The Research of Adaptive Network Congestion Control Algorithm Based on AQM

As the study on and promotion of wireless sensor networks (WSN) are deepening, people have increasingly higher requirements for the transmission speed and transmission performance of wireless sensor networks. Network congestion is an important factor of the transmission performance of a network, so congestion control becomes a hot research topic in the studies on the improvement of WSN services. Based on the study on WSN congestion control mechanism, this paper proposes an adaptive congestion control algorithm and simulates the algorithm. The simulation result shows that this algorithm can ensure stable network performance.

In order to guarantee traffic flow medium in network, congestion control strategy is adopted in traffic flow medium. According to the delay sensitivity of flow medium data, this paper proposes a novel congestion control for traffic flow medium in network known as Adaptive Delay Congestion Control (ADCC). ADCC improves the QoS of multimedia stream in Network by detecting and discarding these packets that their accumulated delay exceeds multimedia stream's tolerance delay threshold, and maintain high bandwidth utilization. Another notable feature of the scheme is that ADCC implement congestion control by adaptive adjusting the switch device's queue length threshold according to propagation delay. Simulation results shows that the proposed scheme can effectively reduce the average delay than that of using traditional congestion control algorithm. Moreover, it is suitable to be used in the ever-changing network environment and can improve utilization of the network resources.

Channel congestion is one of the most critical issues in IEEE 802.11p-based vehicular communications as it leads to the unreliability of safety applications. As a countermeasure, many Decentralized Congestion Control (DCC) algorithms have been proposed. One of the most prominent DCC algorithms is the message-rate based LIMERIC. Recently, algorithms have also been proposed to support higher vehicle density (better scalability). One of such algorithms is the combined message-rate and data-rate based congestion control algorithm (MD-DCC). MD-DCC can support around 2.7 times higher vehicular density than LIMERIC. However, if LIMERIC has been deployed, can MD-DCC be introduced and coexist well with LIMERIC. The objective of this paper is to investigate how MD-DCC coexists with LIMERIC. Given a scenario where vehicles may use either LIMERIC or MD-DCC, we study the impact of coexistence on channel load, fairness and reliability of vehicles at different densities. Simulation studies show that there is no significant degradation of reliability both for LIMERIC and MD-DCC at different densities. On the contrary, coexistence can improve the reliability of LIMERIC vehicles. Furthermore, MD-DCC can support vehicles at large densities even when it coexists with LIMERIC retaining its scalability. However, fair allocation of resources is not guaranteed when LIMERIC and MD-DCC coexist.

In recent years, hybrid congestion control (CC) algorithms that combine rule-based CC and learning-based CC have gained significant attention. They incorporate the fast adaption ability of learning-based CC and the stability of rule-based CC, tending to select the better-performing rate based on the network feedback. However, the practical implementation of such algorithms has revealed primary issues. Specifically, they require both CCs to run alternately, which results in a poorly performing CC continuing to run in the network. Moreover, hybrid CCs cannot converge to the optimal rate when both CCs perform poorly. This paper proposes Anole to address these issues. Anole has three main algorithmic contributions: 1) Anole always selects the better-performing CC, 2) Anole temporarily deprecates the consistently underperforming CC, 3) when both CCs pervform poorly, Anole infers the optimal sending rate based on the network feedback. We carry out comprehensive experiments in both emulated and real-world wired networks, as well as in real-world WiFi networks, to assess the performance of Anole. The experiment results demonstrate that Anole achieves approximately 6% higher throughput in real-world links and 34% lower delay in the 48Mbps link compared to the state-of-the-art CC. Anole also exhibits superior performance in adaptability and fair convergence.

