Enhanced packet reordering procedure to improve TCP communication

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sai Prasanthi Kasimsetti ◽  
Asdaque Hussain
Keyword(s):  
Wireless Network ◽  
Search Algorithm ◽  
Research Work ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Linear Network ◽  
Content Type ◽  
Packet Reordering ◽  
Linear Network Coding

Purpose The research work is attained by Spurious Transmission–based Enhanced Packet Reordering Method (ST-EPRM). The packet reordering necessity is evaded by presenting random linear network coding process on wireless network physical layer which function on basis of sequence numbers. The spurious retransmission happening over wireless network is obtained by presenting monitoring concept for reducing number of spurious retransmissions because it might need more than three DUPACKs for triggering fast retransmit. This monitoring node performs as centralized node as well variation amid buffer length and number of packets being sent can be predicted. This information helps in differentiating spurious retransmission from the packet loss. Design/methodology/approach Based on transmission detection, action is accomplished whether to retransmit or evade transmission. Monitoring node selection is achieved by presenting improved cuckoo search algorithm. The modified support vector machine algorithm is greatly used for variation-based spurious transmission. Findings The research work which is attained by ST-EPRM. The packet reordering necessity is evaded by presenting random linear network coding process on wireless network physical layer which function on basis of sequence numbers. The spurious retransmission happening over wireless network is obtained by presenting monitoring concept for reducing number of spurious retransmissions because it might need more than three DUPACKs for triggering fast retransmit. This monitoring node performs as centralized node as well variation amid buffer length and number of packets being sent can be predicted. This information helps in differentiating spurious retransmission from the packet loss. Originality/value Based on transmission detection, action is accomplished whether to retransmit or evade transmission. Monitoring node selection is achieved by presenting improved cuckoo search algorithm. The modified support vector machine algorithm is greatly used for variation-based spurious transmission.

scholarly journals A New Hybrid Approach for Wind Speed Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Cuckoo Search Algorithm

2018 ◽  
Vol 8 (10) ◽  
pp. 1754 ◽  
Author(s):  
Tongxiang Liu ◽  
Shenzhong Liu ◽  
Jiani Heng ◽  
Yuyang Gao
Keyword(s):  
Wind Speed ◽  
Search Algorithm ◽  
Wind Farms ◽  
Cuckoo Search ◽  
Ensemble Empirical Mode Decomposition ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Wind Speed Forecasting ◽  
Mode Decomposition ◽  
Proposed Model

Wind speed forecasting plays a crucial role in improving the efficiency of wind farms, and increases the competitive advantage of wind power in the global electricity market. Many forecasting models have been proposed, aiming to enhance the forecast performance. However, some traditional models used in our experiment have the drawback of ignoring the importance of data preprocessing and the necessity of parameter optimization, which often results in poor forecasting performance. Therefore, in order to achieve a more satisfying performance in forecasting wind speed data, a new short-term wind speed forecasting method which consists of Ensemble Empirical Mode Decomposition (EEMD) for data preprocessing, and the Support Vector Machine (SVM)—whose key parameters are optimized by the Cuckoo Search Algorithm (CSO)—is developed in this paper. This method avoids the shortcomings of some traditional models and effectively enhances the forecasting ability. To test the prediction ability of the proposed model, 10 min wind speed data from wind farms in Shandong Province, China, are used for conducting experiments. The experimental results indicate that the proposed model cannot only improve the forecasting accuracy, but can also be an effective tool in assisting the management of wind power plants.

scholarly journals Dual-Mass MEMS Gyroscope Parallel Denoising and Temperature Compensation Processing Based on WLMP and CS-SVR

Micromachines ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 586 ◽  
Author(s):  
Longkang Chang ◽  
Huiliang Cao ◽  
Chong Shen
Keyword(s):  
Wavelet Transform ◽  
Support Vector Regression ◽  
Temperature Compensation ◽  
Linear Prediction ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Temperature Error ◽  
Support Vector ◽  
Mixed Features

For the sake of decreasing the effects of noise and temperature error on the measurement accuracy of micro-electro-mechanical system (MEMS) gyroscopes, a denoising and temperature drift compensation parallel model method based on wavelet transform and forward linear prediction (WFLP) and support vector regression based on the cuckoo search algorithm (CS-SVR) is proposed in this paper. First, variational mode decomposition (VMD) is proposed in this paper, which is aimed at dividing the output signal of the gyroscope into intrinsic mode functions (IMFs); then, the IMFs are classified into three features—drift, mixed, and pure noise features—by the sample entropy (SE) value. Second, a wavelet transform and forward linear prediction (WFLP) are combined to remove the noise from the mixed features. Meanwhile, the drift feature is compensated by support vector regression based on the cuckoo search algorithm (CS-SVR). Finally, through reconstruction, the final signal is obtained. Experimental results demonstrate that the VMD-SE-WFLP-CS-SVR method proposed in this paper can decrease noise and compensate the temperature error effectively (angular random walking value is optimized from 1.667°/√h to 0.0667°/√h and the bias stability is reduced from 30°/h to 4°/h). In terms of denoising, the performance of the WFLP algorithm is superior to the wavelet threshold and FLP, as it combines their advantages; furthermore, in terms of temperature compensation, the proposed CS-SVR algorithm uses the cuckoo search algorithm to find the optimal parameters of SVR, improving the accuracy of the model.

