An Efficient and Stable Congestion Control Scheme with Neighbor Feedback for Cluster Wireless Sensor Networks

In Internet gateways with active queue management, effective algorithms are often used to enhance the traffic flows through the gateway. The random early detection (RED) algorithm is a widely used algorithm for controlling the transmission control protocol (TCP) flows. Specifically, the RED algorithm has deliberately introduced randomness into the explicit congestion notification (ECN) marking mechanism in order to enhance the fairness and to avoid synchronization of TCP flows through the gateway. As randomness plays a key role in providing better performance for RED gateways than the DropTail gateways, knowledge of the effect of the random mechanism is important to the understanding the behavior of the system. The fluid flow model (FFM), ignoring the randomness of ECN markings, has provided a fast alternative to the slow ns-2 network simulation tool in studying the TCP flow in the RED gateway. In this brief, the random ECN marking mechanism in RED gateways has been studied. The randomness of the RED ECN marking algorithm is implemented into the FFM. Verified using ns-2 simulations, the proposed model shows better dynamical performance than FFM in both the single-bottleneck dumbbell network and in networks with interactive bottlenecks.

Random Early Detection (RED) is one of the most commonly used Active Queue Management (AQM) algorithms that is recommended by IETF for deployment in the network. Although RED provides low average queuing delay and high throughput at the same time, but effectiveness of RED is highly sensitive to the RED parameters setting. As network condition varies largely, setting RED's parameters with fixed values is not an efficient solution. We propose a new method to dynamically tuning RED's parameters. For this purpose, we compute the rate of which the queue is occupied and consider it as a congestion metric that will be forecasted when the queue is overloaded. This meter is used to dynamically setting RED parameters. The simulation results show the effectiveness of the proposed method. According to the results, we achieve a significantly higher utilization and less packet loss comparing to original RED algorithm in dynamic conditions of the network.

Random Early Detection(RED) algorithm of Active Queue Management(AQM) is a well-known link congestion control algorithm. Based on analyzing the linear RED algorithm this paper proposes a nonlinear feedback method of composite curves which used to notify end system congestion state feedback mechanism. When the new mechanism is applied to the queue management process of the router buffer, the result of the simulations indicate the effectiveness of the new algorithm. Therefore more effective congestion control target is achieved.

Active queue management (AQM) algorithm is the key technology of congestion control for routers recommended by IETF. AQM provides a main way to avoid Internet congestion combined with TCP. Random Early Detection (RED), recommended by AQM as the default algorithm for routers of NGN, has become the research hotspot nowadays. In this paper, we proposes an improved RED algorithm named ERED (Enhanced RED) based on the study of existing RED, congestion detection and congestion avoidance. A serial of simulation had been done based on NS2 simulator. The experimental results indicate that the packet loss and stability are better than RED algorithm. The comparison of packet numbers and packet loss numbers under different congestion condition shows that the packet loss and link utilization of ERED are better than RED, which enhances the adaptability of RED algorithm.

Active Queue Management (AQM) is an active research area in the Internet community. Random Early Detection (RED) is a typical AQM algorithm, but it is known that it is difficult to configure its parameters and its average queue length is closely related to the load level. This paper proposes an effective fuzzy congestion control algorithm based on fuzzy logic which uses the predominance of fuzzy logic to deal with uncertain events. The main advantage of this new congestion control algorithm is that it discards the packet dropping mechanism of RED, and calculates packet loss according to a preconfigured fuzzy logic by using the queue length and the buffer usage ratio. Theoretical analysis and Network Simulator (NS) simulation results show that the proposed algorithm achieves more throughput and more stable queue length than traditional schemes. It really improves a router’s ability in network congestion control in IP network.

Low‐rate denial of service (LDoS) attacks exploit the congestion control mechanism to degrade the network quality of service. As a classic active queue management algorithm, random early detection (RED) algorithm is widely used to avoid network congestion. However, RED is vulnerable to LDoS attacks. LDoS attacks with well‐configured attack parameters force RED queue to fluctuate severely, thereby throttling transmission control protocol (TCP) senders’ sending rate. A feedback control model is proposed to describe the process of the congestion control, by which the congestion window and queue behaviours are analysed combined. After that, a two‐dimensional queue distribution model composed of the instantaneous queue and the average queue is designed to extract the attack feature. Moreover then, a combination of a simple distance‐based approach and an adaptive threshold algorithm is proposed to detect every LDoS attack burst. Test results of network simulator (NS)‐2 simulation and test‐bed experiments indicate that the proposed detection strategy can almost completely detect LDoS attack bursts and is especially robust to legitimate short bursts.

Efficient congestion control in communications using novel weighted ensemble deep reinforcement learning

ZigBee is a sensor based IEEE 802.15.4 wireless standard. ZigBee has taken a role in the utilization of wireless network for diverse applications in home, office or industrial automation. The vast use of ZigBee devices which bring forth a huge application traffic. ZigBee Coordinator processes the application traffic. This traffic waits to be executed in the Coordinator queue. Routers transfer the application traffic to the destination. These huge application traffic can be a cause of queuing drop which reduces throughput as well as increases delay. An active queue management is in demand for ZigBee Coordinator. In this paper, Random Early Detection (RED) algorithm is applied to maintain Coordinator queue for gaining high throughput as the lowest possible delay and data packet dropped. The simulation results show the performance of RED enabled network parameters are better. This analysis will be a convenient direction for the further development of ZigBee network.

