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A smart and Secured Office System Using iot

A Smart and Secured Office System Using IoT

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2024 international conference on Advances in computing, Communication, Electrical, and smart Systems, iCACCESS 2024

作者： Jion, Md. Sabiul Azad Ahmad, Mohiuddin Khulna9203 Bangladesh Department of Electrical and Electronic Engineering Khulna9203 Bangladesh

ISBN: (纸本)9798350350289

The implementation of smart offices through the Internet of Things along with the application of face detection systems is shaping the future of official spaces, elevating our quality of life to new heights. For individual offices secure and beneficial technologies are important in smart office architecture. A smart and employee-friendly office model is presented in this research work with supporting technologies and associated challenges, and a few value-added smart office services are anticipated. The Internet of Things (iot) is applied to create an environment of smart offices, office apps, and face-detection entry systems which are referred to as a definitive class of workplace technology & security. iot enables global machine and device connectivity, generating vast data for valuable knowledge across various service domains, and enhancing efficiency and efficiency. This research work proposes smart offices using iot, focusing on two main components: automatic appliances like temperature, lights, and door sensors, and a face detection system for office entrance doors. The app is only accessible to registered employees, and the entrance door opens automatically after 10 seconds of verification. The prototype testing demonstrates the efficiency of the proposed system in making the office smart and secure. © 2024 IEEE.

关键词： Internet of things

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iot based Vehicle Monitoring System for Safety Standards in smart Cities 4

IoT based Vehicle Monitoring System for Safety Standards in ...

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4th international conference on Pervasive computing and Social Networking (ICPCSN)

作者： Piruthiviraj, P. Tarun, S. R. Tarun, M. K. Tarun, M. New Horizon Coll Engn Bengaluru Dept Elect & Commun Bengaluru India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356;9798350386349

The rapid urbanization and increasing number of vehicles in smart cities have increased the importance of ensuring road safety. This model proposes the development and implementation of an Internet of Things (iot)-based vehicle monitoring system to increase the safety standards in smart city environments. Leveraging cutting-edge technology, the system continuously monitors and evaluates on-road vehicle behavior in real-time, providing essential data for traffic management and accident prevention. By integrating a variety of in-car sensors and communication devices, the proposed system collects and transmits data to a centralized server, including information on the vehicle's position, speed, acceleration, and braking patterns. This data enables the detection of potentially hazardous situations, such as aberrant driving patterns and traffic congestion. The iot-based Vehicle Monitoring System not only monitors individual vehicle behavior but also facilitates communication between vehicles and infrastructure, fostering a cooperative traffic ecosystem. With the incorporation of advanced analytics algorithms, the proposed system can identify factors affecting road safety and enhance its prediction capabilities over time. By harnessing technologies like Artificial Intelligence (AI) and machine learning (ML), the proposed system continuously evolves to address evolving safety challenges. The ultimate goal of this study is to contribute to the development of smarter and safer cities by proactively addressing safety and traffic management concerns, thereby reducing traffic congestion, accidents, and creating a more secure urban transportation network.

关键词： Internet of Things Traffic Monitor Safety Node Microcontroller unit

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Non Plant Specific smart Greenhouse with Convective Drying Unit 2

Non Plant Specific Smart Greenhouse with Convective Drying U...

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2nd IEEE international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Mukherjee, Jhilam Mukherjee, Dibyasree Upadhyay, Sneha Basu, Ariyan Shaw, Lipakshi Singh, Debiya Manna, Sumana Adamas University Department of Computer Science Engineering Barasat India Adamas University Department of Computer Science and Engineering Barasat India

ISBN: (纸本)9798350384277

This paper presents a thorough analysis of the design and execution of a smart greenhouse system that makes use of machine learning and iot technology. By automating critical procedures, improving monitoring capabilities, and providing predictive analytics for optimal plant growth and disease detection, the system seeks to completely transform conventional greenhouse agriculture. The greenhouse atmosphere is dynamically maintained by the integration of iot-enabled actuators controlled by a WiFi module (ESP32). Fans, servo motors, mist sprays, lights, and pumps are automated to maintain ideal conditions. Real-time access to environmental factors is facilitated by cloud-based data storage and remote monitoring. Furthermore, plant growth and disease detection can be predicted using machine learning techniques on Raspberry Pis. A CNN model has been customised to identify specific plant diseases. Additionally, energy efficiency and environmental control are improved by the use of a vortex tube in a new drying and cooling system. Accurate soil moisture measurements are possible using a moisture metre based on microclimate conditions, which improves irrigation management. By addressing the issues with traditional greenhouse agriculture, this comprehensive strategy uses cutting-edge technologies to open the door for productive and sustainable crop production. © 2024 IEEE.

关键词： Moisture meters

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3rd international conference on Advancements in smart computing and Information Security, ASCIS 2024

3rd International Conference on Advancements in Smart Comput...

