Congestion Control Techniques Research Articles

Performance evaluation of the congestion severity aware rate regulation (CSRR) algorithm in wireless body area networks

SummaryWireless body area network (WBAN) is a potential low‐cost technology for privacy‐sensitive telemedicine and e‐health monitoring and services. However, it faces limited protocol and physical resource support challenges, which can result in packet transfer difficulties. In particular, WBAN requires an emergency‐aware technology that ensures a promising quality of service (QoS). One significant issue affecting QoS and energy efficiency in WBAN is congestion. Effective congestion control techniques are essential for achieving proper load balancing. To address these challenges, we propose a congestion severity aware rate control (CSRR) algorithm that enhances packet transmission rate by reducing packet losses and retransmissions. The CSRR algorithm incorporates a fuzzy controller to predict congestion rates based on runtime metrics. To regulate congestion window growth in different algorithm phases, we introduce sequences such as the Fibonacci retracement sequence, knight's move sequence, and the binary logarithm of the primorial sequence to regulate congestion window growth in the different phases of the proposed algorithm. We mathematically analyze the proposed CSRR algorithm using a Markov model. The simulation results demonstrate the superiority of our algorithm compared to existing approaches. Specifically, our algorithm achieves significant optimizations in terms of throughput (52.92%), packet loss (38.11%), delay (37.23%), and remaining energy (36.86%) when compared to existing algorithms.

COER: A Network Interface Offloading Architecture for RDMA and Congestion Control Protocol Codesign

RDMA (Remote Direct Memory Access) networks require efficient congestion control to maintain their high throughput and low latency characteristics. However, congestion control protocols deployed at the software layer suffer from slow response times due to the communication overhead between host hardware and software. This limitation has hindered their ability to meet the demands of high-speed networks and applications. Harnessing the capabilities of rapidly advancing Network Interface Card (NIC) can drive progress in congestion control. Some simple congestion control protocols have been offloaded to RDMA NIC to enable faster detection and processing of congestion. However, offloading congestion control to the RDMA NIC faces a significant challenge in integrating the RDMA transport protocol with advanced congestion control protocols that involve complex mechanisms. We have observed that reservation-based proactive congestion control protocols share strong similarities with RDMA transport protocols, allowing them to integrate seamlessly and combine the functionalities of the transport layer and network layer. In this paper, we present COER, an RDMA NIC architecture that leverages the functional components of RDMA to perform reservations and completes the scheduling of congestion control during the scheduling process of the RDMA protocol. COER facilitates the streamlined development of offload strategies for congestion control techniques, specifically proactive congestion control, on RDMA NIC. We use COER to design offloading schemes for eleven congestion control protocols, which we implement and evaluate using a network emulator with a cycle-accurate RDMA NIC model that can load MPI programs. The evaluation results demonstrate that the architecture of COER does not compromise the original characteristics of the congestion control protocols. Compared to a layered protocol stack approach, COER enables the performance of RDMA networks to reach new heights.

Optimization techniques for IDS‐Generated traffic congestion control in VANET

AbstractVehicular Ad‐hoc Network (VANET) is an emerging field of wireless networks that enables a variety of vehicle safety and convenience applications. It employs Intrusion Detection System (IDS) frameworks in its different tiers to ensure reliable and secure communication among nodes. However, IDS requires a significant amount of data to process for monitoring intrusive activities in the network. As a result, the volume of traffic increases, resulting in the network congestion. Motivated by this fact, this study provides an overview of the optimization techniques for VANET traffic congestion control. It discusses a state‐of‐the‐art analysis along with the requirements for IDS‐generated traffic congestion control. It highlights the congestion control approaches for the traffic generated by an IDS and identifies the challenges in this domain. This study also proposes a novel IDS framework for reducing IDS‐generated network traffic by combining the Local Outlier Factor and Random Forest classifier. The proposed study achieved a high precision while yielding low false positive and false negative rates. The study outperformed the existing studies with an increase in accuracy of 1.16% and a reduction in attack detection time of 1.1869 seconds. Additionally, it discusses the possible future research directions that can be applied to address the issues of IDS‐generated traffic congestion. Overall, this study serves as a comprehensive guide to the current status of IDS‐generated traffic congestion control and diverse approaches to lessen it that can be employed by academicians and researchers.

