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A review paper on wireless sensor network techniques in internet of things (iot)

Materials today: Proceedings 2022年 51卷 161-165页

作者： Kamal Gulati Raja sarath Kumar Boddu Dhiraj Kapila sunil L. Bangare Neeraj Chandnani G. saravanan ASIBAS Amity University Noida India Department of CSE Lenora College of Engineering Rampachodavaram Andhra Pradesh India Department of Computer Science & Engineering Lovely Professional University Phagwara India Department of I.T. Sinhgad Academy of Engineering Pune India Military College of Telecommunication Engineering Mhow Madhya Pradesh India Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Coimbatore India

in recent times, it has been witnessed that wireless systems based on iot-based have developed rapidly in various sectors. the iot (internet of things) is the network in which physical devices, equipment, sensors and other objects can communicate among themselves without human involvement. the WsN (Wireless sensor Network) is a central component of the iot, which has proliferated into several different applications in real-time. the iot and WsNs now have various critical and non-critical applications impacting nearly every area of our everyday life. WsN nodes are usually small and battery-driven machines. thus, the energy effective data aggregation techniques that increase the lifespan of the network are highly significant. Various approaches and algorithms for energy-efficient data aggregation in iot-WsN systems were presented. this paper reviews the literature with specific attention to aspects of wireless networking for the preservation of energy and aggregation of data.

关键词： internet of things (iot) Wireless sensor Networks (WsN) Energy-efficiency Network lifetime Data aggregation

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Next Generation image Watermarking via Combined DWt-sVD technique 2

Next Generation Image Watermarking via Combined DWT-SVD Tech...

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2nd international Conference on Cyber Resilience, iCCR 2024

作者： Al-Na'amneh, Qais Almomani, Ammar Nasayreh, Ahmad Nahar, Khalid M.O. Gharaibeh, Hasan Al Mamlook, Rabia Emhamed Alauthman, Mohammad Applied Science Private University Department Of Cyber Security Amman Jordan School Of Computing Skyline University College P.O. Box 1797 Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I. T. Department Irbid Jordan Yarmouk University Department Of Computer Sciences Irbid211633 Jordan Yarmouk University Faculty Of Information Technology Computer Sciences Irbid211633 Jordan Trine University Dep. Of Business Administration IN United States University Of Zawia Dep.of Mechanical And Industrial Engineering Tripoli Libya University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan

ISBN: (纸本)9798350394962

Personal computers and the internet are used in different areas and are easier to use. Most data is easy to transmit and duplicate in digital format, and being tampered with and stolen easily leads to issues for content owners and service providers. the Paper describes a secure (tampering-resistant) method for watermarking images for protection and authentication and digital media like audio, video, and multimedia data, and a combination of singular value decomposition (sVD) and Discrete wavelet transform (DWt) for insertion watermark by MAtLAB to increase accuracy, simplicity, and flexibility Readability, secure robustness, and imperceptibility the visibility of the image. the proposed algorithm consists of three main steps: Firstly, we decomposed the original image into four sub-bands and the original watermark. secondly, embed the watermark in the LL sub-band by replacing the singular value for the watermark. Finally, embed a watermark in the other three sub-bands by adding LL values for a watermark in each sub-band in the original image. © 2024 iEEE.

关键词： Discrete wavelet transforms

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Development of Efficient Wireless sensor Network for iot Applications

Development of Efficient Wireless Sensor Network for IoT App...

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Pervasive Computing and social Networking (iCPCsN), international Conference on

作者： s. shankar G. Deepika G. Devi s. Ramesh suraj srivastava s sandeep Kumar Department of Electronics and Communication Systems Sri Krishna Arts and Science College Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering St. Peter's Engineering College Hyderabad Telangana India Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India Depattment of Computational Intelligence Institute of Artificial Intelligence and Machine Learning Saveetha University Chennai Tamil Nadu India Department of Computer science and Engineering I.K. Gujral Punjab Technical University SahibzadaAjit Singh Nagar Punjab India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

this research study provides an overview of the effort that went into developing reliable “Wireless sensor networks” for iot applications, including both theoretical study and practical testing. the report also includes the findings of an analysis of variance and regression analysis, which pinpoint the significance of such aspects as network architecture, traffic load, and ambient conditions on the performance of Wireless sensor Network (WsN). Finally, study provides a discussion on the future research directions, as well as the design and implementation of a Wireless sensor Network (WsN) for internet of things (iot) applications.

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intelligent inhalation therapy for Cystic Fibrosis Using iot and Machine Learning solutions

Intelligent Inhalation Therapy for Cystic Fibrosis Using IoT...

