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Secure Smart Healthcare Surveillance Framework Using Fuzzy C-Means Clustering with Effective Ant Colony Optimization in Internet of Things

Secure Smart Healthcare Surveillance Framework Using Fuzzy C...

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2022 IEEE International Conference on Knowledge engineering and Communication Systems, ICKES 2022

作者： Karuppaiah, Jayakumar Aarthi, C. Girirajan Jananai, M. Parameshachari, D.B. Biomedical Engineering Kalaigar Karunanidhi Institute of Technology Coimbatore India Sengunthar Engineering College Thiruchengode India Sr University Department of ECE Warangal India Kpr Instiute of Engineering and Technology Computer Science and Engineering Coimbatore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9781665456371

The emergence of IoT device networks is not limited to a specific industry, application, or environment;instead, they are assisting in the development of the country in a number of sectors, including industries, cities, agriculture, and defense. But IoT device networks have some restrictions, including those related to bandwidth, energy resources, and other resources, as well as restrictions based on the environment. The fast growth of the Internet of Things (IoT) has brought to light significant privacy protection problems. This has a tremendous impact on the large-scale IoT applications. In this study, a ground-breaking IOT computing platform for secure and knowledgeable healthcare monitoring services is introduced. Within an IoT architecture, fully homomorphism encryption (FHE), which protects data privacy, is handled and kept. For the proposed IoT framework, a distributed method for clustering-based approaches is created with the scalability to gather and independently analyse massive amounts of hetrogeneous data in distributed IoT devices before it is transferred to the cloud. In this study, the Cluster Heads (CHs) were chosen from the available IoT devices using a brand-new Efficient Ant Colony Optimization (EACO) technique. For healthcare surveillance, fuzzy C-Means clustering (FCMC) is employed. FCM allows us to recognise regular (typical) bio signal patterns. © 2022 IEEE.

关键词： Internet of things

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Multi-Objective Modified Glowworm Swarm Optimization Algorithm for Efficient Spectrum Sensing in the Cognitive Radio Network

Multi-Objective Modified Glowworm Swarm Optimization Algorit...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Babiyola, A. Kumar, Gotte Ranjith Shridhar, V. Hussein, Abbas Hameed Abdul Alzubaidi, Laith H. Meenakshi Sundararajan Engineering College Dept of Ece Chennai India SR University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Ahl Al Bayt University College of Pharmacy Karbala Iraq The Islamic University Najaf Iraq

ISBN: (纸本)9798350315455

The combination of machine learning and Cognitive Radio (CR) technology has generated a lot of interest in research, especially in the field of spectrum management. Even with the possible advantages, high spectral efficiency, low latency, high network efficiency, and enhanced energy economy are only a few of the performance qualities that need to be addressed in order to prepare technology for spectrum management. But accomplishing these goals might frequently provide opposite difficulties. In the context of spectrum management, a new Multi-Objective Modified Glowworm Swarm Optimization (MOMGSO) approach is developed to handle multi-objective optimization issues. In order to achieve global optima, the Glowworm Swarm Optimization (GSO) modification seeks to achieve a balance between the exploration and exploitation processes. This tweak modifies the mutation coefficient, leader selection, and discrimination weight. An analysis is conducted between the suggested technique and current methods in terms of energy efficiency, interference, and throughput. The results of the simulation show that the MOMGWO algorithm outperforms existing techniques in terms of the Pareto front quality. © 2023 IEEE.

关键词： Cognitive Radio Multi-Objective Modified Glowworm Swarm Optimization Multi-Objective Optimization Spectrum Management Spectrum Sensing

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Efficient Model Training in Decentralized Systems with Federated Learning 2

Efficient Model Training in Decentralized Systems with Feder...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Rao, M.S. Srinivasa Saraswathy, S. Neela, V. Arun, M.R. Gupta, Sandeep Taqui, Syed Noeman Vignana Jyothi Institute of Engineering & Technology Vnr Department of Mechanical Engineering Telangana Hyderabad India Sri Sairam Engineering College Department of Computer Science and Business Systems Chennai India School of Computing and Information Technology Reva University Karnataka Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Department of Electrical Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

