Congestion Control Scheme Research Articles

An Effective Congestion Control Algorithm based on Traffic Assignment and Reassignment in Wireless Sensor Network

Background: It is important to improve the quality of service by using congestion detection technology to find the potential congestion as early as possible in wireless sensor network. Methods: So an improved congestion control scheme based on traffic assignment and reassignment algorithm is proposed for congestion avoidance, detection and mitigation. The congestion area of the network is detected by predicting and setting threshold. When the congestion occurs, sensor nodes can be recovery quickly from congestion by adopting reasonable method of traffic reassignment. And the method can ensure the data in the congestion areas can be transferred to noncongestion areas as soon as possible. Results: The simulation results indicate that the proposed scheme can reduce the number of loss packets, improve the throughput, stabilize the average transmission rate of source node and reduce the end-to-end delay. Conclusion: : So the proposed scheme can enhance the overall performance of the network. Keywords: wireless sensor network; congestion control; congestion detection; congestion mitigation; traffic assignment; traffic reassignment.

Network-Cognitive Traffic Control: A Fluidity-Aware On-Chip Communication

A novel network-on-chip (NoC) integrated congestion control and flow control scheme, called Network-Cognitive Traffic Control (NCogn.TC), is proposed. This scheme is cognizant of the fluidity levels in on-chip router buffers and it uses this measurement to prioritize the forwarding of flits in the buffers. This preferential forwarding policy is based on the observation that flits with higher levels of fluidity are likely to arrive at their destinations faster, because they may require fewer routing steps. By giving higher priority to forward flits in high-fluidity buffers, scarce buffer resources may be freed-up sooner in order to relieve on-going traffic congestion. In this work, a buffer cognition monitor is developed to rapidly estimate the buffer fluidity level. An integrated congestion control and flow control algorithm is proposed based on the estimated buffer fluidity level. Tested with both synthetic traffic patterns as well as industry benchmark traffic patterns, significant performance enhancement has been observed when the proposed Network-Cognitive Traffic Control is compared against conventional traffic control algorithms that only monitor the buffer fill level.

Perimeter traffic control for single urban congested region with macroscopic fundamental diagram and boundary conditions

Macroscopic fundamental diagram (MFD) characterizes a unimodal and low-scatter relationship between traffic accumulation and trip completion rate for homogenous urban network, providing a new perspective for the development of regional congestion control strategies. This paper contributes to the state-of-the-art by proposing a perimeter traffic control strategy for single urban congested region with MFD model and boundary conditions. For an urban protected region, the MFD is introduced to capture network traffic dynamics and optimal capacity. When regional accumulation exceeds the optimal capacity, the restricted inflow distribution considering boundary condition constraints of real-time traffic flows and queue dynamics is used to formulate a perimeter traffic flow control strategy for adjusting traffic signal timing on the periphery of region. To proactively reduce the possibility of queue spillbacks at border links, the dynamic queue-dependent border gated points are also integrated into the proposed control method. The simulation experiment was performed using part of urban network in Jinan, China. The results demonstrate the proposed perimeter control method alleviates the issue of congestion and improves traffic performance in the protected region and at the boundaries.

Joint beacon frequency and beacon transmission power adaptation for internet of vehicles

AbstractInternet of Vehicles (IoV) has emerged as a research field studied to improve vehicle connectivity while ensuring traffic safety. It uses vehicular ad hoc networks as communication technology. The vehicular communication achieves awareness among vehicles with the help of a periodic one‐hop broadcast of beacons. The highly dynamic vehicular environment poses challenges to a vehicular network in the IoV. The shared channel used for beacon transmission can get congested, leading to delay and beacon loss, resulting in the degradation of network performance and related applications. The proposed scheme proactively monitors the network, and with the help of local information adapts the transmission parameters. The proposed scheme is a joint beacon frequency and beacon transmission power adaptation (JFTP) based on the channel usage estimation and transmission parameter adaptation with the help of the adaptation fraction parameter. JFTP is a proactive and lightweight congestion control scheme. The performance analysis of JFTP is done through simulations confirming its effectiveness.

