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Cooperative Spectrum Sensing Deployment for Cognitive Radio Networks for Internet of things 5G Wireless Communication

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Tsinghua Science and Technology 2024年第3期29卷 698-720页

作者： thulasiraman Balachander Kadiyala Ramana Rasineni Madana Mohana Gautam Srivastava th.ppa reddy gadekallu SRM Institute of Science and Technology Tamil Nadu 603203India Lebanese American University Beirut 1102Lebanonand also with Chaitanya Bharathi Institute of TechnologyHyderabad 500075India Chaitanya Bharathi Institute of Technology Hyderabad 500075India Brandon University BrandonR7A 0A1CanadaChinaMedical UniversityTaichung 404327Chinaand Lebanese American UniversityBeirut 1102Lebanon Department of Electrical and Computer Engineering Lebanese American UniversityBeirut 1102Lebanon

Recently,Cooperative Spectrum Sensing(CSS)for Cognitive Radio Networks(CRN)plays a significant role in efficient 5G wireless *** sensing is a significant technology in CRN to identify underutilized *** CSS technique is highly applicable due to its fast and efficient performance.5G wireless communication is widely employed for the continuous development of efficient and accurate Internet of things(IoT)networks.5G wireless communication will potentially lead the way for next generation IoT *** has established significant consideration as a feasible resource to improve identification performance by developing spatial diversity in receiving signal strength in *** this paper,an optimal CSS for CRN is performed using Offset Quadrature Amplitude Modulation Universal Filtered Multi-Carrier Non-Orthogonal Multiple Access(OQAM/UFMC/NOMA)*** of spectrum and bandwidth utilization is a key challenge in CRN for IoT 5G wireless *** optimal solution for CRN in IoT-based 5G communication should be able to provide optimal bandwidth and CSS,low latency,Signal Noise Ratio(SNR)improvement,maximum capacity,offset synchronization,and Peak Average Power Ratio(PAPR)*** Energy Efficient All-Pass Filter(EEAPF)algorithm is used to eliminate *** deployment approach improves Quality of Service(QoS)in terms of system reliability,throughput,and energy *** in-depth experimental results show that the proposed methodology provides an optimal solution when directly compares against current existing methodologies.

关键词： cooperative spectrum sensing cognitive radio network Internet of things offset quadratureamplitude modulation universal filtered multi-carrier non-orthogonal multiple access

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Explainable-AI-based two-stage solution for WSN object localization using zero-touch mobile transceivers

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Science China(Information Sciences) 2024年第7期67卷 27-45页

作者： Kai FANG Junxin CHEN Han ZHU th.ppa reddy gadekallu Xiaoping WU Wei WANG School of Mathematics and Computer Science Zhejiang A&F University School of Software Dalian University of Technology Faculty of Applied Sciences Macao Polytechnic University Department of Electrical and Computer Engineering Lebanese American University School of Information Engineering Huzhou University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University

Artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability, the interference factors in the process of WSN object localization cannot be effectively eliminated. In this paper, an explainable-AI-based two-stage solution is proposed for WSN object localization. In this solution, mobile transceivers are used to enlarge the positioning range and eliminate the blind area for object localization. the motion parameters of transceivers are considered to be unavailable,and the localization problem is highly nonlinear with respect to the unknown parameters. To address this,an explainable AI model is proposed to solve the localization problem. Since the relationship among the variables is difficult to fully include in the first-stage traditional model, we develop a two-stage explainable AI solution for this localization problem. the two-stage solution is actually a comprehensive consideration of the relationship between variables. the solution can continue to use the constraints unused in the firststage during the second-stage, thereby improving the performance of the solution. therefore, the two-stage solution has stronger robustness compared to the closed-form solution. Experimental results show that the performance of both the two-stage solution and the traditional solution will be affected by numerical changes in unknown parameters. However, the two-stage solution performs better than the traditional solution, especially with a small number of mobile transceivers and sensors or in the presence of high noise. Furthermore,we have also verified the feasibility of the proposed explainable-AI-based two-stage solution.

关键词： explainable AI object localization semidefinite relaxation mobile transceiver two-stage solution closed-form solution

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Smart contract token-based privacy-preserving access control system for industrial Internet of things

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Digital Communications and Networks 2023年第2期9卷 337-346页

作者： Weizheng Wang Huakun Huang Zhimeng Yin th.ppa reddy gadekallu Mamoun Alazab Chunhua Su Department of Computer Science City University of Hong KongHong Kong SAR999077China Division of Computer Science University of AizuFukushima965-8580Japan School of Information Technology and Engineering Vellore Institute of TechnologyTamil NaduIndia College of Engineering IT and EnvironmentCharles Darwin UniversityAustralia School of Computer Science and Cyber Engineering Guangzhou UniversityChina

Due to mobile Internet technology's rapid popularization,the Industrial Internet of things(IIoT)can be seen everywhere in our daily *** IIoT brings us much convenience,a series of security and scalability issues related to permission operations rise to the surface during device ***,at present,a reliable and dynamic access control management system for IIoT is in urgent *** till now,numerous access control architectures have been proposed for ***,owing to centralized models and heterogeneous devices,security and scalability requirements still cannot be *** this paper,we offer a smart contract token-based solution for decentralized access control in IIoT ***,there are three smart contracts in our system,including the Token Issue Contract(TIC),User Register Contract(URC),and Manage Contract(MC).these three contracts collaboratively supervise and manage various events in IIoT *** also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units(NTRU)to preserve user privacy during the registration ***,to evaluate our proposed architecture's performance,we build a prototype platform that connects to the local ***,experiment results show that our scheme has achieved secure and dynamic access control for the IIoT system compared with related research.

