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Design and Implementation of Real-Time Wireless Sensor Network Protocols with Relay Nodes and Localization Methods for Efficient System Performance

Design and Implementation of Real-Time Wireless Sensor Netwo...

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intelligent control, Computing and Communications (IC3), international conference on

作者： Gunapriya Balan Garima Pandey N Sirisha Sajjad Ziara H Pal Thethi Juttiga Alekhya Department of Electrical and Electronics Engineering New Horizon College of Engineering Bangalore India Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Lovely Professional University Phagwara India Department of Information Technology GRIET Hyderabad Telangana India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

In this research paper we focus on the design and implementation of reliable real time wireless sensor network (WSN) protocols that make use of relay node and proper localization techniques to enhance the performance of the system in real time applications. However, Wireless Sensor Networks (WSNs) offer a very important application in environmental monitoring, industrial control, and smart cities. Maintaining efficient communication protocols and accurate real time localization of sensor nodes, especially in environments with dynamic topologies, is one of the key challenges. This paper presents a robust WSN protocol with relay nodes which achieve both energy efficiency and communication efficiency. Additionally, it examines specialized localization algorithms for use in real time systems that offer enhanced localization precision without sacrificing system performance. Results show the reliability, scalability and energy efficiency of WSNs if utilized in real time applications can be highly improved using the optimized protocols that are coupled with localization techniques.

关键词： Location awareness Wireless sensor networks Protocols Accuracy Smart cities System performance Reliability engineering Real-time systems Energy efficiency Relays

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Drone Surveillance for intelligent Multi-Vehicles monitoring and Classification

Drone Surveillance for Intelligent Multi-Vehicles Monitoring...

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international conference on Advancements in Computational Sciences (ICACS)

作者： Ghulam Mujtaba Muhammad Hanzla Ahmad Jalal Dept. of Computer Sciences Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

Accurate vehicle detection and classification from aerial imagery are critical components for advancing intelligent Transportation Systems (ITS), yet existing methodologies often face significant limitations due to environmental variability and data inconsistencies. This paper presents a pioneering approach for robust vehicle analysis using UAV-captured images, incorporating a novel six-stage framework to overcome these challenges. Initially, we apply GAN-based Super-Resolution (ESRGAN) to enhance image quality, addressing resolution issues and noise interference. The segmentation process is then performed using U-Net, ensuring precise extraction of vehicle-related features from complex backgrounds. For vehicle detection, we leverage the cutting-edge YOLOv10 model, renowned for its exceptional accuracy and real-time performance. Feature extraction is accomplished through a hybrid of CNN, HOG, and Wavelet Transform, enabling the capture of both spatial and frequency domain characteristics. These features undergo optimization with Particle Swarm Optimization (PSO), ensuring efficient selection of the most discriminative attributes. Finally, classification is performed using a Neuro-Fuzzy Classifier, adept at handling the inherent uncertainties in aerial data. Our methodology was evaluated on the Aerial Car Dataset, achieving a remarkable classification accuracy of 95%, significantly outperforming conventional techniques. The results not only demonstrate the efficacy of our approach in real-world vehicle monitoring applications but also underline its potential to revolutionize traffic surveillance and management, offering enhanced operational efficiency and scalability in diverse environments.

关键词： Wavelet transforms Image quality Image segmentation Accuracy Vehicle detection Surveillance Superresolution Traffic control Feature extraction Particle swarm optimization

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Federated Learning-Based intelligent Indoor Smoke and Fire Detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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Telecommunications and intelligent Systems (ICTIS), international conference on

作者： Mohamed Rafik Aymene Berkani Ammar Chouchane Yassine Himeur Anes Abdennebi Seref Sagiroglu Abbes Amira Research Laboratory in Advanced Electronics Systems (LSEA) University Yahia Fares of Medea Medea Algeria Laboratory of LI3C University Biskra University Center of Barika Barika Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Software and Information Technology Engineering École de Technologie Supérieure Montréal Canada Artificial Intelligence and Big Data Analytics Security R&D Center Gazi University Ankara Turkey Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments.

关键词： Privacy Smart buildings Costs Federated learning Atmospheric modeling Data models Convolutional neural networks Wearable sensors monitoring intelligent sensors

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Sentinel Exploring the Effectiveness of Autonomous IoT Based Security Robots in Enhancing Surveillance

Sentinel Exploring the Effectiveness of Autonomous IoT Based...

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Advanced Computing and Emerging Technologies (ACET), international conference on

作者： Ajitesh Kumar Mona Kumari Computer Engineering and Applications GLA University Mathura India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

This paper introduces an IIoT driven intelligent Security based robot "SENTINEL", leveraging an Arduino Uno, camera unit, ultrasonic sensor, motor controller, motors,The envisioned robot is engineered to autonomously survey a specified area, capturing images and videos with the camera unit. The ultrasonic sensor identifies obstacles to avert collisions, alerting the user. The robot maneuvers and alters its course through the motor controller and motors, managed by an Arduino Uno. This system finds utility in various domains, including surveillance and security, enhancing security-based system. It is economically developed, broadening its accessibility for future used. The system's deployment has undergone testing, demonstrating proficiency in recognizing and responding to environmental cues. A web- based interface facilitates remote control and monitoring by users.

关键词： Service robots Surveillance Scalability Robot vision systems Motors Software Hardware Security Collision avoidance Interoperability

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Architecture for AI-Based Validation of Wastewater Management Using Open Data Exchange Technique

Architecture for AI-Based Validation of Wastewater Managemen...

