GA-PSO-Optimized Neural-Based Control Scheme for Adaptive Congestion Control to Improve Performance in Multimedia Applications

Active queue control aims to improve the overall communication network throughput while providing lower delay and small packet loss rate. The basic idea is to actively trigger packet dropping (or marking provided by explicit congestion notification (ECN)) before buffer overflow. In this paper, two artificial neural networks (ANN)-based control schemes are proposed for adaptive queue control in TCP communication networks. The structure of these controllers is optimized using genetic algorithm (GA) and the output weights of ANNs are optimized using particle swarm optimization (PSO) algorithm. The controllers are radial bias function (RBF)-based, but to improve the robustness of RBF controller, an error-integral term is added to RBF equation in the second scheme. Experimental results show that GA- PSO-optimized improved RBF (I-RBF) model controls network congestion effectively in terms of link utilization with a low packet loss rate and outperform Drop Tail, proportional-integral (PI), random exponential marking (REM), and adaptive random early detection (ARED) controllers.Comment: arXiv admin note: text overlap with arXiv:1711.0635

A bio-knowledge based method to prevent control system instability

This study presents a novel bio-inspired method, based on gain scheduling, for the calculation of Proportional-Integral-Derivative (PID) controller parameters that will prevent system instability. The aim is to prevent a transition to control system instability due to undesirable controller parameters that may be introduced manually by an operator. Each significant operation point in the system is firstly identified. Then, a solid stability structure is calculated, using transfer functions, in order to program a bio-inspired model by using an artificial neural network. The novel method is empirically verified under working conditions in a liquid-level laboratory plant

A UNIQUE MATHEMATICAL QUEUING MODEL FOR WIRED AND WIRELESS NETWORKS

The de-facto protocol for transmitting data in wired and wireless networks is the Transmission Control Protocol/Internet Protocol (TCP/IP). While a lot of modifications have been done to adapt the TCP/IP protocol for wireless networks, a lot remains to be done about the bandwidth underutilization caused by network traffic control actions taken by active queue management controllers currently being implemented on modern routers. The main cause of bandwidth underutilization is uncertainties in network parameters. This is especially true for wireless networks. In this study, two unique mathematical models for queue management in wired and wireless networks are proposed. The models were derived using a recursive, thirdorder, discrete-time structure. The models are; the Model Predictive Controller (MPC) and the Self-Tuning Regulator (STR). The MPC was modeled to bear uncertainties in gain, poles and delay time. The STR, with an assigned closed-loop pole, was modeled to be very robust to varying network parameters. Theoretically, the proposed models deliver a performance in network traffic control that optimizes the use of available bandwidth and minimizes queue length and packet loss in wired and wireless networks

A bio-inspired robust controller for a refinery plant process

This research presents a novel bio-inspired knowledge method, based on gain scheduling, for the calculation of Proportional-Integral-Derivative controller parameters that will prevent system instability. The aim is to prevent a transition to control system instability due to undesirable controller parameters that may be introduced manually by an operator. Each significant operation point in the system is identified first. Then, a solid stability structure is calculated, using transfer functions, in order to program a bio-inspired model by using an artificial neural network. The novel method is empirically verified under working conditions in a real refinery plant process

A UNIQUE MATHEMATICAL QUEUING MODEL FOR WIRED AND WIRELESS NETWORKS

The de-facto protocol for transmitting data in wired and wireless networks is the Transmission Control Protocol/Internet Protocol (TCP/IP). While a lot of modifications have been done to adapt the TCP/IP protocol for wireless networks, a lot remains to be done about the bandwidth underutilization caused by network traffic control actions taken by active queue management controllers currently being implemented on modern routers. The main cause of bandwidth underutilization is uncertainties in network parameters. This is especially true for wireless networks. In this study, two unique mathematical models for queue management in wired and wireless networks are proposed. The models were derived using a recursive, thirdorder, discrete-time structure. The models are; the Model Predictive Controller (MPC) and the Self-Tuning Regulator (STR). The MPC was modeled to bear uncertainties in gain, poles and delay time. The STR, with an assigned closed-loop pole, was modeled to be very robust to varying network parameters. Theoretically, the proposed models deliver a performance in network traffic control that optimizes the use of available bandwidth and minimizes queue length and packet loss in wired and wireless networks

A Novel Self-tuning Feedback Controller for Active Queue Management Supporting TCP Flows

[[abstract]]Wireless access points act as bridges between wireless and wired networks. Since the actually available bandwidth in wireless networks is much smaller than that in wired networks, there is a bandwidth disparity in channel capacity which makes the access point a significant network congestion point. The recently proposed active queue management (AQM) is an effective method used in wired network and wired–wireless network routers for congestion control, and to achieve a tradeoff between channel utilization and delay. The de facto standard, the random early detection (RED) AQM scheme, and most of its variants use average queue length as a congestion indicator to trigger packet dropping. In this paper, we propose a Novel autonomous Proportional and Differential RED algorithm, called NPD-RED, as an extension of RED. NPD-RED is based on a self-tuning feedback proportional and differential controller, which not only considers the instantaneous queue length at the current time point, but also takes into consideration the ratio of the current differential error signal to the buffer size. Furthermore, we give theoretical analysis of the system stability and give guidelines for the selection of feedback gains for the TCP/RED system to stabilize the instantaneous queue length at a desirable level. Extensive simulations have been conducted with ns2. The simulation results have demonstrated that the proposed NPD-RED algorithm outperforms the existing AQM schemes in terms of average queue length, average throughput, and stability