Transmission Control Protocol Network Research Articles

BBO tuned PI controller for the stability of TCP networks

The congestion is the most important issue that effects on the performance of data transition over internet networks. One of the important techniques developed is active queue management (AQM) that prepares an efficient congestion control by reducing losing packets, queue size, and energy consumption. Therefore, AQM technique deemed as a base of many congestion control algorithms schemes. This work suggested the using of proportional integral (PI) controller as an AQM and then use an optimized control system such as biogeography-based optimization (BBO) with PI controller as (BBO-PI). The optimal control (BBO-PI) is characterized by access to design and fine-tuning of defining the shapes of the optimal parameters of PI controller. The BBO algorithm was implemented by using the mathematical system model by M-file/Matlab and Simulink. The simulation results showed the best performance for the transmission control protocol (TCP) network when compared the system with using the PI controller and using optimal control (BBO-PI), the ratio of enhancing the system with using of BBO-PI better than using a PI controller only in terms of rising time is 1.11, settling time is 2.85 and overshooting is 85%. Therefore, the proposed method was very fast and required few iterations.

Mathematical Modeling and Validation of Retransmission-Based Mutant MQTT for Improving Quality of Service in Developing Smart Cities

Unreliable networks often use excess bandwidth for data integration in smart cities. For this purpose, Messaging Queuing Telemetry Transport (MQTT) with a certain quality of service (QoS) is employed. Data integrity and data security are frequently compromised for reducing bandwidth usage while designing integrated applications. Thus, for a reliable and secure integrated Internet of Everything (IoE) service, a range of network parameters are conditioned to achieve the required quality of a deliverable service. In this work, a QoS-0-based MQTT is developed in such a manner that the transparent MQTT protocol uses Transmission Control Protocol (TCP)-based connectivity with various rules for the retransmission of contents if the requests are not entertained for a fixed duration. The work explores the ways to improve the overall content delivery probability. The parameters are examined over a transparent gateway-based TCP network after developing a mathematical model for the proposed retransmission-based mutant QoS-0. The probability model is then verified by an actual physical network where the repeated content delivery is explored at VM-based MQTT, local network-based broker and a remote server. The results show that the repeated transmission of contents from the sender improves the content delivery probability over the unreliable MQTT-based Internet of Things (IoT) for developing smart cities' applications.

Deep Learning (DL)-based adaptive transport layer control in UAV Swarm Networks

This paper focuses on the congestion control issues in Unmanned Aerial Vehicle (UAV) Swarm Networks (USNs). In a USN, many network factors can cause segment loss, including dynamic swarming, high mobility, and link fading loss. With traditional transport layer protocols such as Transmission Control Protocol (TCP), these losses are interpreted as congestion events and will cause the data sending rate being decreased dramatically, therefore impacting throughput. In this paper, a learning-based adaptive network coding scheme is proposed to handle segment loss. In this scheme, a certain amount of redundancy is attached to the original data. If the segment loss is caused by random factors (such as radio interference), the lost segments are retrieved by decoding. However, if the loss is caused by congestion, the sender will retransmit the lost segments and decrease the sending rate. The coding rate is a critical factor, which should guarantee that the random loss can be retrieved by decoding while the congestion loss triggers retransmission and sending rate deduction. To achieve this goal, a Deep Learning (DL) algorithm is proposed, which comprehensively considers the wireless network conditions and dynamically optimizes the coding rate. Our experimental results show that the DL-based network coding scheme provides improved throughput and end-to-end delay compared to the TCP and general network coding schemes.