In the fast-changing field of machine learning, data privacy and model training efficiency are crucial. Federated Learning (FL) is a breakthrough method for training models on several decentralized devices or servers without centralizing raw data. This study examines Federated Learning's challenges in optimizing model training in dispersed systems. The first stage is to examine the Federated Learning (FL) framework and its merits, particularly in protecting data privacy and reducing server dependence. The next section will discuss communication overhead, stragglers, and non-IID (independent and identically distributed) data issues in a decentralized training model. We developed novel optimization methodologies and adaptive algorithms to improve Federated Learning (FL) efficiency. This includes updating asynchronous models, adaptability-based communication frequency, and non-IID data management. We also present a resilient aggregation method that mitigates Byzantine failures in participating nodes. We demonstrate that our methods improve convergence rate, communication costs, and model performance trained with Federated Learning (FL) by conducting a series of comprehensive experiments and analyzing real-world case studies. We show that these enhancements do not compromise data privacy, preserving Federated Learning (FL) principles. In conclusion, this study advances Federated Learning in decentralized systems. In an age of data sensitivity, this approach could help implement efficient, scalable, and privacy-preserving machine-learning methods. © 2023 IEEE.

关键词： Byzantine Failures Communication Efficiency Data Privacy Decentralized Systems Federated Learning Model Training Non-IID Data

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Improving the Performance of Network-on-Chip by Utilizing Fault Zone Tiling to Reuse Trace Buffers

Improving the Performance of Network-on-Chip by Utilizing Fa...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： R. Valli Suseela Neethu Anna Sabu S.Kevin Andrews S. Revathy L. Revathy K.Lakshmi Narayanan Department of ECE Francis Xavier Engineering College Tirunelveli Tamil Nadu India Department of Electronics and Telecommunication K J Somaiya College of Engineering Mumbai Department of Computer Applications Dr. MGR Educational Educational and Research institute Chennai-95 ECE Department Francis Xavier Engineering College Tirunelveli Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Network-on-Chip (NoC) is employed in multi-core systems to enhance the data transfer capacity and adaptability. The Network-on-Chips (NoCs) experience dependability issues due to software faults. Tiling is a procedure used to enhance the accuracy of data in NoCs (Networks-on-Chip). The fault-masking technique prevents the need for rechecking by the block. At this location, the presence of X tracer and the process of tiling are ongoing. The proposed work is implemented by simulation using Xilinx 12.1.

关键词： Green communications Termination of employment Network-on-chip Debugging Data transfer Software Silicon Energy efficiency Table lookup Testing

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Deep Learning Approaches for Intrusion Detection with Emerging Cybersecurity Challenges

Deep Learning Approaches for Intrusion Detection with Emergi...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Hemalatha, S. Mahalakshmi, M. Vignesh, V. Geethalakshmi, M. Balasubramanian, D. Jose, Anand A. St. Joseph's Institute of Technology Department of Electrical and Electronics Engineering Chennai600119 India Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Chennai603203 India Karpagam College of Engineering Department of Computer Science and Engineering Coimbatore641032 India Kcg College of Technology Department of Mathematics Chennai600097 India Jaya Engineering College Department of Mca Chennai602024 India Kcg College of Technology Department of Ece Tamil Nadu Chennai600097 India

ISBN: (纸本)9798350313987

Intrusion Detection Systems (IDS) play a pivotal role in preventing computer networks from an ever-evolving landscape of cybersecurity threats. As cyberattacks become increasingly sophisticated and diversified, the need for robust and adaptable intrusion detection models has never been more critical. This study explores the application of Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) in the context of intrusion detection, particularly in addressing emerging cybersecurity challenges. The performance of deep learning (DL)-based IDSs is highly dependent on the dataset used and no benchmark models for intrusion detection have been found. In this work, a recent dataset CIC-DDoS-2019 is used for the detection of DDOS (distributed deny of services). This work underscores the importance of adopting DL techniques, including DNNs, CNNs, and RNNs, for intrusion detection in the face of emerging cybersecurity challenges. It highlights the potential of these neural network architectures to enhance network security, adapt to evolving threats, and mitigate the risks posed by modern cyber adversaries. Moreover, it encourages further research and development in this field to stay ahead of the ever-changing cybersecurity landscape. © 2023 IEEE.

