RED Algorithm Research Articles

Developed the EX-RED Algorithm to Find Best Parameters Based on Load Connection Factor

The performance of the Internet is significantly impacted by network congestion. Because of the internet's current rapid growth, congestion could increase and cause more packets to be dropped. The Transmission Control Protocol (TCP) connection is used as the reliable transmission of packets which has a Drop Tail (DT) mechanism since congestion DT signals occur only when the queue has become full. The Random Early Detection algorithm mechanism can be developed as congestion control which eliminates the drawback of a tail drop queue full. The Red algorithm also has problems dealing with different numbers of connections due to fix parameter tuning. In this study, the EXhaustive search used with the RED algorithm to develop the EX-RED algorithm. Based on network performance metrics such as packet drop, delay, and throughput, the developed algorithm adjusts the default RED parameter to find the best of them. When the number of TCP connections changes, the exhaustive search used systematically enumerates all possible states of the parameter. The simulation results showed that the EXRED improved performance of the network as compared with the other five algorithms (GRED, RED, ARED, NLRED, and NLGRED) by decreasing delay and dropped packets and increasing packet throughput. Index Terms— AQM, Congestion Control, Exhaustive search, RED.

Design and Application of a Resource Allocation Method for CAEVs Internet of Things Based on Swarm Intelligence Computing

The Internet of Things (IoT) faces significant challenges in the requirements of sensitive task latency, reasonable resource allocation and reliability for resource transactions. This paper introduces a novel method for road resource allocation in the IoT context of connected and autonomous electric vehicles (CAEVs). The proposed algorithm leverages the ant colony algorithm (ACA) to effectively allocate and coordinate road resources within groups of CAEVs. By considering the energy consumption and pheromone volatilization, the allocation and coordination process of road resources are optimized. To improve the linear packet loss of RED, we adopt the advanced ACA and CRED in the NS2 platform. The experimental results demonstrate that the proposed method outperforms the RED algorithm in packet loss rate and delay time, significantly enhancing system efficiency and performance. Furthermore, the combination of the CRED algorithm and ant colony algorithm successfully mitigates short-term congestion and identifies optimized paths with minimal delay.

Efficient Privacy-Preserving Red Deer Optimization Algorithm with Blockchain Technology for Clustered VANET

Efficient Privacy-Preserving Red Deer Optimization Algorithm with Blockchain Technology for Clustered VANET

A multi-objective mathematical model of financial flows in Omni-Channel distribution systems

Nowadays, companies and financial institutions have different strategies and priorities aligned with their financial goals and digital maturity; however, they rapidly make efforts to meet customers’ ever-changing expectations. One of the technologies that can meet the needs and expectations of customers faster and more accurately is the Omni-channel distribution system. Within this system, a set of distribution channels is defined. In addition to increasing the level of service and customer satisfaction, the amount of product demand and sales increases, resulting in more revenue and profit. The present study aimed to design a mathematical multi-objective Mixed Integer Linear Programming (MOMILP) model to investigate the relationship between different components of an Omni-Channel system and an innovative retail business model in the form of a financial approach for the optimization of profitability. In order to confirm the accuracy of the proposed mathematical model, numerical experiments were carried out using near-reality data based on an integration of Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Red Deer Algorithm (RDA). To solve the problem, the input parameters of this algorithm and mathematical model were provided in the form of problems with three sizes: small, medium, and large. The problem with the small size provided a fast and highly precise optimized response.