Optimal Resource Allocation in Multihop Wireless Networks Relying on Energy Harvesting

In this letter, we propose a new backpressure-based optimal joint routing and congestion control scheme for wireless multihop networks operating solely on energy harvesting. Compared to previous works, we assume: i) different energy consumption for each commodity, and ii) energy is consumed when a node transmits, receives and produces traffic. We show that this requires infinite battery capacity for logarithmic utility functions and further improve the battery capacity computation. Finally, we show queue stability, while ensuring energy causality.

Analysis of the interplay between RPL and the congestion control strategies for CoAP

The Constrained Application Protocol (CoAP) is gaining attention as a standardised RESTful interface for the Internet of Things (IoT). Recent studies have focused on different congestion control strategies for CoAP, in order to ensure proper operation of large-scale IoT deployments. In this paper, we carry out a performance evaluation of different congestion control policies for CoAP in a realistic environment by exploiting WiSHFUL, a platform for large-scale experimentation of network architectures. Our goal is to analyse different congestion control policies and their interplay with the routing protocol in a real environment, where unstable links and route fluctuations are frequent, due to channel variability. The results of our experiments highlight that the dynamics of the routing protocol have a noticeable impact and can impair significantly the performance of the congestion control algorithm. Specifically, the influence of the routing protocol depends on the specific congestion control policy adopted: an aggressive policy that re-transmits messages more frequently, e.g. CoCoA, is more penalised than others, in terms of throughput.

Elastic Traffic Engineering Subject to a Fair Bandwidth Allocation via Bilevel Programming

The ability of TCP’s congestion control scheme to adapt the rate of traffic flows and fairly use all the available resources is one of the Internet’s pillars. So far, however, the elasticity of traffic has been disregarded in traffic engineering (TE) methodologies mainly because, only recently, the increase in access capacity has moved the bottlenecks from the access network to the operator network and hungry cloud-based applications have begun to use all the available bandwidth. We propose a new approach to TE with elastic demands which models the interaction between the network operator and the end-to-end congestion control scheme as a Stackelberg game . Given a set of elastic traffic demands only specified by their origin-destination pairs, the network operator chooses a set of routing paths (leader’s problem) which, when coupled with the fair bandwidth allocation that the congestion control scheme would determine for the chosen routing (follower’s problem), maximizes a network utility function. We present bilevel programming formulations for the above TE problem with two widely-adopted bandwidth allocation models, namely, max-min fairness and proportional fairness, and derive corresponding exact and approximate single-level mathematical programming reformulations. After discussing some key properties, we report on computational results obtained for different network topologies and instance sizes. Interestingly, even feasible solutions to our bilevel TE problems with large optimality gaps yield substantially higher network utility values than those obtained by solving a standard single-level TE problem and then fairly reallocating the bandwidth a posteriori .

An Adaptive Broadcasting Strategy for Efficient Dynamic Mapping in Vehicular Networks

In this work, we face the issue of achieving an efficient dynamic mapping in vehicular networking scenarios, i.e., obtaining an accurate estimate of the positions and trajectories of connected vehicles in a certain area. State-of-the-art solutions are based on the periodic broadcasting of the position information of the network nodes, with an inter-transmission period set by a congestion control scheme. However, the movements and maneuvers of vehicles can often be erratic, making transmitted data inaccurate or downright misleading. To address this problem, we propose to adopt a dynamic transmission scheme based on the actual positioning error, sending new data when the estimate overcomes a preset error threshold. Furthermore, the proposed method adapts the error threshold to the operational context according to an innovative congestion control algorithm that limits the collision probability among broadcast packet transmissions. This threshold-based strategy can reduce the network load by avoiding the transmission of redundant messages, and is shown to improve the overall positioning accuracy by more than 20% in realistic urban scenarios.