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sustainable Expert systems (iCsEs), international Conference on

作者： Chitra sabapathy Ranganathan Arul Prasath A Nandhini R Kannan shanmugam s. Velmurugan s. srinivasan Mphasis Corporation Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Gaming Technology School of Computing Science and Engineering VIT Bhopal University Bhopal Madhya Pradesh India Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Optimal patients result in the long-term management of cystic fibrosis (CF) and need personalized treatment strategies due to the hereditary disorder's impact on respiratory problems. One potential approach to individualized CF treatment is intelligent inhalation therapy systems that combine machine learning (ML) algorithms with internet of things (iot) sensors. to provide CF patients with a predictive modeling framework that can personalize their inhalation treatment using the Random Forest algorithm. the algorithm is designed to predict individual-level outcomes, including the potential of an exacerbation and the efficacy of therapy, by mining a large and varied dataset that includes patient demographics, lung function measurements, medication adherence records, environmental factors, and reports of symptoms. the Random Forest method was selected due to its capacity to manage diverse data types, detect intricate relationships between variables, and provide insights that can be understood. it shows that the Random Forest model effectively predicts important outcomes for CF care by training, validating, and evaluating it using real-world data from CF patients. Exacerbation risks and patient-specific therapy responses may be better understood by feature significance analysis, allowing for more targeted intervention approaches. the proposed predictive modeling framework will improve the efficiency and effectiveness of inhalation treatment for managing CF. Healthcare practitioners may enhance the management of CF via ML analytics and data collected through the iot. this allows for delivering personalized treatment regimens, optimizing resource allocation, and better patient outcomes. Challenges include ensuring data accuracy, managing extensive datasets, sustaining real-time monitoring, optimizing algorithms, and ensuring system stability.

关键词： Machine learning algorithms Accuracy Lungs Medical treatment Predictive models Prediction algorithms Real-time systems Data models internet of things Random forests

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A lightweight Federated Learning-based intrusion detection model for internet of things

A lightweight Federated Learning-based Intrusion detection m...

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iEEE international Conference on Multi topic

作者： Noshina tariq Mamoona Humayun Ghadah Naif Alwakid Bushra Almas NZ Jhanjhi Muhammad Zakwan Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad Pakistan School of Arts Humanities and Social Sciences University of Roehampton London United Kingdom Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Institute of Information Technology Quaid-i-Azam University Islamabad Pakistan School of Computer Science Taylor’s University Selangor Malaysia Department of Avionics Engineering Air University Islamabad Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

the advancement of sophistication in botnet intrusions on internet of things (iot) systems and the resources required by traditional iDs make strong security solutions necessary, which reduce resource usage while enhancing detection capability. therefore, this paper tests the performance and performance comparison of classification models based on lightweight Federated Learning (FL) for iot botnet intrusion detection using Random Forest (RF), XGBoost, and LightGBM classifiers. Utilizing a large-scale dataset from iot traffic, the three models were tested in metrics such as confusion matrices, ROC curves, and Precision-Recall curves. the results show that the RF and XGBoost classifiers achieve high AUCs of 1.00, thereby, there is no trade-off between sensitivity and specificity. On the other hand, LightGBM scored a low of 0.58 AUC. the results show that robust ensemble methods can be applied to handle complex and imbalanced datasets commonly presented in iot traffic in FL environments.

关键词： Radio frequency Measurement Federated learning Botnet intrusion detection sensitivity and specificity internet of things Ensemble learning security Random forests

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Detection of Pneumonia in Chest X-Ray with Bat-Algorithm selected Deep and Handcrafted Features 8

Detection of Pneumonia in Chest X-Ray with Bat-Algorithm Sel...

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8th international Virtual Conference on Biosignals, images, and instrumentation, iCBsii 2022

作者： suresh Manic, K. Kadry, seifedine Biju, Roshima Balaji, Gangadharam Department of Electrical and Communication Engineering National University of Science and Technology Oman Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway System Engineer M/S TATA Consultancy Services Limited TCSL Siruseri SEZ Unit SIPCOT I.T. Park TN Chennai603103 India

Pneumonia is one infectious disease caused by viruses/bacteria, and early screening is necessary for the detection and treatment. Furthermore, pneumonia causes severe problems in children and elderly. the proposed work aims to develop a disease screening scheme for efficiently classifying the chest radiograph (X-ray) pictures into the Normal/Pneumonia group. the proposed process has the below phases;(i) image collecting and resizing, (ii) Deep-feature extraction, (iii) Handcrafted feature extraction, (iv) Bat-Algorithm based feature selection and (v) Classification. in this work, the VGG16 scheme is considered to extract the deep-features and the necessary handcrafted features are mined using the Weighted Local Binary Pattern (WLBP). the necessary feature is then selected using the bat-algorithm supported feature selection. the experimental result of this study proves the accuracy of KNN is healthier (>98%) than other methods. © Published under licence by iOP Publishing Ltd.