Utilization of Modified NDVIred and red edge algorithm for analysis of Mangrove ecosystem conditions in Lembar bay area of West Lombok Indonesia

Utilization of Modified NDVIred and red edge algorithm for analysis of Mangrove ecosystem conditions in Lembar bay area of West Lombok Indonesia

An automated deep learning based pancreatic tumor diagnosis and classification model using computed tomography images

PurposeThe advancements of deep learning (DL) models demonstrate significant performance on accurate pancreatic tumor segmentation and classification.Design/methodology/approachThe presented model involves different stages of operations, namely preprocessing, image segmentation, feature extraction and image classification. Primarily, bilateral filtering (BF) technique is applied for image preprocessing to eradicate the noise present in the CT pancreatic image. Besides, noninteractive GrabCut (NIGC) algorithm is applied for the image segmentation process. Subsequently, residual network 152 (ResNet152) model is utilized as a feature extractor to originate a valuable set of feature vectors. At last, the red deer optimization algorithm (RDA) tuned backpropagation neural network (BPNN), called RDA-BPNN model, is employed as a classification model to determine the existence of pancreatic tumor.FindingsThe experimental results are validated in terms of different performance measures and a detailed comparative results analysis ensured the betterment of the RDA-BPNN model with the sensitivity of 98.54%, specificity of 98.46%, accuracy of 98.51% and F-score of 98.23%.Originality/valueThe study also identifies several novel automated deep learning based approaches used by researchers to assess the performance of the RDA-BPNN model on benchmark dataset and analyze the results in terms of several measures.

Adaptive Deep Learning Strategy with Red Deer Algorithm for Sparse Channel Estimation and Hybrid Precoding in Millimeter Wave Massive MIMO-OFDM systems

Adaptive Deep Learning Strategy with Red Deer Algorithm for Sparse Channel Estimation and Hybrid Precoding in Millimeter Wave Massive MIMO-OFDM systems

Opposition based red wolf algorithm for solving optimal reactive power problem

<p>This paper presents an opposition based red wolf optimization (ORWO) algorithm for solving optimal reactive power problem. Each red wolf has a flag vector in the algorithm, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). In this proposed algorithm, red wolf optimization algorithm has been intermingled with opposition-based learning (OBL). By this amalgamate procedure the convergence speed of the proposed algorithm will be increased. To discover an improved candidate solution, the concurrent consideration of a probable and its corresponding opposite are estimated which is closer to the global optimum than an arbitrary candidate solution. Proposed algorithm has been tested in standard IEEE 14-bus and 300-bus test systems. The simulation results show that the proposed algorithm reduced the real power loss considerably.</p>

Calibration of Structured Light Scanning System

Abstract On the basis of studying the calibration method of the camera and the rotating platform, we designed an automatic camera calibration scheme based on four azimuth circles, which can realize the automatic sorting of the mark points. In order to improve the efficiency of identifying the center of the calibration plate, this paper uses RANSAC to improve the RED ellipse center detection algorithm. Experimental verification shows that the improved RED algorithm has increased 29.34% in anti-noise interference ability and 30.10% in center detection accuracy, which effectively guarantees the measurement stability and accuracy of the measurement system. Then, with the aid of the designed calibration board, we fit the rotation center of the turntable using the principle of three points in a circle, which provides a basis for the subsequent point cloud splicing. Experiments show that the back-projection error of the calibration method in this paper is less than 1 pixel, and the calibration accuracy is better than Zhang’s algorithm.

Queueing systems with different types of renovation mechanism and thresholds as the mathematical models of active queue management mechanism

This article is devoted to some aspects of using the renovation mechanism (different types of renovation are considered, definitions and brief overview are also given) with one or several thresholds as the mathematical models of active queue management mechanisms. The attention is paid to the queuing systems in which a threshold mechanism with renovation is implemented. This mechanism allows to adjust the number of packets in the system by dropping (resetting) them from the queue depending on the ratio of a certain control parameter with specified thresholds at the moment of the end of service on the device (server) (in contrast to standard RED-like algorithms, when a possible drop of a packet occurs at the time of arrivals of next packets in the system). The models with one, two and three thresholds with different types of renovation are under consideration. It is worth noting that the thresholds determine not only from which place in the buffer the packets are dropped, but also to which the reset of packets occurs. For some of the models certain analytical and numerical results are obtained (the references are given), some of them are only under investigation, so only the mathematical model and current results may be considered. Some results of comparing classic RED algorithm with renovation mechanism are presented.