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3rd international conference on Advancements in smart computing and Information Security, ASCIS 2024

ISBN: (纸本)9783031862922

The proceedings contain 28 papers. The special focus in this conference is on Advancements in smart computing and Information Security. The topics include: Prediction of Anti-Cholinesterase Inhibitor for Alzheimer Disease Using machine learning Techniques;scrutinize Search Engine Optimization Strategies with Artificial Intelligence to Rank a Website;a Study on machine learning Algorithms: Forecasting Crop Prices;improving Student Stress Analysis: Novel Methods for Collecting, Preprocessing, and Fusing Features;extraction of Normal and Abnormal Region in Colposcopy Image to Support Cervical Cancer Clinical Decision;optimizing Pricing Strategies: A Comprehensive Framework Using Bayesian Inference and Game Theory;hybrid Ensemble Gradient Boosting Algorithm to Predict Diabetes Health Care Analytics;a Review on machine learning Algorithms for Real-Time Traffic Management;hyperparameter-Tuned Intention Mining for Mental Health Diagnosis Using Logistic Regression;machine learning for Accessible and Precise Assessment in smart Monitoring Systems;an Efficient Model for Academic Performance Prediction of the University Students;comprehensive Comparative Study on Data Mining and machine learning Approaches for Fraud Detection in Financial Services;prediction of Stock Price and Detection of Stock Market Trends Using Adaptive learning Techniques;asthma Prediction Using Fuzzification and machine learning Based Ensemble Classifier;detection and Analysis of Features of Optic Nerve Head Using Retinal Fundus Images of an Eye for a Priori Prediction of Glaucoma;a Hybrid Deep learning Framework for Uncertain Supply Chains: An Optimization Approach;a Comprehensive Review of machine learning Techniques in Recommender System for E-Commerce Platform;Ethics of AI in the Educational Sector - Navigating the Moral Landscape;international Law and AI Interface.

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iot-Enabled smart E-Healthcare System with Predictive Prescription Algorithm for Automatic Patient Monitoring and Treatment 4

IoT-Enabled Smart E-Healthcare System with Predictive Prescr...

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4th international conference on Pervasive computing and Social Networking (ICPCSN)

作者： Ahmad, Shahnawaz Lakshmi, T. R. Vijaya Arunkumar, K. Ravivarman, G. Senthilkumar, G. Natrayan, L. Bennett Univ Sch Comp Sci Engn & Technol Greater Noida Uttar Pradesh India Mahatma Gandhi Inst Technol Dept Elect & Commun Engn Hyderabad Telangana India MVJ Coll Engn Dept Mech Engn Bengaluru Karnataka India Karpagam Acad Higher Educ Dept Elect & Elect Engn Coimbatore Tamil Nadu India Panimalar Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India SIMATS Saveetha Sch Engn Dept Mech Engn Chennai Tamil Nadu India

ISBN: (纸本)9798350386356;9798350386349

In this research, an innovative iot-enabled smart e-healthcare system for predictive healthcare and real-time patient monitoring is presented. To quickly obtain critical health metrics, the system makes use of a range of sensors, including ones that measure blood pressure, temperature, pulse rate, and oxygen levels. The sensor data is first carefully converted from analog to digital format using the Analog-to-Digital (A to D) converter, and then it is sent to the Raspberry Pi, which serves as the primary processing unit. Following processing, the data is moved to a cloud server, where it is stored securely and is only accessible by medical professionals. machine learning models such as Artificial Neural Networks (ANN), Support Vector machines (SVM), Decision Trees (DT), and Naive Bayes (NB) are used to predict health outcomes based on sensor readings in order to enhance the proactive capabilities of the system. The accuracy rate of 97.76% achieved by the ANN is comparatively higher. The results highlight the anticipatory healthcare's revolutionary potential-that is, its capacity to give precise and timely predictions to assist medical professionals.

关键词： E-healthcare iot patient monitoring predictive healthcare sensors machine learning Artificial Neural Networks

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19th international conference on Future Networks and Communications, FNC 2024 / 21st international conference on Mobile Systems and Pervasive computing, MobiSPC 2024 / 14th international conference on Sustainable Energy Information Technology, SEIT 2024

19th International Conference on Future Networks and Communi...

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19th international conference on Future Networks and Communications, FNC 2024 / 21st international conference on Mobile Systems and Pervasive computing, MobiSPC 2024 / 14th international conference on Sustainable Energy Information Technology, SEIT 2024

The proceedings contain 80 papers. The topics discussed include: randomized protocols for resilient peer-to-peer networks;evaluating the performance of 5G NR in indoor environments: an experimental study;an approach towards reducing training time of the input doubling method via clustering for middle-sized data analysis;analysis of legislative framework governing biometric data;designing a testbed for privacy-preserving voice-based social networks;classical machine learning and large models for text-based emotion recognition;one hop routing optimization approach using machine learning;toward an enhanced stock market forecasting with machine learning and deep learning models;smart agriculture system based on iot reference architecture and service choreography;and privacy-preserving crime reporting and rewarding in the presence of active insider and outsider adversaries.

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machine learning-Based Feature Selection for Intrusion Detection Systems in IEC 61850-Based Digital Substations

Machine Learning-Based Feature Selection for Intrusion Detec...