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.

Data Rate Selection Strategies for Periodic Transmission of Safety Messages in VANET

Vehicular ad hoc networks (VANETs) facilitate communication among vehicles and possess designated infrastructure nodes to improve road safety and traffic flow. As the number of vehicles increases, the limited bandwidth of the wireless channel used for vehicle-to-vehicle (V2V) communication can become congested, leading to packets being dropped or delayed. VANET congestion control techniques attempt to address this by adjusting different transmission parameters, including the data rate, message rate, and transmission power. In this paper, we propose a decentralized congestion control algorithm where each factor adjusts the data rate (bitrate) used to transmit its wireless packet congestion based on the current load on the channel. The channel load is estimated independently by each vehicle using the measured channel busy ratio (CBR). The simulation results demonstrate that the proposed approach outperforms existing data rate-based algorithms, in terms of both packet reception and overall channel load.

Traffic aware dynamic load distribution in the Data Plane of SDN using Genetic Algorithm: A case study on NSF network

For a particular topology, with the best controller and switch positions SDN occasionally exhibits subpar performance, resulting in annoyance-inducing delays and unsatisfied users. Increased traffic volume can lead to congestion, which raises the burden on specific switches and controllers and lengthens the delay of data transmission. Additionally, the failure of specific nodes or links might result in an anomalous rise in the traffic load on specific switches and controllers. The conventional switch migration and congestion control techniques might not be sufficient to deliver the best throughput and capacity for frequent and unpredictable network traffic variations. Also, certain switches/controllers may experience heavy traffic load as compared to other switches/controllers. Under such adverse situations, the performance of the network becomes extremely poor. In light of these factors, this paper suggests an effective approach based on Genetic Algorithm for traffic load distribution in the Data Plane of SDN, which effectively reroutes packet flows (when such an adverse situation arises) in order to avoid abnormal load on switches/links and maintain acceptable network performance with the available resources. The proposed approach was applied to the NSF network and a case study was performed. The proposed approach was evaluated and found to be quite effective in reducing abnormal loads on switches/links as it is found that there can be a decrease in the average load by 396 units at the cost of increasing the average latency by 1.29 ms only.

A comprehensive survey on congestion control techniques and the research challenges on VANET

The nature of vehicular mobility and high speed of vehicular ad hoc network (VANET) with dynamic change in the network topology let the vehicular remain as one of the most challenging problems in vehicular-to-vehicular (V2V) communications. Information dissemination is the major problem in VANET with a fixed bandwidth which is causing congestion on the resources, such as channels and affects the performance of the important application, especially when the emergency or secure transmission of messages is exchanged between the vehicles-to-vehicles communication. To mitigate these problems and introduce a safe vehicular environment in urban and highway, congestion detection and control has been considered and with various strategies and techniques which is take the attention of researchers in VANET. In our survey we mentioned recent techniques and approaches which is used in congestion detection and control and applied different matrices and parameters which is used to evaluate these approaches. In addition, the study also explained the limitation and problems that face the current congestion detection and control schemes, finally we present various solution approach and future expectations in vehicular communication.

A Survey on Various Congestion Control Techniques in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are made up of small battery-powered sensors that can sense and monitor a variety of environmental conditions. These devices are self-contained and fault tolerant. The majority of WSNs are built to perform data collection tasks. These data are gathered and then sent to the sink node. Small packets are sent towards the sink node in such cases, and as a result, the areas near the sink node become congested, becoming the bottleneck of the entire network. In this paper, a survey of existing techniques or methods for detecting and eliminating congestions is conducted. Finally, a comparison in the form of a table based on various matrices is presented.