Congestion tracking control for multi-router TCP/AQM network based on integral backstepping

With the rapid development of the Internet of things and mobile Internet, more and more data flows into the Internet and will cause congestion of multiple routers in the network. However, most of the current congestion control methods are designed for a single router, which cannot adapt to the situation of multi-router congestion. Therefore, this paper proposes a congestion control algorithm to solve the network congestion problem of multiple routers. The model used in this paper captures the dynamics of Transmission Control Protocol (TCP) and covers the four possible behaviors of the TCP network when congestion occurs. The idea of TCP Active Queue Management (AQM) is to reduce congestion by actively discarding some packets in the queue. Taking the probability of packets marked as dropping as the control input, the congestion problem can be solved by using the backstepping method to make the queue length converge to the desired queue length. In this paper, the classical control strategy, backstepping, is extended to TCP network with multiple bottleneck routers to handle the AQM problem. Inspired by the existing network model with one bottleneck router, a revised model is introduced for TCP/AQM network with multiple bottleneck routers. And then, an AQM method Backstepping Congestion Control for Multi-router (BCCM) is proposed to deal with network congestion based on integral backstepping. Simulation results demonstrate that the convergence time of BCCM is about 10% of that of RED and The packet loss ratio of BCCM is 3 orders of magnitude lower than RED. In addition, BCCM has greater stability when traffic, link capacity, and router locations change.

A robust fractional-order PID controller design based on active queue management for TCP network

ABSTRACTIn this paper, a robust fractional-order controller is designed to control the congestion in transmission control protocol (TCP) networks with time-varying parameters. Fractional controllers can increase the stability and robustness. Regardless of advantages of fractional controllers, they are still not common in congestion control in TCP networks. The network parameters are time-varying, so the robust stability is important in congestion controller design. Therefore, we focused on the robust controller design. The fractional PID controller is developed based on active queue management (AQM). D-partition technique is used. The most important property of designed controller is the robustness to the time-varying parameters of the TCP network. The vertex quasi-polynomials of the closed-loop characteristic equation are obtained, and the stability boundaries are calculated for each vertex quasi-polynomial. The intersection of all stability regions is insensitive to network parameter variations, and results in robust stability of TCP/AQM system. NS-2 simulations show that the proposed algorithm provides a stable queue length. Moreover, simulations show smaller oscillations of the queue length and less packet drop probability for FPID compared to PI and PID controllers. We can conclude from NS-2 simulations that the average packet loss probability variations are negligible when the network parameters change.

Multi-Objective Genetic Algorithm Optimization Using PID Controller for AQM/TCP Networks

The competition of connected sources to access to the resources within a network leads to a congestion that causes a global delay in the network. For this reason, it's challenging to design an optimized controller that stabilizes the system and reduces the present delay. This paper proposed a developed PID (Proportional Integral Derivative) controller based on an extension of Hermite-Biehler theorem applicable to quasipolynomials, that is to say to systems with delay. Then the stability region of the PID controller parameters was obtained. An improved Multi-objective Genetic Algorithm (GA) is employed to seek the optimal PID controller gains such that performance indices of integrated-absolute error (IAE), integrated-squared error (ISE), integrated-time-absolute error (ITAE) and integrated-time-squared error (ITSE) are minimized, and thereby a stability of TCP (Transmission Control Protocol)network is guaranteed. The performance of the proposed control scheme is evaluated via a series of numerical simulations that show its efficiency.

TCPNC-DGSA: Efficient Network Coding Scheme for TCP in Multi-hop Cognitive Radio Networks

Cognitive radio (CR) has emerged as a promising solution to enhance spectrum utilization. In CR networks (CRNs), the secondary users can opportunistically exploit frequency bands when the primary users (PUs) do not occupy the bands. In this communication paradigm, transmission control protocol (TCP) performance may suffer from significant degradation due to the features of CRNs. In this paper, we investigate the limitations of TCP in multi-channel multi-radio multi-hop CRNs, and propose a novel TCP called TCP Network Coding Dynamic Generation Size Adjustment (TCPNC-DGSA) based on network coding. We dynamically adjust generation size in network coding operation according to the wireless communication environment--Generation Round Trip Time. In the meanwhile, we modify the TCP mechanism to fit into CRNs, by considering spectrum sensing state, spectrum changing state and presence of PUs. The simulation results indicate that TCPNC-DGSA can significantly improve the network performance in terms of throughput, bandwidth efficiency and delay. To the best of our knowledge, TCPNC-DGSA is the first TCP for CRNs from a network coding perspective, which can guarantee a quality of service in terms of delay.