A novel hybrid control technique for bifurcation in an exponential RED algorithm

SummaryThis paper introduces a new hybrid control strategy to control the bifurcation of the delayed congestion model. The delay or gain is taken as the bifurcation parameter to analyze the characteristic root distribution of the linearized system. The stability and existence conditions of Hopf bifurcation are obtained. The results show that by selecting the appropriate method, the dynamics of the controlled system can be changed, and the nonzero equilibrium constant control parameters are maintained. It reveals that when such bifurcation is undesirable or desirable, the hybrid controller can delay or advance the onset of the intrinsic bifurcation. Finally, the case study illustrates and visualizes the effectiveness of the new control strategy in controlling Hopf bifurcation.

Red Congestion Control with Energy Aware Auction Based Route Selection in MANET

Congestion is a very big issue in mobile ad hoc network. In MANET has various congestion control algorithm to solve this issue. RED algorithm is the one of the congestion control algorithm. It increasing buffer space of the queue and it reduce the packet loss and control transmission delay. In the proposed technique first fine out the energy aware auction based route selection then used the red algorithm to control the congestion. This techniques give a better result comparatively other techniques. Reduce the transmission delay so it increase the network throughput and avoid the packet losses and also give a better packet delivery ratio.

An Adjust Duty Cycle Method for Optimized Congestion Avoidance and Reducing Delay for WSNs

With the expansion of the application range and network scale of wireless sensor networks in recent years, WSNs often generate data surges and delay queues during the transmission process, causing network paralysis, even resulting in local or global congestion. In this paper, a dynamically Adjusted Duty Cycle for Optimized Congestion based on a real-time Queue Length (ADCOC) scheme is proposed. In order to improve the resource utilization rate of network nodes, we carried out optimization analysis based on the theory and applied it to the adjustment of the node’s duty cycle strategy. Using this strategy to ensure that the network lifetime remains the same, can minimize system delay and maximize energy efficiency. Firstly, the problems of the existing RED algorithm are analyzed. We introduce the improved SIG-RED algorithm into the ADCOC mechanism. As the data traffic changes, the RED protocol cannot automatically adjust the duty cycle. A scheduler is added to the buffer area manager, referring to a weighted index of network congestion, which can quickly determine the status of network congestion. The value of the weighting coefficient W is adjusted by the Bayesian method. The scheduler preferably transmits severely urgent data, alleviating the memory load. Then we combined improved data fusion technology and information gain methods to adjust the duty cycle dynamically. By simulating the algorithm, it shows that it has faster convergence speed and smaller queue jitter. Finally, we combine the adjusted congestion weight and the duty cycle growth value to adjust the data processing rate capability in the real-time network by dynamically adjusting it to adapt to bursts of data streams. Thus, the frequency of congestion is reduced to ensure that the system has higher processing efficiency and good adaptability.

Congestion Control in Cable Network Transmission using Novel RED Algorithm

Congestion is an important issue in cable network where transmitted packets to the network are much more than holding capacity of network. Each network needs the maximum number of successful packets to reach the destination with less number of packet losses and low time delay with better fairness. There are several congestion control algorithms which have been studied to keep the network stable. They employ various techniques from which the buffer or queue management used by the router is one significant issue to control congestion. The Active Queue Management algorithms like RED controls the queue size by dropping or marking the packets before buffer becomes full . In this paper a new algorithm has been proposed and the results are compared with existing RED algorithm using network simulator (NS-2) in a responsive environment of the cable network.

Agile two-stage lot-sizing and scheduling problem with reliability, customer satisfaction and behaviour under uncertainty: a hybrid metaheuristic algorithm

ABSTRACT This article proposes a new multi-objective model for a lot-sizing and scheduling problem (LSSP) under uncertainty. The model considers economic aspects, reliability and quality inspection, and customer satisfaction and behaviour in designing the LSSP. A utility function is applied to increase customer satisfaction and maximize responsiveness. In addition, the adaptive neuro-fuzzy inference system is employed to address uncertain demands. The presented model uses a fuzzy c-means clustering method to assess customers' behaviour. A hybrid multi-objective metaheuristic algorithm, comprised of the multi-objective red deer algorithm and parallel non-dominated sorting genetic algorithm-II, is applied to solve the model efficiently. The results obtained from experiments on several problem instances show the superiority of the proposed metaheuristic algorithm over other algorithms, such as multi-objective particle swarm optimization, used in this article. Finally, a real case study is presented to show the applicability of the model, and several analyses are implemented to extend managerial insights.