Q‐learning‐based congestion control strategy for information‐centric networking

The traditional congestion control strategies cannot be used for Information‐Centric Networking (ICN), and unfortunately the current proposals on ICN congestion control have also some limitations, such as high load, inaccurate information acquisition and inefficient cooperation. In this paper, we exploit the Q‐learning‐based reinforcement learning method to further address the ICN congestion control issue. Meanwhile, the Q‐learning is used to learn the network state, at the same time, a value which depends on the transmission delay is generated. In particular, the generated value is considered as a key factor and employed to do the decision forwarding for each routing node, of which the process is the hop‐by‐hop mode. Based on a simulated multipath topology, we compare the proposed congestion control strategy with two latest benchmarks by testing packet loss ratio and throughput, and the results show that it is more efficient.

Reliability Analysis of Window Flow Control Based on Packet Transmission Interval with ECN Considering Packet Loss

This paper discusses the reliability model of a window flow control scheme using High-performance and Flexible Protocol (HpFP) with Explicit Congestion Notification (ECN) considering packet loss. HpFP is an important techniques as congestion control scheme in a radio environment and video stream communication. HpFP has the character that throughput is adjusted by changing a packet transmission interval. We have already discussed some reliability models of a window flow control scheme based on a packet transmission interval. In these models, if some packets has failed at a first-time transmission, the packet transmission interval is prolonged. On the other hand, the server checks the state of network congestion by ECN bit. That is, if ECN bit has been set during connection, a packet transmission interval is also prolonged. We consider an extended stochastic model of a window flow control scheme based on a packet transmission interval with ECN considering packet loss. That is, the server checks ECN bit during connection and if the server detects the network congestion, the server executes congestion control that a packet transmission interval is prolonged. Thereafter, if a constant number of the retransmission has failed, or a constant number of packets has failed, the server checks it again. We derive the mean time until packet transmissions succeed, and discuss analytically a window size which maximizes the amount of packets per unit of mean transmission time.

Congestion Control and Traffic Differentiation for Heterogeneous 6TiSCH Networks in IIoT.

The Routing Protocol for Low power and lossy networks (RPL) has been introduced as the de-facto routing protocol for the Industrial Internet of Things (IIoT). In heavy load scenarios, particular parent nodes are likely prone to congestion, which in turn degrades the network performance, in terms of packet delivery and delay. Moreover, there is no explicit strategy in RPL to prioritize the transmission of different traffic types in heterogeneous 6TiSCH networks, each according to its criticality. In this paper, we address the aforementioned issues by introducing a congestion control and service differentiation strategies to support heterogeneous 6TiSCH networks in IIoT applications. First, we introduce a congestion control mechanism to achieve load balancing under heavy traffic scenarios. The congestion is detected through monitoring and sharing the status of the queue backlog among neighbor nodes. We define a new routing metric that considers the queue occupancy when selecting the new parent node in congestion situations. In addition, we design a multi-queue model to provide prioritized data transmission for critical data over the non-critical ones. Each traffic type is placed in a separate queue and scheduled for transmission based on the assigned queue priority, where critical data are always transmitted first. The performance of the proposed work is evaluated through extensive simulations and compared with existing work to demonstrate its effectiveness. The results show that our proposal achieves improved packet delivery and low queue losses under heavy load scenarios, as well as improved delay performance of critical traffic.

DEFT: Multipath TCP for High Speed Low Latency Communications in 5G Networks

Multipath TCP (MPTCP) is a promising solution that can provide high end-to-end throughput in fifth generation (5G) mobile networks. Many next-generation applications will require high throughput and low latency simultaneously, but the current MPTCP congestion control algorithms cannot reliably satisfy this requirement. In this paper, a novel MPTCP congestion control scheme named delay-equalized FAST (DEFT) is proposed to achieve high throughput and low end-to-end (E2E) delay in 5G networks. First, in order to achieve high throughput, DEFT includes a novel window control algorithm that shows fast responsiveness when the state of the millimeter-wave (mmWave) link changes from line-of-sight (LOS) to non-LOS (NLOS) and vice versa. Second, in order to achieve low E2E delay, DEFT includes a delay-equalizing algorithm which minimizes additional reordering delay in the receive buffer. The performance of DEFT was evaluated based on ns-3 simulation and was compared with wVegas, Balia, and delay-adapted LIA. Simulation results show that DEFT can provide a significant goodput gain and application-level E2E delay reduction for the range of interest.