关键词： DDoS attacks Deep learning Distributed denial-of-service (DDoS) Intrusion detection system Machine Learning

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Enhance Path Selection with Multi-Hop Data Dissemination to Achieve High Quality-of-Service in Vehicular Communication

Enhance Path Selection with Multi-Hop Data Dissemination to ...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Y Shasikala V. Chinnammal Maria Rubiston. M Imad Shalout Prashant D. Kamble M. Dinesh Department of Computer Applications Aditya University Surampalem Andhra Pradesh Department of ECE Rajalakshmi Institute of Technology Chennai Department of ECE S.A. Engineering College Chennai Department of Civil Engineering faculty of engineering Zarqa university Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mech Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

VANETs through the proposed EPMHQ approach. It discusses the challenges of VANETs, and related works, and introduces the EPMHQ approach integrating DSRC, IEEE 802.11p, AODV routing, and Whale Optimization Algorithm (WOA) based path selection. The methodology and objectives of the approach are outlined, emphasizing its aim to enhance network performance and efficiency. Results from experiments utilizing NS-2 simulation are shown, indicating better performance in packet delivery ratio, network throughput, average delay, energy efficiency, and routing overhead when compared to current techniques. The abstract concludes by highlighting the potential of the EPMHQ approach in improving VANETs for intelligent transportation systems.

关键词： Data dissemination Spread spectrum communication Quality of service Routing Throughput Whale optimization algorithms Energy efficiency Delays Intelligent transportation systems Global Positioning System

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Cluster, Cloud, Grid Computing via Network Communication Using Control Communication and Monitoring of Smart Grid 2

Cluster, Cloud, Grid Computing via Network Communication Usi...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Kumar, Santosh Kumar, N M G Geetha, B.T. Sangeetha, M. Chakravarthi, M. Kalyan Tripathi, Vikas Chandigarh University Department of CSE India Sree Vidyanikethan Engineering College Department of Electrical and Electronics Engineering Tirupathi India Saveetha School of Engineering Simats Department of ECE Tamil Nadu India School of Computing and Information Technology Reva University Bengaluru India School of Electronics Engineering VIT-AP University Amaravathi India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

Traditional power consumption management systems are not showing enough reliability and thus, smart grid technology has been introduced to reduce the excess power wastages. In the context of smart grid systems, network communication is another term that is used for developing the network between the users and the load profiles. Cloud computing and clustering are also executed for efficient power management. Based on the facts, this research is going to identify wireless network communication systems to monitor and control smart grid power consumption. Primary survey-based research has been carried out with 62 individuals who worked in the smart grid system, tracked, monitored and controlled the power consumptions using WSN technology. The survey was conducted online where the respondents provided their opinions via a google survey form. The responses were collected and analyzed on Microsoft Excel. Results show that hybrid commuting of cloud and edge computing technology is more advantageous than individual computing. Respondents agreed that deep learning techniques will be more beneficial to analyze load profiles than machine learning techniques. Lastly, the study has explained the advantages and challenges of using smart grid network communication systems. Apart from the findings from primary research, secondary journal articles were also observed to emphasise the research findings. © 2022 IEEE.

关键词： Wireless sensor networks

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A Novel Implementation of an IoT Powered Open Borewell Child Rescue System using ESP8266 and Logical Sensors Association

A Novel Implementation of an IoT Powered Open Borewell Child...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： R.T Charulatha S Priyadharshini V Sujatha R. Sabitha M. Dinesh Department of ECE Rajalakshmi Institute of Technology Chennai Department of Electronics and Instrumentation Engineering R.M.K. Engineering College Chennai Department of Master of Computer Applications S.A. Engineering College Chennai Department of CSE Rajalakshmi Engineering College Thandalam Chennai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Natural resources such as water, air, land, etc., support the livelihoods of the majority of the population of India, a developing nation. The concept is based on the widespread practice of rescuing children from unprotected bore wells. Young children are at risk of drowning in open bore wells because they are not properly protected. Tragically, many children are dying while only a handful is being saved. The bore well kills countless innocent children despite its intended use of water. Once upon a time, there was a mechanism in place that would only notify the appropriate adults once a youngster had fallen down a bore well. An IoT-based Borewell Child Rescuer (IoTBCR) is the name of the suggested method that makes use of IoT technology. The new approach is tested for efficiency by comparing it to the standard model known as ZigBee based Borewell Child Rescuer (ZBCR). The prototype is a scaleddown version of the whole mechanical and electrical setup described before. A power supply unit, an LCD screen, an integrated microcontroller with a WiFi module called NodeMCU, a motor driver IC, and a number of buttons and switches make up the prototype’ s control module. Several reports have surfaced recently regarding abandoned borewells that have now turned into death wells. Tragically, several youngsters are being sucked down these bore wells to their deaths. To save lives is the true intention of bore-wells, but unfortunately, many innocent people have been killed by these wells. These rescue procedures typically take a long time, are difficult, and include a lot of steps. An easy and practical way to get the kid out of the bore-well is described in this publication. All of the system’ s controls and coordination are housed in one central electronic unit. Through analysis of the simulation, the system aims to lessen the danger associated with the kid rescue operation.