关键词： Diseases

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Extraction and assessment of COViD19 infection in lung Ct images using VGG-UNet 8

Extraction and assessment of COVID19 infection in lung CT im...

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8th international Virtual Conference on Biosignals, images, and instrumentation, iCBsii 2022

作者： tanavade, satish suresh Kadry, seifedine suresh Manic, K. Balaji, Gangadharam Department of Electrical and Communication Engineering National University of Science and Technology Oman Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway System Engineer M/S TATA Consultancy Services Limited TCSL Siruseri SEZ Unit SIPCOT I.T. Park TN Chennai603103 India

the infectious disease in humans is gradually rising for various reasons, and COViD19 is one of the recently discovered diseases caused by sARs-CoV-2. From early 2020, the infection due to COViD19 has gradually increased, and still, its infection exists. COViD19 will cause severe infection in the respiratory tract, and early detection and treatment are essential. the harshness of the infection needs to be examined before implementing the treatment. this research aims to build up and implement a suitable procedure to extract and assess the infected section in lung Ct slices. this work extracts the infected section using the pre-trained VGG-UNet scheme. the separated section is validated against the ground-truth (Gt) image, and the necessary presentation standards are calculated. the performance of the VGG-UNet is then compared and verified with the UNet and UNet+ schemes. the investigational product of this study authenticate that the effect reached with the proposed study confirms that the VGG-UNet provides better Jaccard, Dice and accuracy compared to UNet and UNet+. © Published under licence by iOP Publishing Ltd.

关键词： Coronavirus

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Automated Facial Emotion Detection using Arithmetic Optimization Algorithm with Deep Convolutional Neural Network for Autonomous Vehicle Drivers

Automated Facial Emotion Detection using Arithmetic Optimiza...

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Automation, Computing and Renewable systems (iCACRs), international Conference on

作者： R. saranya M. Vijayaragavan s. ismail Kalilulah K.N.V. satyanarayana M. suresh s srimathi CSE Department R.M.K. College of Engineering and Technology Department of EEE Mailam Engineering college MailamTindivanam T.k Villupuram Dist Computer Science and Engineering Dr. MGR Educational and Research Institute Periyar E.V.R. High Road Maduravoyal Chennai Dept of ECE S.R.K.R.ENGINEERING COLLEGE China amiram Bhimavaram A.P. Department of ECE Rajarajeswari College of Engineering Bangalore Department of Bio Technology Saveetha School of Engineering SIMATS Chennai Tamilnadu India

Facial Emotion Recognition (FER) utilizing Deep Learning (DL) is indeed employed in the context of autonomous vehicle drivers. By investigating the facial expressions of drivers, the vehicle system is gain insights into the emotional state of drivers. FER is given to whole safety by identifying critical emotions like fear, anger, or surprise. When the system identifies these emotions, it responds accordingly by altering the driving performance or taking preventive measures for avoiding potential risks or aggressive maneuvers. the training of the DL algorithms namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are done on the preprocessed data. these approaches learn to extract appropriate features in facial images and forecast the same emotions. this manuscript offers the design of Automated Facial Emotion Detection using Arithmetic Optimization Algorithm with Deep Convolutional Neural Network (AFED-AOADCNN) technique for Autonomous Vehicle Drivers. the purpose of the AFEDAOADCNN technique is to detect various kinds of facial emotions in autonomous vehicle drivers. in this introduced AFED-AOADCNN technique, DCNN method is applied for procedure of feature extraction. Next, the AOA is employed for optimum hyperparameter tuning of the DCNN method. At last, quantized neural network (QNN) approach is exploited for the identification and classification of different kinds of facial emotions. the investigational evaluation of the AFEDAOADCNN method is assessed on facial emotion dataset. the experimental validation stated the improved outputs of the AFED-AOADCNN method over recent approaches.

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the Canadian RADiation impacts on Climate and Atmospheric Loss satellite (RADiCALs) Mission: Exploring the Coupling of space Weather and terrestrial Climate

The Canadian RADiation Impacts on Climate and Atmospheric Lo...