Random Early Detection(RED) is a network congestion control algorithm which calculates the packet-lossratio according to current length of average queue. This paper describes an improved RED algorithm: Firstly, predicts network flows with RBF neural Network in order to forecast queue length much earlier, and then, fits the Packet-loss-ratio of RED algorithm according to some special points using curve fitting method, therefore, controls nonlinear network congestion. From our simulation, it can be concluded that the algorithm avoids congestion more efficiently.

Schemes described in the literature on network congestion management are in general based on queue management. It is widely accepted that Poisson model is not sufficient to characterize the traffic in current Internet. In this paper, we present an alternate RED (Random Early Detection) algorithm for traffic congestion management in IP networks having self-similar (SS) input. We first discuss the basic scheme of Normal and Gentle RED as proposed by Floyd et.al., for the Poisson input model, and then explain piecewise RED, an extension of Gentle RED and Exponential RED - a new AQM proposed by us. We then explain the modification to these algorithms that we propose for the self-similar IP traffic input. Our modification takes into consideration probability values corresponding to the average queue lengths for computing the marking/dropping probability. Verification of the algorithms proposed viz., Normal SS RED and Exponential SS RED, vis-a-vis that of Floyd has been done using simulated self-similar traffic. Results of the verification have been discussed in the paper.

Congestion is an important issue which researchers focus on in the Transmission Control Protocol (TCP) network environment. To keep the stability of the whole network, congestion control algorithms have been extensively studied. Queue management method employed by the routers is one of the important issues in the congestion control study. Active Queue Management (AQM) has been proposed as a router-based mechanism for early detection of congestion inside the network. In this study, we are comparing AQM two popular queue management methods, Random Early Detection (RED) and droptail, in different aspects, such as throughput and fairness Index. The comparison results indicate RED performed slightly better with higher throughput and higher fairness Index than droptail. Simulation is done by using Network Simulator (NS2) and the graphs are drawn using X- graph.

Congestion control in cluster wireless multimedia sensor networks is capable of delivering and gathering multimedia information, while propagation delay and external interference may cause it unable to achieve the maximal network throughput without congestion control. To solve the problem, based on the additive-increase multiplicative-decrease adjustment strategy, a robust congestion control model is proposed with a scheduling scheme in cluster wireless multimedia sensor networks considering the neighbor feedback. The robust congestion control model is a Lur’e-type system. The cluster wireless multimedia sensor network is capable of achieving the desired cluster synchronization with network parameters provided by Lyapunov–Krasovskii functionals. Sufficient conditions of the robust congestion control are formulated by linear matrix inequalities. Numerical results show the effectiveness of the proposed scheme.

Random early detection (RED) is widely deployed as a congestion control mechanism in the routers. However, RED is often limited by the difficulty of tuning its parameters under different congestion levels. Adaptive RED (ARED) addresses this problem by adaptively adjusting the aggressiveness of RED settings to keep the average queue size between minimum threshold (minth) and maximum threshold (maxth ). We have observed that the additive increase multiplicative decrease (AIMD) approach used in ARED can always maintain high link utilization but not effective enough in preventing high packet loss rates under heavy congestion. In this paper, we propose an adaptive version of RED which applies non-linear power function instead of linear AIMD approach to achieve better link utilization as well as minimizing the packet loss rates under various traffic load conditions

Congestion control in TCP/AQM networks is expected to perform well for a wide-range of conditions, but recent advances in modeling and analysis indicate that present AQM (active queue management) schemes need an extra dose of adaptability to cope. The paper answers the call and proposes a self-tuning structure wherein AQM parameters are automatically tuned in response to on-line estimation of link capacity and traffic load. This approach is applicable to any AQM scheme that is parameterizable in terms of link capacity and TCP load. We describe this self-tuning structure, illustrate its application to PI (proportional-integral) and RED (random early detection) AQMs, provide stability analysis, and conduct ns simulations to compare with both fixed AQM schemes and the recently proposed adaptive RED.

This paper applies a novel optimization method for Proportional-Integral-Differential (PID) controller design with D-stable regions based on Integral of Time-weighted Absolute Error (ITAE) performance to Active Queue Management (AQM) routers that permits the designer to control the desired dynamic performance of a closed-loop system. A set of desired D-stable regions in the complex plane is first specified and then a numerical optimization algorithm based on ITAE performance is run to find the controller parameters such that all the roots of the closed-loop system are within the specified regions. This controller for AQM routers can detect and control the congestion effectively and predictively. Compared with the Random Early Detection (RED) and Proportional-Integral (PI) algorithms via experimental simulations in Network Simulator Version 2 (NS2), the proposed method, called DITAE-PID method, is indeed more efficient and robust in achieving the smaller queuing delay and higher throughput of an AQM router.