Routing with Cooperative Nodes Using Improved Learning Approaches

作     者：R.Raja N.Satheesh J.Britto Dennis C.Raghavendra 

作者机构：Department of CSITCVR College of EngineeringTelanganaIndia Department of Computer Science and EngineeringSt.Martin’s Engineering CollegeSecunderabadTelanganaIndia Department of Computer Science and EngineeringDhanalakshmi Srinivasan UniversityTrichyTamilnaduIndia CSIT DepartmentCVR College of EngineeringHyderabadIndia 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第35卷第3期

页      面：2857-2874页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Internet of Things(IoT) stacked long short term memory bi-directional long short term memory error rate stochastic gradient descent 

摘      要：In IoT,routing among the cooperative nodes plays an incredible role in fulfilling the network requirements and enhancing system *** eva-luation of optimal routing and related routing parameters over the deployed net-work environment is *** research concentrates on modelling a memory-based routing model with Stacked Long Short Term Memory(s-LSTM)and Bi-directional Long Short Term Memory(b-LSTM).It is used to hold the routing information and random routing to attain superior *** pro-posed model is trained based on the searching and detection mechanisms to com-pute the packet delivery ratio(PDR),end-to-end(E2E)delay,throughput,*** anticipated s-LSTM and b-LSTM model intends to ensure Quality of Service(QoS)even in changing network *** performance of the proposed b-LSTM and s-LSTM is measured by comparing the significance of the model with various prevailing ***,the performance is measured with Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)for mea-suring the error rate of the *** prediction of error rate is made with Learn-ing-based Stochastic Gradient Descent(L-SGD).This gradual gradient descent intends to predict the maximal or minimal error through successive *** simulation is performed in a MATLAB 2020a environment,and the model performance is evaluated with diverse *** anticipated model intends to give superior performance in contrast to prevailing approaches.