Effective and Enhanced Congestion Control Technique using Adaptive Routing Protocol to Improve the Performance of Crammed WSN

Nowadays, congestion on the network becomes a usual fact which is to be focused and to be addressed appropriately especially in Wireless Sensor Networks (WSN) for crammed type networks. Limited capacity on channel and wastage of energy are the root cause of congestion in WSN. The effects of congestion implies on QoS parameters, queue length, data arrival rate etc. Furthermore, data packets should be transmitted energy-efficiently to the sink node. In this regard, an Energy-Efficient Routing Protocol is offered to efficiently transmit the nodes to their end node or destination. To control congestion, an Adaptive Buffer trade-off and Improved Trust-based Energy Efficient Routing protocol are first presented, this method identifies the congestion free paths and the Buffer trade-off handles the buffer effectively. To route the protocol, a Cross-Layer Security-Based Fuzzy Logic Energy Efficient Packet Loss Preventive Routing Protocol has been developed. The proposed protocol routes the nodes and the protocol adopts a routing protocol that imparts security in terms of avoiding malicious nodes and preventing data loss. Consequently, to improve the lifetime of the network, a Density Aware Optimal Clustering Approach is presented. The proposed method is evaluated based on the Matlab software and the QoS performance metrics are Energy Consumption, Packet Delivery Ratio, Trust Value Computation, latency, reliability, energy efficiency, end-to end delay, Average Throughput, accuracy and network lifetime. The effectiveness of the research is evaluated by comparing it with other existing techniques, including Trust Aware Secure Routing Protocol (TASRP), Artificial Flora Algorithm Based Support Vector Machine (SVM-AF), Well-Organized Trust Estimation Based Routing Scheme (ETERS), Lion Fuzzy Bee, and Bat Fuzzy Bee Algorithm. Accordingly, the suggested method’s performance is higher than the existing methods for Packet delivery ratio, throughput, network lifetime, energy efficiency, and reliability. Consequently, the proposed method improves the congestion control performance in an energy-efficient manner, in future; a recently advanced technique is proposed to effectively improve the network performance respectively.

QoS based congestion evasion clustering framework of wireless sensor networks

Congestion is a significant issue for event-based applications due to the continuous data collection and transmission by the sensors constituting the network. The congestion control technique monitors the process of adjusting the data and intends to manage the network traffic level to the threshold value. The information gathered from an intensive study is required to strengthen the knowledge base for devising a QoS based congestion evasion clustering framework of wireless sensor networks. In this scheme, the cluster heads are optimally determined and dispersed over the network. The data aggregation approach has been applied in a clustered network and set out a crucial paradigm for WSN routing. The proposal employs to mitigate congestion while messages are being forwarded via an alternate route to distribute the traffic and increase the throughput. This technique aims to balance the energy ingestion among the sensor nodes, reduce energy consumption, improve network lifetime, and achieve the quality of services. The result analysis revealed that the proposed scheme recommends 22.5% better throughput, 21% lesser end-to-end delay, 25.5% better delivery ratio, and efficiently relieves congestion while preserving the network's performance for attaining QoS in wireless sensor networks.

RAFT: Congestion control technique for efficient information dissemination in ICN based VANET

Vehicular communications contain a massive number of nodes with a high mobility rate moving between the network connectivity to connect and disconnect the network rapidly. With the increasing demand and services, many information packets are generated and forwarded between “vehicle to vehicle (V2V) and vehicle to infrastructure (V2I)”. This will result in a congestion problem, which increases the delay and sometimes the loss of packets. It is a severe problem in VANET, wherein urban areas, a large number of nodes present in network with rapid mobility and dynamic topology. We proposed a network-aware solution called RAFT in vehicular ad-hoc networks (VANET) connected with Information-Centric Networks architecture to address these problems. We can solve the congestion problem at two levels at level one by effectively managing the network’s available recourses. In level two, we prioritize data for effective data dissemination. The balance between the available to consumed recourse will improve the network’s throughput, lessen delay, and improve the data dissemination rate. We have validated our proposed work with the simulation of ICN based VANET using ndnSIM.

The use of Analytical Hierarchy Process in sensor-based networks for security-aware congestion control

<abstract><p>Network congestion may occur naturally or intentionally caused by selfish nodes. Existing congestion control techniques designed by researchers for sensor-based networks have primarily focused on natural modes of congestion occurrence and ignored malevolent nodes' potential for purposeful congestion-like scenario creation. In light of this fact, a security attack-resistant congestion control method that takes into account both possible sources of congestion in sensor nodes has been developed. So firstly, a trust-based technique has been developed to get rid of selfish nodes' intentional attempts to cause congestion. After the elimination of malicious nodes, a congestion avoidance method has been applied which tries to prevent the natural way of congestion occurrence. For this purpose, we have applied a multi-criteria decision-making method as there are many factors responsible for congestion occurrence. The remaining energy, node potential value, node load factor, and traffic burst rate have been considered as decision factors. Simulation results show that our Security Aware Congestion Control technique using the AHP method (SACC-AHP) outperforms the existing relevant techniques LEACH, TCEER, TASRP, CARA and SACC in terms of energy efficiency, security, packet delivery ratio and network lifetime.</p></abstract>

Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

Vehicular Ad hoc Network (VANET) has become an integral part of Intelligent Transportation Systems (ITS) in today's life. VANET is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real world. VANET is susceptible to security issues, particularly DoS attacks, owing to maximum unpredictability in location. So, effective identification and the classification of attacks have become the major requirements for secure data transmission in VANET. At the same time, congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections, for a traveler. With this motivation, the current research paper presents an intelligent DoS attack detection with Congestion Control (IDoS-CC) technique for VANET. The presented IDoS-CC technique involves two-stage processes namely, Teaching and Learning Based Optimization (TLBO)-based Congestion Control (TLBO-CC) and Gated Recurrent Unit (GRU)-based DoS detection (GRU-DoSD). The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the network. TLBO algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel utilization. TLBO is applied to avoid congestion on roadways. Besides, GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the network. The outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network.

Multistate Active Combined Power and Message/Data Rate Adaptive Decentralized Congestion Control Mechanisms for Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) have emerged in time to reduce on-road fatalities and provide efficient information exchange for entertainment-related applications to users in a well-organized manner. VANETs are the most instrumental elements in the Internet of Things (IoT). The objective lies in connecting every vehicle to every other vehicle to improve the user’s quality of life. This aim of continuous connectivity and information exchange leads to the generation of more information in the medium, which could congest the medium to a larger extent. Decentralized congestion control (DCC) techniques are specified to reduce medium congestion and provide various safety applications. This article presents two DCC mechanisms that adapt message rate and data rate combined with transmit power control mechanism. These mechanisms are developed under multi-state active design proposed by the standard. The proposed methods deliver better performance over other mechanisms in terms of power, channel load, and channel utilization using real-time-based scenarios by simulation in SUMO.

Delay-Aware Priority Access Classification for Massive Machine-Type Communication

Massive Machine-type Communications (mMTC) is one of the principal features of the 5th Generation and beyond (5G+) mobile network services. Due to sparse but synchronous MTC nature, a large number of devices tend to access a base station simultaneously for transmitting data, leading to congestion. To accommodate a large number of simultaneous arrivals in mMTC, efficient congestion control techniques like access class barring (ACB) are incorporated in LTE-A random access. ACB introduces access delay which may not be acceptable in delay-constrained scenarios, such as, eHealth, self-driven vehicles, and smart grid applications. In such scenarios, MTC devices may be forced to drop packets that exceed their delay budget, leading to a decreased system throughput. To this end, in this paper a novel delay-aware priority access classification (DPAC) based ACB is proposed, where the MTC devices having packets with lesser leftover delay budget are given higher priority in ACB. A reinforcement learning (RL) aided framework, called DPAC-RL, is also proposed for online learning of DPAC model parameters. Simulation studies show that the proposed scheme increases successful preamble transmissions by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$75 \%$</tex-math></inline-formula> while ensuring that the access delay is well within the delay budget.

Opportunities of hybrid random access protocols for M2M communications in LTE/LTE-A networks

Machine-to-machine (M2M) communications on Long-term evolution (LTE) networks form a substantial part for the Internet-of-things (IoT). The random access procedure is the first step for M2M devices to access network resources. Many researchers have attempted to improve the efficiency of the random access procedure. This work revisits the performance of the hybrid random access protocols which combine congestion control techniques with collision resolution techniques. In particular, we investigate two hybrid protocols. The first one combines the pre-backoff (PBO) with tree random access (TRA), and the second one combines dynamic access barring (DAB) with TRA. The probability analysis is presented for both protocols. The performance is evaluated based on the access success rate, the mean throughput, the mean delay, the collision rate and the mean retransmissions. The simulation results show that the hybrid protocols achieve the highest success rate and throughput with moderate delay and low collision rates with a lower mean number of retransmissions compared to three benchmarks that apply either a congestion control or a collision resolution. The opportunities of future developments of hybrid protocols are listed at the end of this paper to highlight the issues that could be investigated to improve the performance of hybrid random access protocols.