ACTIVE POWER LOSS DIMINUTION & VOLTAGE STABILITY ENHANCEMENT BY RED WOLF OPTIMIZATION ALGORITHM

In this paper optimal reactive power dispatch problem (ORPD), has been solved by Enriched Red Wolf Optimization (ERWO) algorithm. Projected ERWO algorithm hybridizes the wolf optimization (WO) algorithm with swarm based algorithm called as particle swarm optimization (PSO) algorithm. In the approach each Red wolf has a flag vector, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). Exploration capability of the projected Red wolf optimization algorithm has been enriched by hybridization of both WO with PSO. Efficiency of the projected Enriched Red wolf optimization (ERWO) evaluated in standard IEEE 30 bus test system. Simulation study indicates Enriched Red wolf optimization (ERWO) algorithm performs well in tumbling the actual power losses& particularly voltage stability has been enriched.

BANKS CAPACITOR COMPENSATION FOR CRITICAL NODAL DETECTION BY AUGMENTED RED WOLF OPTIMIZATION ALGORITHM

In this paper Banks Capacitor Compensation for Critical Nodal Detections by Augmented Red Wolf Optimization Algorithm has been worked out. Projected ERWO algorithm hybridizes the wolf optimization (WO) algorithm with swarm based algorithm called as particle swarm optimization (PSO) algorithm. In the approach each Red wolf has a flag vector, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). Exploration capability of the projected Red wolf optimization algorithm has been enriched by hybridization of both WO with PSO. Efficiency of the projected Enriched Red wolf optimization (ERWO) is tested in standard IEEE 57 bus test system. Simulation study indicates Enriched Red wolf optimization (ERWO) algorithm performs well in tumbling the actual power losses.

Implementation of Hybrid Modified RED Algorithm for Congestion Avoidance in MANETS

Implementation of Hybrid Modified RED Algorithm for Congestion Avoidance in MANETS

Research on a Method of Geographical Information Service Load Balancing

Abstract. With the development of geographical information service technologies, how to achieve the intelligent scheduling and high concurrent access of geographical information service resources based on load balancing is a focal point of current study. This paper presents an algorithm of dynamic load balancing. In the algorithm, types of geographical information service are matched with the corresponding server group, then the RED algorithm is combined with the method of double threshold effectively to judge the load state of serve node, finally the service is scheduled based on weighted probabilistic in a certain period. At the last, an experiment system is built based on cluster server, which proves the effectiveness of the method presented in this paper.

Computational and Simulation Models of the Control System on Modelica

When modeling network protocols, the choice of a model approach and a software implementation tool is a problem. The specificity of this subject area is that for the description of protocols usually the discrete-event approach is used. However, the discrete model approach has several disadvantages. It is poorly scalable, not well suited for describing dynamic systems. As an alternative to the discrete approach, a continuous approach is usually considered. But when modeling discrete events, continuous description becomes unnecessarily complicated and heavy. Events take the form of some restrictions on the continuous system, which are often not explicitly included in the continuous model, but have the form of additional semantic descriptions. The authors propose to use a hybrid (continuous-discrete) approach when modeling such systems. In the framework of the hybrid approach, the discrete system is recorded in a continuous form, and the events take the form of discrete transitions inherent in the approach. In addition, if it is based on the description of events, a simulation model can be obtained on the basis of a hybrid approach. This paper demonstrates the use of a hybrid approach to describe systems with control by the example of the interaction of the TCP protocol and the RED algorithm. The simplicity of creating both computational and simulation models of the system is demonstrated. The Modelica language is used as the implementation language.