Multi-path selection based on fractional cuckoo search algorithm for QoS aware routing in MANET

Sensor Review ◽  
2019 ◽  
Vol 39 (2) ◽  
pp. 218-232 ◽  
Author(s):  
Rama Rao A. ◽  
Satyananda Reddy ◽  
Valli Kumari V.
Keyword(s):  
Ad Hoc ◽  
Search Algorithm ◽  
Life Time ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Path Selection ◽  
Content Type ◽  
Time Distance ◽  
Multiple Qos Constraints ◽  
Multiple Qos

Purpose Multimedia applications such as digital audio and video have stringent quality of service (QoS) requirement in mobile ad hoc network. To support wide range of QoS, complex routing protocols with multiple QoS constraints are necessary. In QoS routing, the basic problem is to find a path that satisfies multiple QoS constraints. Moreover, mobility, congestion and packet loss in dynamic topology of network also leads to QoS performance degradation of protocol. Design/methodology/approach In this paper, the authors proposed a multi-path selection scheme for QoS aware routing in mobile ad hoc network based on fractional cuckoo search algorithm (FCS-MQARP). Here, multiple QoS constraints energy, link life time, distance and delay are considered for path selection. Findings The experimentation of proposed FCS-MQARP is performed over existing QoS aware routing protocols AOMDV, MMQARP, CS-MQARP using measures such as normalized delay, energy and throughput. The extensive simulation study of the proposed FCS-based multipath selection shows that the proposed QoS aware routing protocol performs better than the existing routing protocol with maximal energy of 99.1501 and minimal delay of 0.0554. Originality/value This paper presents a hybrid optimization algorithm called the FCS algorithm for the multi-path selection. Also, a new fitness function is developed by considering the QoS constraints such as energy, link life time, distance and delay.

Optimization Through Nature-Inspired Soft-Computing and Algorithm on ECG Process

2017 ◽  
pp. 489-519 ◽  
Author(s):  
Goutam Kumar Bose ◽  
Pritam Pain
Keyword(s):  
Soft Computing ◽  
Search Algorithm ◽  
Removal Rate ◽  
Research Work ◽  
Cuckoo Search ◽  
Lower Surface ◽  
Cuckoo Search Algorithm ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Response Optimization

In the present research work selection of significant machining parameters depending on nature-inspired algorithm is prepared, during machining alumina-aluminum interpenetrating phase composites through electrochemical grinding process. Here during experimentation control parameters like electrolyte concentration (C), voltage (V), depth of cut (D) and electrolyte flow rate (F) are considered. The response data are initially trained and tested applying Artificial Neural Network. The paradoxical responses like higher material removal rate (MRR), lower surface roughness (Ra), lower overcut (OC) and lower cutting force (Fc) are accomplished individually by employing Cuckoo Search Algorithm. A multi response optimization for all the response parameters is compiled primarily by using Genetic algorithm. Finally, in order to achieve a single set of parametric combination for all the outputs simultaneously fuzzy based Grey Relational Analysis technique is adopted. These nature-driven soft computing techniques corroborates well during the parametric optimization of ECG process.

Reproducing Polynomial Kernel Extreme Learning Machine

2017 ◽  
Vol 21 (5) ◽  
pp. 795-802 ◽  
Author(s):  
Yibo Li ◽  
Chao Liu ◽  
Senyue Zhang ◽  
Wenan Tan ◽  
Yanyan Ding ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Reproducing Kernel ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Fast Training ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Conventional kernel support vector machine (KSVM) has the problem of slow training speed, and single kernel extreme learning machine (KELM) also has some performance limitations, for which this paper proposes a new combined KELM model that build by the polynomial kernel and reproducing kernel on Sobolev Hilbert space. This model combines the advantages of global and local kernel function and has fast training speed. At the same time, an efficient optimization algorithm called cuckoo search algorithm is adopted to avoid blindness and inaccuracy in parameter selection. Experiments were performed on bi-spiral benchmark dataset, Banana dataset, as well as a number of classification and regression datasets from the UCI benchmark repository illustrate the feasibility of the proposed model. It achieves the better robustness and generalization performance when compared to other conventional KELM and KSVM, which demonstrates its effectiveness and usefulness.

scholarly journals An EPC Forecasting Method for Stock Index Based on Integrating Empirical Mode Decomposition, SVM and Cuckoo Search Algorithm

2014 ◽  
Vol 2 (6) ◽  
pp. 481-504 ◽  
Author(s):  
Xiangfei Li ◽  
Zaisheng Zhang ◽  
Chao Huang
Keyword(s):  
Empirical Mode Decomposition ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Stock Index ◽  
Support Vector ◽  
Mode Decomposition ◽  
Parameters Selection ◽  
Predicted Values ◽  
Improved Cuckoo Search Algorithm

AbstractIn order to improve the forecasting accuracy, a hybrid error-correction approach by integrating support vector machine (SVM), empirical mode decomposition (EMD) and the improved cuckoo search algorithm (ICS) was introduced in this study. By using two indexes as examples, the empirical study shows our proposed approach by means of synchronously predict the prediction error which used to correct the preliminary predicted values has better prediction precision than other five competing approaches, furthermore, the improved strategies for cuckoo search algorithm has better performance than other three evolutionary algorithms in parameters selection.

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