Developing closed-loop brain stimulation systems can benefit the treatment of neurological and neuropsychiatric disorders and facilitate brain functions. Current designs of closed-loop controllers have used time-invariant linear models of brain activity to devise non-adaptive controllers. However, unmodeled nonlinear dynamics can happen during real-time closed-loop control, leading to nonlinear uncertainty in the brain activity model. Current non-adaptive controllers cannot track the nonlinear model uncertainty and are not robust to noise, both of which can compromise their control performance. Here, within an ℒ1 adaptive control framework, we develop a new discrete-time robust and adaptive closed-loop control algorithm that addresses a general form of nonlinear model uncertainty. We conduct Monte Carlo simulations to validate the robust and adaptive control algorithm and show that it significantly outperforms existing closed-loop control algorithms. Our results can facilitate future designs of precise and safe closed-loop brain stimulation systems to treat neurological and neuropsychiatric disorders and modulate brain functions.

Control algorithms are the most critical aspects in the successful control of civil structures subjected to earthquake and wind forces. In recent years, adaptive intelligent control algorithms are emerging as an acceptable substitute method to conventional model-based control algorithms. These algorithms mainly work on the principles of artificial intelligence (AI) and soft computing (SC) methods that make them highly efficient in controlling highly nonlinear, time-varying, and time-delayed complex civil structures. The current research probes to control algorithms, that this article set forth an inclusive state-of-the-art review of adaptive intelligent control (AIC) algorithms for vibration control of smart civil structures. First, a general introduction to adaptive intelligent control is presented along with its advantages over conventional control algorithms. Second, their classification concerning artificial intelligence and soft computing methods is provided that mainly consists of artificial neural network-based controller, brain emotional learning-based intelligent controller, replicator dynamics-based controller, multi-agent system-based controller, support vector machine-based controller, fuzzy logic control, adaptive neuro-fuzzy inference system-based controller, adaptive filters-base controller, and meta-heuristic algorithms-based hybrid controllers. Third, a brief review of these algorithms with their developments on the theory and applications is provided. Fourth, we demonstrate a summarized overview of the cited literature with a brief trend analysis is presented. Finally, this study presents an overview of these innovative AIC methods that can demonstrate future directions. The contribution of this article is the anticipation of detailed and in-depth discussion into the perspective of AI and SC-based AIC method advances that enabled practical applications in attenuating vibration response of smart civil structures. Moreover, the review demonstrates the computing advantages of AIC over conventional controllers that are important in creating the next generation of smart civil structures.

Development, Comparison and Application of Various Parameter-Adaptive Digital Control Algorithms

The rapid evolution of computer networks, increase in the number of Internet users, and popularity of multimedia applications have exacerbated the congestion control problem. Congestion control is a key factor in ensuring network stability and robustness. When the underlying network and flow information are unknown, the transmission control protocol (TCP) must increase or reduce the size of the congestion window to adjust to the changes of traffic in the Internet Protocol (IP) network. However, it is possible that a software-defined approach can relieve the network congestion problem more efficiently. This approach has the characteristic of centralized control and can obtain a global topology for unified network management. In this paper, we propose a software-defined congestion control (SDCC) algorithm for an IP network. We consider the difference between TCP and the user datagram protocol (UDP) and propose a new method to judge node congestion. We initially apply the congestion control mechanism in the congested nodes and then optimize the link utilization to control network congestion.

Congestion control and avoidance in Wireless Sensor Networks (WSNs) is a subject that has attracted a lot of research attention in the last decade. Besides rate and resource control, the utilization of mobile nodes has also been suggested as a way to control congestion. In this work, we present a Mobile Congestion Control (MobileCC) algorithm with two variations, to assist existing congestion control algorithms in facing congestion in WSNs. The first variation employs mobile nodes that create locally-significant alternative paths leading to the sink. The second variation employs mobile nodes that create completely individual (disjoint) paths to the sink. Simulation results show that both variations can significantly contribute to the alleviation of congestion in WSNs.

Congestion control is a fundamental technology to balance the traffic load and the network. The Internet Engineering Task Force (IETF) Quick UDP Internet Connection (QUIC) protocol has flexible congestion control and at the same time possesses the advantages of high efficiency, low latency, and easy deployment at the application layer. Bottleneck bandwidth and round‐trip propagation time (BBR) is an optional congestion control algorithm adopted by QUIC. BBR can significantly increase throughput and reduce latency, in particular over long‐haul paths. However, BBR results in high packet loss in low bandwidth and low fairness in multi‐stream scenarios. In this article, we propose the enhanced BBR congestion control (eBCC) algorithm, which improves the BBR algorithm in two aspects: (1) 10.87% higher throughput and 74.58% lower packet loss rate in the low‐bandwidth scenario and (2) 8.39% higher fairness in the multi‐stream scenario. This improvement makes eBCC very suitable for IoT communications to provide better QoS services.