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2024 small satellites systems and services symposium, 4s 2024

作者： Mann, ian R. Cully, C.M. Fedosejevs, R. Milling, D.K. Enno, G.A. Lipsett, M. Zee, R.E. Larouche, B.P. Rankin, R. Connors, M.G. McWilliams, K.A. Ward, W.E. Fiori, R.A. Olifer, L. Ozeke, L. Marshall, R.A. Cullen, D. Barona, D. Howarth, A.D. Yau, A.W. University of Alberta Department of Physics 11335 Saskatchewan Drive NW EdmontonABT6G 2H5 Canada University of Calgary Department of Physics & Astronomy 2500 University Drive NW CalgaryABT2N 1N4 Canada University of Alberta Department of Electrical and Computer Engineering 9211 116 St NW EdmontonABT6G 1H9 Canada University of Alberta Department of Mechanical Engineering 9211 116 St NW EdmontonABT6G 1H9 Canada Space Flight Laboratory University of Toronto Institute for Aerospace Studies 4925 Dufferin Street TorontoONM3H-5T6 Canada Athabasca University 1 University Drive AthabascaABT9S 3A3 Canada University of Saskatchewan Department of Physics & Engineering Physics 9 Campus Drive SaskatoonSKS7N 5A5 Canada University of New Brunswick Department of Physics 8 Bailey Ave FrederictonNBE3A 1H4 Canada Canadian Space Weather Forecast Centre Natural Resources Canada 2617 Anderson Rd OttawaONK1A 0E7 Canada University of Colorado at Boulder College of Engineering and Applied Science 1111 Engineering Drive 422 UCB BoulderCO80309-0422 United States

ISBN: (纸本)9781510688940

the RADiation impacts on Climate and Atmospheric Loss satellite (RADiCALs) is a low-Earth orbiting Canadian small satellite mission investigating the transport of space radiation into the atmosphere, and its impact on Earth’s climate. scheduled for launch in late 2026, the mission will launch into a polar orbit with an integrated payload comprising two back-to-back look direction High Energy Particle (RADHEPt) telescopes, an X-Ray imager (XRi) to remote sense energetic particle precipitation using back-scattered Bremsstrahlung X-rays, and a boom mounted FluxGate Magnetometer (FGM) and search Coil Magnetometer (sCM). Using an innovative thomson spin-stabilized configuration, the satellite will sample the pitch angle distributions in the spin-plane twice per spin. the back-to-back RADHEPt look directions allow for a contemporaneous view of the down-going and back-scattered up-going electrons, at the same time the XRi remote-senses the related Bremsstrahlung, and the magnetometers provide in-situ magnetic signatures of a range of plasma waves. the key measurement of the pitch angle resolved energetic electron precipitation (EEP) and related back-scatter, including a resolved loss cone, will allow a detailed assessment of the energetic particle energy input to the atmosphere. Measurements of EEP, in addition to measurements of solar energetic particle (sEP) precipitation, will represent a critical data set for assessing the role of space radiation in the climate system, for example through the catalytic destruction of ozone in the middle atmosphere by NOx and HOx. Accurately quantifying the impacts of this space radiation on climate requires accurate and loss cone-resolved characterization of the flux of these precipitating energetic particles for inclusion into whole atmosphere models. the RADiCALs explorer will also enable research into potentially catastrophic space-weather radiation effects on satellite infrastructure, and assess impacts on space weather-related int

关键词： Magnetometers

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Modified Quick Fuzzy Hypersphere Neural Network for Pattern Classification Using supervised Clustering 2nd

Modified Quick Fuzzy Hypersphere Neural Network for Pattern ...

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2nd international Conference on soft Computing and signal Processing, iCsCsP 2019

作者： Mane, D.t. Kshirsagar, J.P. Kulkarni, U.V. Department of Computer Science & Engineering S. G. G. S. I. E. & T. NandedMaharashtra431606 India Department of Computer Engineering Rajarshi Shahu College of Engineering PuneMaharashtra411033 India

ISBN: (纸本)9789811524745

Defining methods which map the features present in the input to a binomial class output is the core objective of pattern classification. the Modified Quick Fuzzy Hypersphere Neural Network (MQFHsNN) algorithm proposed is an extension of the existing Class-specific Fuzzy Hypersphere Neural Network (CsFHsNN). its membership function has been modified such that it outperforms the existing algorithms in the field of pattern classification. Fuzzy set hyperspheres are employed by MQFHsNN for pattern clustering and classes are represented by a combination of fuzzy set hypersphere. By using class labels for forming the clusters, the presented model gives 100% accuracy on the training data. the core contributors to facilitate learning in MQFHsNN are: creation of hyperspheres based on measurements provided by interclass and intraclass distance techniques as well as decision of patterns of individual classes by the fuzzy membership function. the proposed model is put into test on four standard datasets and the results obtained are more superior to the existing models. this leads us to the conclusion that MQFHsNN is being used for improvising model accuracy in pattern classification. © 2020, springer Nature singapore Pte Ltd.

关键词： Fuzzy sets

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