Energy Efficient Data Distribution on Cloud with Optimal Routing Path Based Congestion Control in WSN Environment

Presently, Wireless Sensor Network (WSN) is the extremely standard services utilized in commercial in addition to industrial applications, owing to its technical development in the embedded computing devices’ processor, communication, along with less-power usage. Moreover, in the WSN’s advantages, the sensor data’s energy-efficient distribution onto the cloud data centre is amid the core challenges caused by higher data loads and also congestion. Aimed at overcoming these problems, this work proffers an energy-efficient Data Packet (DP) distribution protocol comprising an optimal routing path centred congestion control technique in the WSN environment. This system not just ponders the energy-efficient DP distribution nevertheless increments the sensor’s lifetime by choosing the Cluster Head (CH) centred on the sensor’s residual energy, packet loss factor, data forwarding rate, and minimal distance. Next, the numerous routing paths prevalent in inter-cluster and intra-clusters are detected utilizing the QLA2ODV method; the congested nodes are detected as of these paths utilizing LM-NN classifier; if congestion happens, the QLA2ODV distributes the DP onto the Base Station (BS) via the optimum congestion-free routing path. The optimal routing paths were elected as of the congestion-free paths utilizing the WFCSO method. Past obtaining the sensor data, the BS transmits the DPs onto the Fog Nodes (FNs). At last, the sensor data are effectively distributed onto the cloud data centres grounded on the data’s features and cloud features utilizing the BD-SBO technique. The experiential outcomes determine the proposed system’s effectiveness.

Link discontinuity and optimal route data delivery for random waypoint model

In mobile ad hoc network (MANET), the traffic load impacts energy utilization and throughput. It can be overcome by efficient congestion control and link discontinuity identifier techniques. To improve the overall network performance of MANET, the link discontinuity and optimal route data delivery (LDORDD) protocol has been implanted based on AOMDV protocol is proposed along with cross-layer design. In this approach, local route recovery and transmission queue management (TQM) activities are used to control the packet's drop and congestion. This protocol works on buffering the incoming packets at the TQM layer, which controls the packets drop by locally repairing the route. The simulation results of the LDORDD protocol is evaluated in terms of network throughput, packet delivery ratio, and average end to end delay. The numerical and graphical representation of the LDORDD proves that the network resource rudiments are enhanced when compared to the existing methods.

Using priority queuing for congestion control in IoT‐based technologies for IoT applications

SummaryThe Internet of Things (IoT) connect millions of devices in diverse areas such as smart cities, e‐health, transportation and defense to meet a wide range of human needs. To provide these services, a large amount of data needs to be transmitted to the IoT network servers. However, the IoT networks suffer from limited resources such as buffer size, node processing capabilities, and server capacities adversely affecting throughputs, latency, and energy consumption. Additionally, the ensuing heavy network traffic due to large amount of data transmitted results in congestion which degrades IoT network performance. Therefore, innovative congestion control techniques, e.g., queue management approach needs to be developed to overcome congestion problems in IoT networks. In this paper, a novel priority queuing technique (Npqt++) is developed to control congestion in IoT networks. The Npqt++ implements a preemptive/nonpreemptive discipline with a discretion rule to classify network traffic based on their real‐time requirement into priority groups. If the discretion rule for low priority packets is satisfied, high priority packets are pushed to the front of the queue; otherwise, they wait in the queue. Our approach significantly outperforms existing techniques in terms of throughput, delay, and energy consumption.

Fuzzy logic controller based priority model for VANET scheduling

Designing a reliable Vehicular Adhoc Network is challenging, due to the critical constraints in providing reliability with ensured data delivery and minimal latency simultaneously. Here, a fuzzy-based congestion control technique, to minimize latency and packet loss for high priority safety messages is proposed. Fuzzy logic is employed to provide multi-level priority assignment to the vehicles contending for channel access. Based on this priority, the queued messages in the Cluster Head are scheduled. NS-2 simulations prove that this technique improves the packet delivery and minimizes packet loss and channel access delay compared to cognitive radio based implementations at largely densed vehicular network.