Problem statement: Transmission Control Protocol is the Internet’s most widely used transport control protocol. TCP’s strength lies in the adaptive nature of its congestion avoidance and control algorithm and its retransmission mechanism. In TCP Vegas rerouting (a path), which change the propagation delay of the connection and this may be able to result in a substantial decrease in through put. An enhanced algorithm for TCP in wireless Ad Hoc networks is needed to obtain a fairer share of the available bandwidth, tackle re-routing problems and solve the problems associated with older TCP Vegas flows. Approach: TCP-Vegas uses an Estimation of the propagation delay, base RTT, to adjust its window size and it is very important for a TCP Vegas connection to be able to have an accurate estimation. Results: One of the issues is rerouting (a path), which change the propagation delay of the connection and this may be able to result in a substantial decrease in through put. The issue identified with TCP Vegas was reinvestigated and to address them, a modification to TCP Vegas’s congestion avoidance algorithm is proposed. Conclusion: This modified TCP enhancement algorithams is shown to obtain a fairer share of the available bandwidth, tackle re-routing problems and solve the problems associated with older TCP Vegas flows.

Parametric study of nonlinear adaptive control algorithm with magneto-rheological suspension systems

In 2016, Google proposed the bottleneck bandwidth round-trip propagation time (BBR) as a new congestion control algorithm for improving network speed. As BBR has different behavioral characteristics from loss-based congestion control, it must ensure the coexistence between congestion control algorithms. However, throughput imbalance has been reported in a competition between BBR and loss-based congestion control. In this paper, we propose an opponent congestion control identification model of BBR (OI-BBR) with machine learning to improving inter-protocol fairness. In the emulation experiments, we confirmed that OI-BBR based on the proposed model improved the inter-protocol fairness by operating differently according to the opponent congestion control algorithms.

Congestion control is a fundamental mechanism for TCP protocol, which has been extensively studied in the past three decades. However, our experimental evaluations show that the state-of-art congestion control algorithms such as Cubic and BBR are far from optimal: they have unresolved issues such as insufficient usage of available bandwidth, inadaptable to dynamic bandwidth variants, and compromising on one or more performance dimensions. To address these challenges, we propose a novel congestion control mechanism called NeuRoc that coordinately uses online changepoint detection and deep reinforcement learning (DRL) technique to generate the optimal congestion control policy, which allows TCP operating at Kleinrock ’s optimal operation point to achieve fully bandwidth usage and low latency. To address the practical issues of deploying the deep learning based congestion control mechanism, we propose a cold-started training and deployment framework to reduce the cost of bootstrap. We implement NeuRoc on an emulation platform which connects to the Linux network protocol stack through virtual network interfaces. Extensive experiments show that NeuRoc achieves the best throughput-latency tradeoff compared with the state-of-the-arts in a variety of scenarios.

Deeper investigation is needed for several open issues related to congestion and admission control within the field of the high speed B‐ISDN network with ATM protocol. The system must be more effective in resource management and robust enough to cope with changes in traffic environment, which nowadays is characterised by sophisticated multimedia services. In this paper a new admission and congestion control strategy is proposed. It originates from the Fast Buffer Reservation (FBR) scheme, a virtual circuit acceptance algorithm proposed by Prof. Turner, and enhances its effectiveness, thus resulting in a complete approach for bandwidth management. The scheme is called Modified FBR (M‐FBR). The use of the FBR method, in fact, when based on the powerful Excess Probability Criterion (EPC), seemed to be impeded by the occurrence of lock out problems. By adopting a new kind of service at the buffer exit, the new mechanism offers the required flexibility and robustness, as well as ease of control, while preserving burst integrity and exhibiting a high burst‐served to burst‐sent ratio.