关键词： Gases Microcontrollers Zigbee Robot sensing systems Control systems Transceivers Sensors

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Revolutionizing Military Operations: A Smart Fabrication of Bomb Detection and Diffusing Robot with Location Sharing Feature

Revolutionizing Military Operations: A Smart Fabrication of ...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： N. V. Krishnamoorthy V. V. Sujatha G. Naresh R. Ratheesh M. Dinesh Department of Mechanical Engineering Sri Krishna College of Engineering and Technology Kuniyamuthur Coimbatore Department of Master of Computer Applications S.A.Engineering College Chennai Department of ECE Mohan Babu University (Erstwhile SreeVidyanikethan Engineering College) Tirupati Andhra Pradesh Department of ECE Agni College of Technology Chennai Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Every day, newspapers all across the globe publish a deluge of news articles about wounded trained workers or military personnel who die while defusing explosives. The disposal and dispersing of bombs has evolved into a very important and sometimes dangerous task for modern humans. To address this issue, a wireless bomb disposal robot concept has been put forward. The robot is programmed to detonate the bomb using a wireless control module. The transmitter is part of the control module, whereas the receiver is the robot itself. Connected to a NodeMCU boards is the receiver module. The commands sent by the transmitter determine how the robot moves. A wireless video receiver receives the signal from the wireless camera and is interfaced with the LCD display in the control module. Ideally, the robot’s wireless camera would be mounted close to the arm that contains the center shaft motors used for diffusion, allowing the operator to see the recorded footage on the LCD screen located in the control module. The robotic arm can now move thanks to DC motors. We are presenting the safest method for disposing of explosives so that ordinary people may avoid harm, without endangering the lives of bomb squad experts, soldiers, or commandos. The Location Sensitive Bomb Detection Robot (LSBDR) is a suggested technique that uses cross-validation with the traditional Bomb Identification Robot (BIR) to assess how well it identifies and reports bomb locations.

关键词： Wireless communication Wireless sensor networks Transmitters Trajectory planning Weapons Robot vision systems Receivers

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IoT-based ELF Magnetic Flux Density Meter

IoT-based ELF Magnetic Flux Density Meter

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International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO)

作者： Manolis G. Tampouratzis George A. Adamidis Demosthenes Vouyioukas Traianos Yioultsis Dimitrios Stratakis Department of Information and Communication Systems Engineering University of the Aegean Karlovasi Greece Department of Electronics Engineering Hellenic Mediterranean University (HMU) Chania Greece Department of Electrical and Computer Engineering (ECE) Aristotle University of Thessaloniki (AUTH) Thessaloniki Greece Department of Electrical and Computer Engineering (ECE) Hellenic Mediterranean University (HMU) Heraklion Greece

This study presents an IoT-based ELF magnetic field meter that can measure magnetic flux density from 100nT. The proposed device has a flat response across the frequency range from 40Hz to 10 KHz, detecting and measuring most magnetic field sources. The proposed meter can measure fields from the power supply network (usually 50 or 60Hz) and harmonic frequencies extending several times above the base frequency in the operating frequency band. The proposed circuit of the magnetic flux density (magnetic field) meter is quite simple for implementation and the measured field can be displayed on any mobile device with Wi-Fi connectivity. A WeMos D1 Arduino board with an embedded ESP-8266 module is responsible for data transferring from the sensor to the cloud as a complete IoT solution, supported by the Blynk application via Android and iOS operating systems or web interface.

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