A survey and taxonomy of congestion control mechanisms in wireless network on chip

Wireless network on chip (WiNoC) has been proposed as a promising solution for on-chip interconnection network due to high scalability, high bandwidth, and low latency. However, the variations of traffic pattern distribution and data flow lead to congestion in wireless interfaces-equipped routers (WRs). Congestion is one of the main challenges in emerging WiNoCs that can reduce network performance. In recent years, various researches have been proposed to decrease congestion in WiNoCs. However, this paper presents a comprehensive survey of the significant congestion control mechanisms in WiNoCs. The available schemes are classified into six categories, includeing hardware resources-based congestion control, congestion-aware routing algorithms, medium access control protocol,congestion-aware architectures, rate-based congestion control, and application-mapping with task-migration techniques. The goal of this survey is to highlight the characteristics and inherent constraints of congestion control mechanisms in a novel approach that can help researchers for designing efficient congestion control scheme.

Research of Wireless Congestion Control Algorithm Based on EKF

The random variation of bandwidth in wireless networks causes some significant challenges to the congestion control protocols based on bandwidth estimation. In this paper, a wireless congestion control scheme based on extended Kalman filtering and bandwidth (CSEKB) is proposed. The CSEKB can effectively perceive the bandwidth oscillation of wireless networks and distinguish the type of packet loss by establishing a noise perception factor. According to the congestion factor, the congestion control parameters are adjusted to correspondingly improve the performance of the wireless network. Moreover, the variation trend of the size of the congestion window presents a law of similar normal distribution curve, which has a certain degree of local symmetry. The CSEKB was implemented in Network simulator 3 (NS3) and compared with TCP Westwood (TCPW), CUBIC, and extended Kalman filtering-based bandwidth estimation (EBE). Through extensive simulation studies, the proposed CSEKB demonstrated the significant performance in wireless networks. First, the CSEKB can achieve congestion control based on the accurate prediction of available bandwidth, and improve average throughput and link utilization. In addition, the CSEKB has good fairness and friendliness compared with several other well-known congestion control methods.

An Effective Congestion Control Scheme for MANET with Relative Traffic Link Matrix Routing

In mobile ad hoc networks (MANETs), obstacle happens owing to the loss of packet, which could be effectively minimized by linking congestion control schemes (comprising of routing methods) and control inflow at the network layer. Here, we propose a link matrix technique for MANETs that minimizes congestion links based on the range before the distortion level of each selected transmission node to get better system ability. The effectiveness of the chosen traffic matrix technique is analyzed by comparing its performance with Capacity Optimized COoperative communication (COCO). Experiments suggest that the traffic matrix technique significantly improves the accuracy to derive traffic patterns in MANETs. The simulation results proved that the proposed method is performed well compared to the COCO method in terms of throughput (41%), delay (28%), energy (60%), overhead (48%) and packet delivery ratio (5%).

Shared Bottleneck-Based Congestion Control and Packet Scheduling for Multipath TCP

In order to be TCP-friendly, the original Multipath TCP (MPTCP) congestion control algorithm is always restricted to gain no better throughput than a traditional single-path TCP on the best path. However, it is unable to maximize the throughput over all available paths when they do not go through a shared bottleneck. Also, bottleneck fairness based solutions detect the bottleneck and conduct different congestion control algorithms at different bottleneck sets to increase throughput while remaining fair to single TCP. However, existing solutions generally detect shared bottlenecks through delay correlation and loss correlation between two flows, which often lead to misjudgement in dynamic and complex network scenarios. Therefore, in this paper, we first propose a new Shared Bottleneck based Congestion Control scheme, called SB-CC, which leverages ECN (Explicit Congestion Notification) mechanism to detect shared bottlenecks among subflows and estimate the congestion degree of each subflow. Then, with the congestion degree, SB-CC balances the loads among all subflows, and smooths out congestion window fluctuation. Also, in order to prevent throughput degradation due to out-of-order packets, we propose a Shared Bottleneck based Forward Prediction packet Scheduling scheme, called SB-FPS. SB-FPS distributes data according to the window size changes of each subflow, and thus could more accurately schedule data in shared bottleneck scenarios. We implement our proposed scheme in the Linux kernel and simulation platform to evaluate the performance in different scenarios. Measurement results indicate that our scheme can detect the bottleneck more accurately and improve the overall network performance while still keeping bottleneck fairness.