EFRED: Enhancement of Fair Random Early Detection Algorithm

Quality of Service (QoS) generally refers to measurable like latency and throughput, things that directly affect the user experience. Queuing (the most popular QoS tool) involves choosing the packets to be sent based on something other than arrival time. The Active queue management is important subject to manage this queue to increase the effectiveness of Transmission Control Protocol networks. Active queue management (AQM) is an effective means to enhance congestion control, and to achieve trade-off between link utilization and delay. The de facto standard, Random Early Detection (RED), and many of its variants employ queue length as a congestion indicator to trigger packet dropping. One of these enhancements of RED is FRED or Fair Random Early Detection attempts to deal with a fundamental aspect of RED in that it imposes the same loss rate on all flows, regardless of their bandwidths. FRED also uses per-flow active accounting, and tracks the state of active flows. FRED protects fragile flows by deterministically accepting flows from low bandwidth connections and fixes several shortcomings of RED by computing queue length during both arrival and departure of the packet. Unlike FRED, we propose a new scheme that used hazard rate estimated packet dropping function in FRED. We call this new scheme Enhancement Fair Random Early Detection. The key idea is that, with EFRED Scheme change packet dropping function, to get packet dropping less than RED and other AQM algorithms like ARED, REM, RED, etc. Simulations demonstrate that EFRED achieves a more stable throughput and performs better than current active queue management algorithms due to decrease the packets loss percentage and lowest in queuing delay, end to end delay and delay variation (JITTER).

Design of proportional-derivative-type state feedback controllers for congestion control of transmission control protocol networks

A new proportional-derivative-type state feedback controller is proposed for congestion control of transmission control protocol (TCP) networks. An analytical TCP model is adopted. In the proposed control scheme, it is possible to efficiently control the TCP traffic using only the queue length at the router without the need to know the TCP window size which is not available locally. The results are presented in terms of delay-dependent linear matrix inequality. The proposed method is verified by simulation examples using NS software, and the effectiveness and superiority of our method over other control schemes, such as the proportional-integral, random early detection and generalised minimum variancemethods, are also shown.

AQM router design for TCP network via input constrained fuzzy control of time-delay affine Takagi–Sugeno fuzzy models

In this article, Takagi–Sugeno (T–S) fuzzy control theory is proposed as a key tool to design an effective active queue management (AQM) router for the transmission control protocol (TCP) networks. The probability control of packet marking in the TCP networks is characterised by an input constrained control problem in this article. By modelling the TCP network into a time-delay affine T–S fuzzy model, an input constrained fuzzy control methodology is developed in this article to serve the AQM router design. The proposed fuzzy control approach, which is developed based on the parallel distributed compensation technique, can provide smaller probability of dropping packets than previous AQM design schemes. Lastly, a numerical simulation is provided to illustrate the usefulness and effectiveness of the proposed design approach.

Sliding Modes for Anomaly Observation in TCP Networks: From Theory to Practice

Anomaly detection has been an active open problem in the networks community for several years. In this brief, we aim at detecting such abnormal signals by control theory techniques. Several classes of sliding mode observers are proposed for a fluid flow model of the transmission control protocol (TCP)/internet protocol network. Comparative simulations via network simulator NS-2 show the enhancement brought by a higher order sliding mode observer. The efficiency of this observer opens the way toward observing traffics with real TCP flow characteristics. To achieve this end, trace replay techniques for TCP traffic traces are presented. Finally, experiments lead to successful anomaly estimation under real traffic conditions.

