: ramp metering for freeways and perimeter control for urban roads, and we aim to develop a deep reinforcement learning (DRL)-based coordinated control framework for large-scale networks. However, there are two significant challenges in the coordinated control in large-scale networks with DRL methods: non-stationary environment and large search space. To address both issues, we incorporate the demonstration to guide the DRL method for better convergence by introducing the concept of “teacher” and “student” models. The teacher models are traditional controllers that provide control demonstrations. For instance, ALINEA and Gating are two representative feedback controllers for ramp metering and perimeter control which can be “teacher” models. The student models are DRL methods, which learn from teachers and aim to surpass the teachers’ performance. Additionally, we develop a parallel training scheme to accelerate the proposed DRL method. To validate the proposed framework, we conduct two case studies in a small-scale network and a real-world large-scale traffic network in Hong Kong. Numerical results show that the proposed DRL method outperforms demonstrators as well as DRL methods, and the coordinated control is more effective than just controlling ramps or perimeters respectively. The research outcome reveals the great potential of combining traditional controllers with DRL for coordinated control in large-scale networks.

Regional route guidance with realistic compliance patterns: Application of deep reinforcement learning and MPC

Solving link-based route guidance problems for large-scale networks is computationally challenging and faces practical issues, such as spatial–temporal data coverage. Thus, regional route guidance has emerged as a promising strategy, which utilizes regional approaches (i.e., network-level macroscopic traffic models) to capture traffic dynamics. Existing regional route guidance models have mainly focused on macroscopic flows and aggregated splitting rates, employing uniformly sampled vehicles as controllable targets. These models, however, overlook the inherent nature of individual drivers’ compliance. It may deteriorate the guidance performance as existing route guidance models cannot effectively generate customized route plans for compliant vehicles. This paper aims to introduce a regional route guidance framework with the utilization of different solution approaches, i.e. model-based and data-driven, considering the compliance pattern. In particular, MPC-based and deep reinforcement learning-based schemes are proposed for the information service provider to send customized route plans to compliant individuals. Within each solution approach, different route guidance strategies are developed for specific purposes and evaluated through numerical experiments under low- and high-congestion scenarios. Besides, the trade-off between total travel time and average trip length is studied, wherein the balance reward in a deep reinforcement learning-based approach enables controllable agents to reduce trip costs while compromising partial system utility. The findings indicate the effectiveness of the proposed strategies in alleviating network congestion. Additionally, multi-agent approaches outperform single-agent ones, highlighting the benefits of cooperative decision-making in traffic management.

Swapping-Based Entanglement Routing Design for Congestion Mitigation in Quantum Networks

The quantum network is designed to connect numerous quantum nodes and support various ground-breaking quantum applications. Most of these applications require communicating parties to share entangled pairs. Therefore, entanglement routing, a technology distributing entangled pairs between distant quantum nodes, plays a vital role in realizing quantum networks’ capability. However, due to the limitation of quantum memory size and quantum decoherence, the entangled pairs shared by adjacent quantum nodes can hardly satisfy concurrent entanglement routing requests, thus leading to severe network congestion. In this paper, we propose a novel congestion mitigation (CM) scheme to tackle such bottleneck problems. The basic idea of CM is to “recycle” idle link-level entanglement resources from well-resourced links to bottleneck links utilizing a unique enabling technology of quantum networks, called entanglement swapping. CM can increase the capacity of each bottleneck link, thus overcoming resource limitations to improve resource utilization and network throughput. To complete our work, we also propose a swapping-based entanglement routing design, including path selection and resource allocation algorithms. Extensive simulations show that our design can significantly alleviate network congestion and improve the request service rate of quantum networks compared to the traditional entanglement routing designs.