Fuzzy logic rate adjustment controls using a circuit breaker for persistent congestion in wireless sensor networks

Congestion control is necessary for enhancing the quality of service in wireless sensor networks (WSNs). With advances in sensing technology, a substantial amount of data traversing a WSN can easily cause congestion, especially given limited resources. As a consequence, network throughput decreases due to significant packet loss and increased delays. Moreover, congestion not only adversely affects the data traffic and transmission success rate but also excessively dissipates energy, which in turn reduces the sensor node and, hence, network lifespans. A typical congestion control strategy was designed to address congestion due to transient events. However, on many occasions, congestion was caused by repeated anomalies and, as a consequence, persisted for an extended period. This paper thus proposes a congestion control strategy that can eliminate both types of congestion. The study adopted a fuzzy logic algorithm for resolving congestion in three key areas: optimal path selection, traffic rate adjustment that incorporates a momentum indicator, and an optimal timeout setting for a circuit breaker to limit persistent congestion. With fuzzy logic, decisions can be made efficiently based on probabilistic weights derived from fitness functions of congestion-relevant parameters. The simulation and experimental results reported herein demonstrate that the proposed strategy outperforms state-of-the-art strategies in terms of the traffic rate, transmission delay, queue utilization, and energy efficiency.

VANETomo: A congestion identification and control scheme in connected vehicles using network tomography

The Internet of Things (IoT) is a vision for an internetwork of intelligent, communicating objects, which is on the cusp of transforming human lives. Smart transportation is one of the critical application domains of IoT and has benefitted from using state-of-the-art technology to combat urban issues such as traffic congestion while promoting communication between the vehicles, increasing driver safety, traffic efficiency and ultimately paving the way for autonomous vehicles. Connected Vehicle (CV) technology, enabled by Dedicated Short Range Communication (DSRC), has attracted significant attention from industry, academia, and government, due to its potential for improving driver comfort and safety. These vehicular communications have stringent transmission requirements. To assure the effectiveness and reliability of DRSC, efficient algorithms are needed to ensure adequate quality of service in the event of network congestion. Previously proposed congestion control methods that require high levels of cooperation among Vehicular Ad-Hoc Network (VANET) nodes. This paper proposes a new approach, VANETomo, which uses statistical Network Tomography (NT) to infer transmission delays on links between vehicles with no cooperation from connected nodes. Our proposed method combines open and closed loops congestion control in a VANET environment. Simulation results show VANETomo outperforming other congestion control strategies.

MAC layer congestion control techniques in vehicular ad hoc network: survey and qualitative analysis

Vehicular ad hoc network (VANET) has become a promising area in the research community because of its various applications in road safety, mobile infotainment, and traffic management. Each vehicle can exchange information regarding the current status of traffic flow and warning through vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) mode of communication. There is a high need for reliable communication to reduce the number of accidents, and to enhance road safety. It can be ensured using efficient congestion control techniques at the medium access control (MAC) layer. The design of MAC layer protocols in VANET is a challenging task because of dynamic topology, frequent link breakage, limited bandwidth, and quality-of-service (QoS) requirements. In this paper, various congestion control schemes that are designed for the MAC layer of VANET are classified and discussed, which include time-division multiple-access (TDMA) and decentralised congestion control (DCC) techniques. TDMA MAC protocols divide the time into slots, and one vehicle can access the medium at each slot. DCC is a cross-layer function at each layer of the intelligent transportation system (ITS). This study discusses various benefits, limitations, and characteristics of the techniques mentioned above. Finally, a qualitative comparison is performed on different performance parameters.

A neuron-based active queue management scheme for internet congestion control

A neuron-based active queue management scheme for internet congestion control