Improving Performance of Remote TCP in Cognitive Radio Networks

Recent advances in cognitive radio technology have drawn immense attention to higher layer protocols above medium access control, such as transmission control protocol (TCP). Most proposals to improve the TCP performance in cognitive radio (CR) networks have assumed that either all nodes are in CR networks or the TCP sender side is in CR links. In those proposals, lower layer information such as the CR link status could be easily exploited to adjust the congestion window and improve throughput. In this paper, we consider a TCP network in which the TCP sender is located remotely over the Internet while the TCP receiver is connected by a CR link. This topology is more realistic than the earlier proposals, but the lower layer information cannot be exploited. Under this assumption, we propose an enhanced TCP protocol for CR networks called TCP for cognitive radio (TCP-CR) to improve the existing TCP by (1) detection of primary user (PU) interference by a remote sender without support from lower layers, (2) delayed congestion control (DCC) based on PU detection when the retransmission timeout (RTO) expires, and (3) exploitation of two separate scales of the congestion window adapted for PU activity. Performance evaluation demonstrated that the proposed TCP-CR achieves up to 255% improvement of the end-to-end throughput. Furthermore, we verified that the proposed TCP does not deteriorate the fairness of existing TCP flows and does not cause congestions.

Genetic-sigmoid random early detection covariance control as a jitter controller

The congestion control problem of the intermediate nodes in the Internet has received extensive attention in control community. In this study, a novel intelligent sigmoid random early detection controller based on a genetic algorithm is presented to solve the problem of active queue management. In this work, an optimisation theoretic method is used to achieve a better performance in queue management in transmission control protocol networks. A covariance control based on the proposed method is implemented to stabilise and control the fluctuation of the queue length, which in turn results in minimum jitter. The authors illustrate their results with NS2 simulations.

Stochastic observability in network state estimation and control

A hidden Markov model for the traffic congestion control problem in transmission control protocol (TCP) networks is developed, and the question of observability of this system is posed. Of specific interest are the dependence of observability on the congestion control law and the interaction between observability ideas and the effectiveness of feedback control. Analysis proceeds with a survey of observability concepts and an extension of some available definitions for linear and nonlinear stochastic systems. The key idea is to link the improvement of state estimator performance to the conditioning on the output data sequence. The observability development proceeds from linear deterministic systems to linear Gaussian systems, nonlinear systems, etc., with backwards compatibility to deterministic ideas. The principal concepts relate to the entropy decrease of scalar functions of the state, which in the linear case are describable in terms of covariance matrices. A feature of nonlinear systems is that the estimator properties may affect the closed-loop control performance. Results are derived linking stochastic reconstructibility to strict improvement of the optimal closed-loop control performance over open-loop control for the hidden Markov model. The entropy provides a means to quantify and thus order simulation results for a simplified TCP network. Motivated by the link between feedback control and reconstructibility, the entropy formulation is also explored as a means to discriminate between different control strategies for improving estimator performance. This approach has connections to dual-adaptive control ideas, where the control has the simultaneous and opposing goals of regulating the system and of exciting the system to prevent estimator divergence.

A three-dimensional probabilistic fuzzy control system for network queue management

A novel probabilistic fuzzy control system is proposed to treat the congestion avoidance problem in transmission control protocol (TCP) networks. Studies on traffic measurement of TCP networks have shown that the packet traffic exhibits long range dependent properties called self-similarity, which degrades the network performance greatly. The probabilistic fuzzy control (PFC) system is used to handle the complex stochastic features of self-similar traffic and the modeling uncertainties in the network system. A three-dimensional (3-D) membership function (MF) is embedded in the PFC to express and describe the stochastic feature of network traffic. The 3-D MF has extended the traditional fuzzy planar mapping and further provides a spatial mapping among “fuzziness-randomness-state”. The additional stochastic expression of 3-D MF provides the PFC an additional freedom to handle the stochastic features of self-similar traffic. Simulation experiments show that the proposed control method achieves superior performance compared to traditional control schemes in a stochastic environment.