Hop Count Based Interest Selection and Content Forwarding Scheme for Vehicular Named Data Networks

Vehicular Named Data Networks (VNDN) face challenges in efficiently disseminating content due to high mobility and intermittent connectivity. To address these challenges, a Hop Count based Interest Selection and Content Forwarding (HISCF) scheme for VNDNs is proposed. The scheme focuses on mitigating interest flooding, reducing data packet duplication, and alleviating network congestion. HISCF consists of two components: interest selection and content forwarding. The selection process chooses a vehicle based on hop count and Interest Satisfaction Ratio (ISR) to forward the interest packet. Content forwarding is performed considering a hop count limit and pending interests, ensuring efficient content delivery. The HISCF scheme is evaluated using extensive simulations in ns-3 with ndnSIM. Performance metrics such as Data Packet Replication Count (DPRC), total number of interest packets forwarded, Interest Response Time (IRT) and routing overhead are analysed. Results show that HISCF outperforms naïve VNDN, reducing DPRC, minimizing interest packets forwarded, and decreasing average IRT. The findings demonstrate that HISCF effectively mitigates interest broadcast storms, reduces data packet duplication, and improves content delivery efficiency in VNDNs. This study contributes to VNDN research advancement and provides insights for designing effective content forwarding mechanisms in vehicular networks.

End-to-end active queue management with Named-Data Networking

The innovative information-based Named-Data Networking (NDN) architecture provides a good opportunity to rethink many of the design decisions that are taken for granted in the Internet today. For example, active queue management (AQM) tasks have been traditionally implemented in the routers to alleviate network congestion before their buffers fill up. However, AQM operations could be performed on an end-to-end basis by taking advantage of NDN features. In this paper, we provide an implementation of an AQM algorithm for the NDN architecture that we use to drive a classical AIMD-based congestion control protocol at the receivers. To accomplish this, we take advantage of the 64-bit Congestion Mark field present in the link layer of NDN packets to encode both rate and delay information about each transmission queue along a network path. In order to make the solution scalable, this information is delivered stochastically, guaranteeing that receivers get accurate and updated information about every pertinent queue. This information is enough to implement the well-known controlled delay (CoDel) AQM algorithm. Simulation results show that our client-located CoDel implementation is able to react to congestion when the bottleneck queuing delay surpasses the 5ms target set by the usual in-network CoDel implementation and, at the same time, get a fair and efficient share of the available transmission capacity.

Adaptive event‐triggered dynamic output feedback control for networked control systems under hybrid attacks

AbstractThis paper concentrates on network congestion and security issue for networked control systems under the hybrid attacks. The hybrid attacks including deception attack, replay attack and denial‐of‐service attack are modelled as Bernoulli random process. A general form adaptive event‐triggered scheme (AETS) under the hybrid attacks is designed to alleviate the network congestion and save communication resources utilizing adaptive threshold and the weighted average of data packets. Meanwhile, the security issue under the hybrid attacks is addressed by a dynamic output feedback controller (DOFC) based on the AETS. Moreover, the sufficient conditions are obtained by a piecewise Lyapunov function to guarantee that the closed‐loop system is exponentially mean‐square stable. A practical experiment on networked motor control system verifies the effectiveness of the proposed scheme. The proposed scheme can not only save communication resources to further alleviate network congestion, but also defense the hybrid attacks in the network.

Review of the Impact of Electric Vehicle Charging on Distribution Networks

Abstract: Electric vehicles (EVs) have gained significant popularity, leading to concerns about their impact on distribution networks and the integration of renewable energy sources.In response, researchers have proposed various strategies and methodologies. These include adaptive charging coordination strategies that aim to minimize the influence of EV chargingon distribution networks while considering uncertainties in renewable energy generation and charging demands. Optimizationbased approaches have been employed to balance loads, alleviate network congestion, and coordinate EV charging schedules with renewable energy generation and distribution network demand. The studies have also highlighted potential issues such as distribution system overloading, voltage stability problems, and the need to account for different charging infrastructure scenarios and standards. To address these challenges, control strategies integrating EV charging and battery energy storage systems (BESS) have been proposed. These strategies aim to reduce peak power demands, optimize the utilization of renewable energy sources, and shift charging loads away from peak periods. How- ever, there are still gaps to be addressed, including scalability forlarge-scale deployment, user convenience, and economic viability.Further research is needed to explore these areas and consider the impact of user behavior, smart grid technologies, and cost- effectiveness in accommodating EV charging and plug-in hybrid electric vehicles (PHEVs) in residential distribution grids