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2024 international conference on Communications Control and computing Technologies for smart Grids

作者： Eynawi, Ahmad Mumrez, Aneeqa Elhez, Ghada Hagemeyer, Veit Karlsruhe Inst Technol KIT Inst Automat & Appl Informat IAI KASTEL Secur Res Labs SRL Karlsruhe Germany

ISBN: (纸本)9798350318562;9798350318555

With the evolution in attack surfaces, smart grid complexity, and adversary models, conventional defense solutions in the smart grid domain have become less effective in providing adequate protection. In recent years, machine learning-based intrusion detection systems have been a current research topic, offering a promising and reliable defense against advanced threats. This work focuses on the development of a machine learning-based feature selection algorithm for a robust and efficient detection of attacks specifically targeting the Sampled Values and Manufacturing Message Specification protocols widely used in IEC 61850-based substations. For dataset generation, we implement several cyber-physical attacks such as injection, replay, time delay, and data modification attacks against a hardware-based substation test environment at KASTEL Security Lab Energy. The generated datasets are used to build and evaluate the developed feature selection algorithm. Our findings show that even a small number of carefully selected features can lead to more efficient and accurate anomaly detection. Considering the scarcity of datasets for smart grids, we have also released our datasets to the community for further analysis and evaluation of data-driven defense mechanisms for digital substations.

关键词： smart grids digital substations IEC 61850 Sampled Values Manufacturing Message Specification intrusion detection systems feature selection ensemble models

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7th international conference on Inventive Computation Technologies, ICICT 2024

7th International Conference on Inventive Computation Techno...

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7th international conference on Inventive Computation Technologies, ICICT 2024

ISBN: (纸本)9798350359299

The proceedings contain 345 papers. The topics discussed include: deep learning-based predictive maintenance for industrial iot applications;optimizing iot data transmission in smart agriculture using cloud computing;CNN-RNN hybrid model to classify a local language slangs using spectral features;knowledge-enriched graph convolution network for hybrid explainable recommendation from review texts and reasoning path;development of emission monitoring and tracking system using iot and Ruby-on-Rails;land use and land cover change detection using google earth engine;ForeScanGuard: proactive monitoring and detection for sustainable forest conservation;digital twins for automotive predictive maintenance;and air quality control using machine learning.

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Using Artificial Intelligence to Defend Internet of Things for smart City Networks 1

引用

8th international conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2023

作者： Nunn, Aaron Prasad, P.W.C. Charles Sturt University Wagga WaggaNSW2650 Australia

ISBN: (数字)9783031717734

ISBN: (纸本)9783031717727

As these iot devices have been embedded and operate within smart City networks they are also open to vulnerabilities similar to other technologies, however, unlike other technologies, these iot cannot run a defence in depth protective layer as these devices will run very specific niche software from vendors for their intended purpose. This leaves many of these iot devices, which can operate power grids, control transit across the cityscape, and provide communications to its citizens or surveillance for protection, are at significant risk of exposure with the emergence of using Artificial Intelligence to enhance attack techniques and therefore would be predisposed to cyber-attacks. This research project explores concepts and proposes a framework for using Artificial Intelligence in combination with modern cyber defence strategies, the application of machine learning or Deep learning to existing technical controls, such as an Intrusion Detection System, then using a Software-Defined Networking or Fog computing as the delivery medium to overcome the resource limitations for a smart City iot network has significant potential. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Greensense: iot-Enabled smart Monitoring And Early Detection System For Optimal Plant Health In Greenhouse

Greensense: IoT-Enabled Smart Monitoring And Early Detection...

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2024 international conference on Advances in Data Engineering and Intelligent computing Systems, ADICS 2024

作者： Venkatesh, R. Aadhira, K.R. Kathiravan, M. Hindustan Institute Of Technology And Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

The emergence of Agriculture 4.0 signifies a transformative shift in traditional farming practices, integrating cutting-edge technologies like iot, machine learning, and robotics to support productivity and sustainability. This era leverages real-time data and automation for precision farming, predictive analytics, and resource optimization, crucial for addressing global food security challenges while minimizing environmental impact. GreenSense represents progress within Agriculture 4.0. It seeks to revolutionize greenhouse management by integrating iot and advanced machine learning algorithms. Through sensors, actuators, and a Raspberry Pi 4 board, this system offers a comprehensive solution for monitoring and optimizing plant health in controlled environments. The motivation behind this system stems from the imperative to enhance agricultural practices sustainably in the face of population growth. By enabling real-time monitoring and swift anomaly detection through application, this paper empowers proactive intervention to mitigate risks and optimize growing conditions. Integration of the YOLOv5 algorithm with 97% accuracy enhances precision in leaf disease and pest detection using OpenCV, reducing crop losses effectively. Beyond greenhouse management, this paper contributes to advancing sustainable agriculture. Its scalable and adaptable nature promotes resource-efficient practices and environmental care. Finally, a user-friendly interface is tailored to facilitate efficient data collection from sensors, enabling comprehensive monitoring of crop conditions, including pest and disease detection. © 2024 IEEE.

关键词： learning algorithms

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