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Deep Learning-based Wireless Signal Classification in the IoT Environment

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Computers, Materials & Continua 2022年第6期71卷 5717-5732页

作者： Hyeji Roh Sheungmin Oh Hajun Song Jinseo Han sangsoon lim Department of Computer Engineering Sungkyul UniversityAnyang430742Korea

With the development of the Internet of Things(IoT),diverse wireless devices are increasing *** devices have different wireless interfaces that generate incompatible wireless *** signal has its own physical characteristics with signal modulation and demodulation *** there exist different wireless devices,they can suffer from severe Cross-Technology Interferences(CTI).To reduce the communication overhead due to the CTI in the real IoT environment,a central coordinator can be able to detect and identify wireless signals existing in the same communication *** paper investigates how to classify various radio signals using Convolutional Neural Networks(CNN),Long Short-TermMemory(LSTM)and attention *** can reduce the amount of computation by reducing weights by using convolution,and LSTM belonging to RNNmodels can alleviate the long-term dependence ***,attention mechanism can reduce the short-term memory problem of RNNs by reexamining the data output from the decoder and the entire data entered into the encoder at every point in *** accurately classify radio signals according to their weights,we design a model based on CNN,LSTM,and attention *** a result,we propose a model CLARINet that can classify original data by minimizing the loss and detects changes in *** a case of the real IoT environment with Wi-Fi,Bluetooth and ZigBee devices,we can normally obtain wireless signals from 10 to 20 *** accuracy of CLARINet’s radio signal classification with CNN-LSTM and attention mechanism can be seen that signal-to-noise ratio(SNR)exhibits high accuracy at 16 dB to about 92.03%.

关键词： Attention mechanism wireless signal CNN-LSTM classification deep-learning

An Intelligent Cluster Optimization Algorithm for Smart Body Area Networks

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Computers, Materials & Continua 2021年第6期67卷 3795-3814页

作者： Adil Mushtaq Muhammad Nadeem Majeed Farhan Aadil Muhammad Fahad Khan sangsoon lim Department of Software Engineering University of Engineering and TechnologyTaxilaPakistan Department of Computer Science COMSATS University IslamabadAttock CampusPakistan Department of Computer Engineering Sungkyul UniversityAnyang430010Korea

Body Area Networks(BODYNETs)or Wireless Body Area Networks(WBAN),being an important type of ad-hoc network,plays a vital role in multimedia,safety,and traffic management *** BODYNETs,rapid topology changes occur due to high node mobility,which affects the scalability of the *** clustering is one mechanism among many others,which is used to overcome this issue in *** are many clustering algorithms used in this domain to overcome this ***,these algorithms generate a large number of Cluster Heads(CHs),which results in scarce resource utilization and degraded *** this research,an efficient clustering technique is proposed to handle these *** transmission range of BODYNET nodes is dynamically tuned accordingly as per their operational *** optimizing the transmission range,the packet loss ratio is minimized,and link quality is improved,which leads to reduced energy *** select optimal CHs the Whale Optimization Algorithm(WOA)is used based on their fitness,which enhances the network performance by reducing routing *** proposed scheme outclasses the existing state-of-the-art techniques,e.g.,Ant Colony Optimization(ACO),Gray Wolf Optimization(GWO),and Dragonfly Optimization Algorithm(DFA)in terms of energy consumption and cluster building time.

关键词： Bodynets WBAN clustering ad-hoc networks whale optimizer artificial neural networks intelligent transportation system

Remote Health Monitoring Using IoT-Based Smart Wireless Body Area Network

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Computers, Materials & Continua 2021年第8期68卷 2499-2513页

作者： Farhan Aadil Bilal Mehmood Najam Ul Hasan sangsoon lim Sadia Ejaz Noor Zaman Computer Science Department COMSATS University IslamabadAttock Campus43600Pakistan Department of Electrical and Computer Engineering Dhofar UniversitySalalahOman Department of Computer Engineering Sungkyul UniversityAnyang430010Korea School of Computer Science and Engineering(SCE) Taylor’s UniversityMalaysia