GraphSAGE-Based Multi-Path Reliable Routing Algorithm for Wireless Mesh Networks

Wireless mesh networks (WMN) promise to be an effective way to solve the “last mile” access problem on the Internet of Things (IoT) and the key to next-generation wireless networks. The current routing algorithms of WMN are difficult to adapt to complex environments and guarantee the reliable transmission of services. Therefore, this paper proposes a reliable routing algorithm that combines the improved breadth-first search and a graph neural network, namely GraphSAGE. The algorithm consists of two parts: (1) A multi-path routing algorithm based on the improved breadth-first search. This algorithm can continuously iterate link information based on network topology and output all shortest paths. (2) A GraphSAGE-based performance optimization algorithm. This algorithm creates a method to generate network labels for supervised training of GraphSAGE. Then, the network labels and GraphSAGE are used to learn graph features to obtain the value of network performance for each shortest path. Finally, the path with the best network performance is selected for data transmission. Simulation results show that in the face of complex environments, the proposed algorithm can effectively alleviate network congestion, improve throughput, and reduce end-to-end delay and packet loss rate compared with the traditional shortest-path routing algorithm and the Equal-Cost Multi-Path routing (ECMP).

Scheduling strategy for networked control system based on variable sampling period and deadband feedback

SummaryA scheduling strategy of variable sampling period combined with deadband feedback for networked control system is proposed. Variable sampling period algorithm can allocate a reasonable sampling period to each controlled loop according to the network utilization and packet transmission time. Deadband feedback algorithm can alleviate network congestion by appropriately adjusting the packets in the network when the networked control system cannot be scheduled. According to the actual overload and utilization of the network, the designed scheduling strategy dynamically adjusts the sampling period and priority, and improves the performance of the system combined with deadband feedback. Based on the TrueTime platform, the proposed scheduling strategy is verified on a three controlled loops networked control system with interference nodes and limited network resources. The simulation results demonstrate that the designed scheduling strategy can overcome the uncertainty of the upper bound of network resources, improve output control performance, reduce integral absolute error value of the controlled loop, and shorten the packet transmission time. The overall control performance of the system is improved. The designed scheduling strategy is effective.

STALB: A Spatio-Temporal Domain Autonomous Load Balancing Routing Protocol

Due to vehicle mobility, the topology of Vehicle Ad-hoc Networks (VANETs) may change dynamically. High mobility, limited bandwidth, and dynamic network topology pose challenges for communication in the Internet of Vehicles (IoVs). Literature works have attempted to promote efficient (e.g., lower end-to-end latency) message forwarding. However, due to the uncertain direction of message forwarding and vehicle mobility, they suffer from unreachable destinations and unstable connections. This paper explores the efficient method of message forwarding to alleviate network congestion in IoVs. We propose a Spatio-Temporal domain Autonomous Load Balancing (STALB) routing protocol. Specifically, STALB is a trajectory-based method for controlling the direction of message forwarding. STALB can significantly reduce the end-to-end latency and overload ratio, since it considers the local status of network relay devices (i.e., buffer score, congestion status) from the spatio-temporal domain. Then, we present a path reconstruction mechanism, which ensures that messages are forwarded to destinations within limited Time-To-live (TTL). Extensive simulation results show that STALB significantly outperforms other baseline methods (BSaW, TDOR, and TBHGR) regarding overhead ratio, average delivery latency, and average buffer time. Especially, the delivery rate of STALB can reach 99.9% under the sparse network scenario (4,500 messages), at least 0.7% higher than other baseline methods. Similarly, the average delivery delay of STALB is at least 84.31% lower than that of other baseline methods under the dense network scenario (18,000 messages).

A secure event-based quantized networked control system

This paper focuses on network time delay, network congestion, and network security in the networked control system. A secure event-based quantized networked control system (SEQNCS) is proposed to address these problems. The predictive method in the SEQNCS actively compensates time delay both in the forward channel and feedback channel according to the previous states and control inputs. Both the event-driven strategy and quantizer are introduced into the SEQNCS to alleviate network congestion by the event trigger conditions and quantization density. The encrypted encoding quantizer is constructed in the SEQNCS to implement the protection of data security under deception attacks while quantifying the transmitted data. The proposed SEQNCS achieves desired system performance of the networked control system under time delay and deception attacks while alleviating network congestion. The stability of the SEQNCS is proved by the piecewise linear model approach. Practical experiments are implemented to demonstrate the effectiveness of the proposed SEQNCS.