Internet Traffic Control Using Dynamic Neural Networks

Active Queue Management(AQM) has been widely used for congestion avoidance in Transmission Control Protocol(TCP) networks. Although numerous AQM schemes have been proposed to regulate a queue size close to a reference level, most of them are incapable of adequately adapting to TCP network dynamics due to TCP's non-linearity and time-varying stochastic properties. To alleviate these problems, we introduce an AQM technique based on a dynamic neural network using the Back-Propagation(BP) algorithm. The dynamic neural network is designed to perform as a robust adaptive feedback controller for TCP dynamics after an adequate training period. We evaluate the performances of the proposed neural network AQM approach using simulation experiments. The proposed approach yields superior performance with faster transient time, larger throughput, and higher link utilization compared to two existing schemes: Random Early Detection(RED) and Proportional-Integral(PI)-based AQM. The neural AQM outperformed PI control and RED, especially in transient state and TCP dynamics variation.

Adaptive neural queue management for TCP networks

Active Queue Management (AQM) is a proven strategy to efficiently maintain queues and ensure high utilization of Transmission Control Protocol (TCP) network resources. The fundamental mechanism is to manage incoming packet rates at a router to prevent incipient network congestion. In this paper, we present an efficient neural network AQM system as a queue controller. The recurrent neural network has a Multi-layer Perceptron-Infinite Impulse Response (MLP-IIR) structure. Three distinct neural AQMs are trained under different network scenarios involving traffic levels. Selecting one of three neural AQMs is based on posterior probability history of traffic level. In addition, we investigate stochastic modeling of the network dynamics by a Dynamic Bayesian Network (DBN). This model allows implementation of a predictive AQM system in which queue dynamics are predicted and used for error prediction via online DBN estimation. Our AQM method is evaluated through simulation experiments both using an Ordinary Differential Equation (ODE) network model and using OPNET ©. The simulation results demonstrate that our adaptive neural AQM outperforms Random Early Detection (RED) and Proportional-Integral-Derivative (PID) based AQM.

Interaction Between MIMD-Poly and PIPD-Poly Algorithms and Other TCP Variants in Multiple Bottleneck TCP Networks

This paper introduces and analyzes a class of non-linear congestion control algorithms called polynomial congestion control algorithms. They generalize the Additive Increase and Multiplicative Decrease (AIMD) algorithms used for Transmission Control Protocol (TCP) connections. These algorithms provide additive increase using a polynomial of the inverse of the current window size and provide multiplicative decrease using the polynomial of the current window size. There are infinite numbers of TCP-compatible polynomial algorithms by assuming a polynomial of a different order. This paper analyzes the interaction between the two models (Multiplicative Increase and Multiplicative Decrease/MIMD-Poly and Polynomial Increase and Polynomial Decrease/PIPD-Poly) of these generalized algorithms, for wired (with unicast and multicast) and wireless TCP networks. TCP compatibility of these algorithms is evaluated using the simulations of the implementations of the proposed two models. Simulations are done using ns2, a discrete event simulator. The model MIMD-Poly is proved to be TCP compatible. The results of the simulation are compared with TCP variants, such as TCP/Tahoe, TCP/Reno, TCP/New Reno and TCP/Vegas. The comparison shows that both algorithms perform better in terms of throughput.

Steady state and transient state behaviours analyses of TCP connections considering interactions between TCP connections and network

The Internet uses a window-based congestion control mechanism in transmission control protocol (TCP). In the literature, there have been a great number of analytical studies on TCP. Most of those studies have focused on the statistical behaviour of TCP by assuming a constant packet loss probability in the network. However, the packet loss probability, in reality, changes according to the packet transmission rates from TCP connections. Conversely, the window size of a TCP connection is dependent on the packet loss probability in the network. In this paper, we explicitly model the interaction between the congestion control mechanism of TCP and the network as a feedback system. By using this model, we analyse the steady state and the transient state behaviours of TCP. We derive the throughput and the packet loss probability of TCP, and the number of packets queued in the bottleneck router. We then analyse the transient state behaviour using a control theoretic approach, showing the influence of the number of TCP connections and the propagation delay on the transient state behaviour of TCP. Copyright © 2005 John Wiley & Sons, Ltd.