Routing with Cooperative Nodes Using Improved Learning Approaches
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.