A wireless body area network(WBAN)consists of tiny healthmonitoring sensors implanted in or placed on the human *** sensors are used to collect and communicate human medical and physiological data and represent a subset of the Internet of Things(IoT)*** are connected to medical servers that monitor patients’*** type of network can protect critical patients’lives due to the ability to monitor patients’health continuously and *** inter-WBAN communication provides a dynamic environment for patients allowing them to move ***,during patient movement,the WBAN patient nodes may become out of range of a remote base ***,to handle this problem,an efficient method for inter-WBAN communication is *** this study,a method using a cluster-based routing technique is *** the proposed method,a cluster head(CH)acts as a gateway between the cluster members and the external network,which helps to reduce the network’s *** clustering,the cluster’s lifetime is a vital parameter for network ***,to optimize the CH’s selection process,three evolutionary algorithms are employed,namely,the ant colony optimization(ACO),multi-objective particle swarm optimization(MOPSO),and the comprehensive learning particle swarm optimization(CLPSO).The performance of the proposed method is verified by extensive experiments by varying values of different parameters,including the transmission range,node number,node mobility,and grid size.A comprehensive comparative analysis of the three algorithms is conducted by extensive *** results show that,compared with the other methods,the proposed ACO-based method can form clusters more efficiently and increase network lifetime,thus achieving remarkable network and energy *** proposed ACO-based technique can also be used in other types of ad-hoc networks as well.

关键词： Wireless body area network clustering internet of things evolutionary algorithm ant colony optimization

Pattern Recognition of Modulation Signal Classification Using Deep Neural Networks

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Computer Systems Science & Engineering 2022年第11期43卷 545-558页

作者： D.Venugopal V.Mohan S.Ramesh S.Janupriya sangsoon lim Seifedine Kadry Department of Electronics and Communication Engineering KPR Institute of Engineering and TechnologyCoimbatore641048India Department of Electronics and Communication Engineering Saranathan College of EngineeringTrichy620012India Department of Computer Science and Engineering Krishnasamy College of Engineering&TechnologyCuddalore607109India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Engineering Sungkyul UniversityAnyangKorea Department of Applied Data Science Noroff University CollegeKristiansandNorway

In recent times,pattern recognition of communication modulation signals has gained significant attention in several application areas such as military,civilian field,*** becomes essential to design a safe and robust feature extraction(FE)approach to efficiently identify the various signal modulation types in a complex *** works have derived new techniques to extract the feature parameters namely instant features,fractal features,and so *** addition,machine learning(ML)and deep learning(DL)approaches can be commonly employed for modulation signal *** this view,this paper designs pattern recognition of communication signal modulation using fractal features with deep neural networks(CSM-FFDNN).The goal of the CSM-FFDNN model is to classify the different types of digitally modulated *** proposed CSM-FFDNN model involves two major processes namely FE and *** proposed model uses Sevcik Fractal Dimension(SFD)technique to extract the fractal features from the digital modulated ***,the extracted features are fed into the DNN model for modulation signal *** improve the classification performance of the DNN model,a barnacles mating optimizer(BMO)is used for the hyperparameter tuning of the DNN model in such a way that the DNN performance can be raised.A wide range of simulations takes place to highlight the enhanced performance of the CSM-FFDNN *** experimental outcomes pointed out the superior recognition rate of the CSM-FFDNN model over the recent state of art methods interms of different evaluation parameters.

关键词： Pattern recognition signal modulation communication signals deep learning feature extraction

Artificial Intelligence-Enabled Cooperative Cluster-Based Data Collection for Unmanned Aerial Vehicles

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Computers, Materials & Continua 2022年第11期73卷 3351-3365页

作者： R.Rajender C.S.S.Anupama G.Jose Moses E.Laxmi Lydia Seifedine Kadry sangsoon lim Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Engineering Sungkyul UniversityAnyang14097Korea

In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile *** encompasses several heterogeneous resource and communication standard in ensuring incessant availability of *** the same time,the development of 6G enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,*** UAV networks,energy efficiency and data collection are considered the major process for high quality network *** these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted *** issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6G *** this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6G *** proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets *** presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOSSO)algorithm for selecting the CHs and construct *** QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of *** performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.

关键词： 6G unmanned aerial vehicles resource allocation energy efficiency artificial intelligence clustering data collection