关键词： Blockchain Privacy preservation Smart contract Industrial IoT

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Deep Neural Networks Based Approach for Battery Life Prediction

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Computers, Materials & Continua 2021年第11期69卷 2599-2615页

作者： Sweta Bhattacharya Praveen Kumar reddy Maddikunta Iyapparaja Meenakshisundaram th.ppa reddy gadekallu Sparsh Sharma Mohammed Alkahtani Mustufa Haider Abidi School of Information Technology and Engineering Vellore Institute of TechnologyVelloreIndia Department of Computer Science and Engineering Baba Ghulam Shah Badshah UniversityRajouriIndia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadhSaudi Arabia Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadhSaudi Arabia

the Internet of things(IoT)and related applications have witnessed enormous growth since its *** diversity of connecting devices and relevant applications have enabled the use of IoT devices in every *** the applicability of these applications are predominant,battery life remains to be a major challenge for IoT devices,wherein unreliability and shortened life would make an IoT application completely *** this work,an optimized deep neural networks based model is used to predict the battery life of the IoT *** present study uses the Chicago Park Beach dataset collected from the publicly available data repository for the experimentation of the proposed *** dataset is pre-processed using the attribute mean technique eliminating the missing values and then One-Hot encoding technique is implemented to convert it to numerical *** processed data is normalized using the Standard Scaler *** Flame Optimization(MFO)Algorithm is then implemented for selecting the optimal features in the *** optimal features are finally fed into the DNN model and the results generated are evaluated against the stateof-the-art models,which justify the superiority of the proposed MFO-DNN model.

关键词： Battery life prediction moth flame optimization one-hot encoding standard scaler

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Classification and Categorization of COVID-19 Outbreak in Pakistan

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Computers, Materials & Continua 2021年第10期69卷 1253-1269页

作者： Amber Ayoub Kainaat Mahboob Abdul Rehman Javed Muhammad Rizwan th.ppa reddy gadekallu Mustufa Haider Abidi Mohammed Alkahtani Department of Computer Science Kinnaird College for WomenLahore54000Pakistan Department of Cyber Security Air UniversityIslamabadPakistan School of Information Technology and Engineering Vellore Institute of TechnologyTamil NaduIndia Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

Coronavirus is a potentially fatal disease that normally occurs in mammals and ***,in humans,the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected *** is a family of viruses that is more lethal than other unpremeditated *** December 2019,a new variant,i.e.,a novel coronavirus(COVID-19)developed in Wuhan province,*** January 23,2020,the number of infected individuals has increased rapidly,affecting the health and economies of many countries,including *** objective of this research is to provide a system to classify and categorize the COVID-19 outbreak in Pakistan based on the data collected every day from different regions of *** research also compares the performance of machine learning classifiers(i.e.,Decision Tree(DT),Naive Bayes(NB),Support Vector Machine,and Logistic Regression)on the COVID-19 dataset collected in *** to the experimental results,DT and NB classifiers outperformed the other *** addition,the classified data is categorized by implementing a Bayesian Regularization Artificial Neural Network(BRANN)*** results demonstrate that the BRANN classifier outperforms state-of-the-art classifiers.

关键词： COVID-19 pandemic neural network BRANN machine learning

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Robust Attack Detection Approach for IIoT Using Ensemble Classifier

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Computers, Materials & Continua 2021年第3期66卷 2457-2470页

作者： V.Priya I.Sumaiya thaseen th.ppa reddy gadekallu Mohamed K.Aboudaif Emad Abouel Nasr School of Information Technology and Engineering Vellore Institute of TechnologyVellore632014India Advanced Manufacturing Institute King Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

Generally,the risks associated with malicious threats are increasing for the Internet of things(IoT)and its related applications due to dependency on the Internet and the minimal resource availability of IoT ***,anomaly-based intrusion detection models for IoT networks are *** detection methodologies need to be developed for the Industrial Internet of things(IIoT)network as threat detection is a significant expectation of *** learning approaches are considered to be evolving techniques that learn with experience,and such approaches have resulted in superior performance in various applications,such as pattern recognition,outlier analysis,and speech *** techniques and tools are not adequate to secure IIoT networks due to the use of various protocols in industrial systems and restricted possibilities of *** this paper,the objective is to develop a two-phase anomaly detection model to enhance the reliability of an IIoT *** the first phase,SVM and Naïve Bayes,are integrated using an ensemble blending technique.K-fold cross-validation is performed while training the data with different training and testing ratios to obtain optimized training and test *** blending uses a random forest technique to predict class *** Artificial Neural Network(ANN)classifier that uses the Adam optimizer to achieve better accuracy is also used for *** the second phase,both the ANN and random forest results are fed to the model’s classification unit,and the highest accuracy value is considered the final *** proposed model is tested on standard IoT attack datasets,such as WUSTL_IIOT-2018,N_BaIoT,and Bot_*** highest accuracy obtained is 99%.A comparative analysis of the proposed model using state-of-the-art ensemble techniques is performed to demonstrate the superiority of the *** results also demonstrate that the proposed model outperforms traditional techniques and thus improv

关键词： Blending ensemble intrusion detection Industrial Internet of things(IIoT)

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