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2023 international conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： William, P. Oyebode, Oluwadare Joshua Ramu, Gandikota Gupta, Manish Alzubaidi, Laith H. Srivastava, Arun Pratap Sanjivani College of Engineering Savitribai Phule Pune University Department of Information Technology Pune India Afe Babalola University Civil and Environmental Engineering Ekiti State Ado-Ekiti Nigeria Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad500043 India Lovely Professional University Division of Research and Development Phagwara India The Islamic University Faculty of Engineering Najaf Iraq Lloyd Institute of Engineering and Technology Department of CSE Uttar Pradesh Greater Noida201308 India

ISBN: (纸本)9798350333244

To processing plant-wide data, it is not uncommon for there to be issues with sensors. Some examples of these issues include sensor failures, sensor calibration difficulties, sensor fouling and blockage, and connection problems between sensors, actuators, and the data management system. These problems may have an effect on the quality of the data, which will make it more difficult to implement sophisticated data-driven monitoring and control of (crucial) water operations. A method for the intelligent validation of data is provided in this article. The sensor data coming from a wastewater treatment plant are validated and verified by this process. The application for data validation has been integrated, tested, and put into production at the water utility known as Water Net. The method of validation is predicated on an anomaly detector that, in the event that an anomaly is found, combines (statistical) threshold-based procedures, (AI-based) autoencoder model predictions, and a component for data reconciliation. This occurrence repeats itself whenever an anomaly of some kind is found. In spite of the signal's volatility, the autoencoder models displayed outstanding levels of accuracy and great reconciliation performance. In addition, signals for both verified and raw data are provided in a way that is extremely near to real time to a dashboard. This keeps the dashboard up to date with the most recent information. Validated data may also be utilized as a screening tool for data that is being ingested by another AI-based model in order to monitor and intelligently manage the wastewater treatment plant, as well as to minimize greenhouse gas emissions and energy consumption while maintaining effluent water quality requirements. These goals may be accomplished while still meeting all applicable criteria. If this were done, it would be in compliance with the regulations for maintaining the effluent water's quality. © 2023 IEEE.

关键词： Wastewater treatment

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intelligent Electrostatic Oil Fume Treatment Method Utilizing IoT Technology

Intelligent Electrostatic Oil Fume Treatment Method Utilizin...

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IEEE international conference on Orange Technologies (ICOT)

作者： Chih-Chieh Lu Shi-Huang Chen Chia-Chun Kang Department of Computer Science and Information Engineering Shu-Te University Kaohsiung City Taiwan (R.O.C.)

ISBN: (数字)9798331523602

ISBN: (纸本)9798331523619

Electrostatic precipitators are widely recognized as effective air pollution control equipment by environmental protection agencies. These devices utilize high voltage to generate an electrostatic field, causing oil fumes and dust particles to polarize. Through electrostatic attraction, these particles are captured on collector plates, achieving high-efficiency filtration. However, traditional electrostatic precipitators require frequent cleaning and maintenance, leading to operational downtime and increased costs. This study proposes a novel solution: integrating IoT technologies into electrostatic precipitators to create a smart system capable of real-time monitoring and data analysis. Sensors embedded in the system collect data such as sparking frequency, voltage, current, temperature, and air pressure. By leveraging big data analytics, maintenance can be optimized, reducing costs and human effort. This innovation holds significant value for the restaurant industry, ensuring compliance with air pollution laws while improving operational efficiency. Furthermore, this research aligns with global sustainability initiatives, including the United Nations' Sustainable Development Goals (SDGs), by advancing eco-friendly technology and addressing critical environmental challenges.

关键词： Temperature sensors Air pollution control Temperature distribution Costs Oils Maintenance Internet of Things Electrostatics Sustainable development Electrostatic precipitators

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Enhanced Perimeter Intrusion Detection System (PIDS) - Resilient to environmental Variations for Military Applications

Enhanced Perimeter Intrusion Detection System (PIDS) - Resil...

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Annual IEEE Systems conference

作者： Ravindranath KV Irfan Ahmad Khan ComputerScience Department Data Science Texas A&M University College Station USA Marine Engineering Technology Department in a Joint Affiliate Appointment With Electrical & Computer Engineering and Computer Science & Engineering Department Texas A&M University Galveston USA

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

Perimeter intrusion detection systems (PIDS) often face challenges in accurately detecting breaches while minimizing false alarms triggered by environmental factors. This paper presents a reliable PIDS approach integrating fiber optic sensing with several environmental transducers and a sophisticated control algorithm. The technical framework uses a distributed network of fiber optic cables installed along the perimeter as sensors to detect vibrations and disturbances. These high-sensitivity fiber optic sensors provide input data to the control system. environmental transducers like rain gauges, anemometers, and temperature sensors are incorporated to detect weather conditions near the *** core innovation is a two-part control algorithm that analyzes this multi-modal sensor data to classify events and make intelligent decisions. The first segment uses historical data to establish adaptive baselines tuned to different conditions. Machine learning models extract informative features to distinguish between various types of events. The second segment employs anomaly detection to identify disturbances in real-time sensor data. A Gaussian Mixture Model algorithm models normal behavior and detects *** testing under diverse simulated scenarios will validate the system's ability to detect intrusions reliably while minimizing false alarms caused by changing environmental contexts. Key advantages over existing methods include higher precision, addressing cold start problems, real-time low latency performance, cost-effectiveness, and resilience to unforeseen events. The proposed system promises substantial military payoffs by enhancing perimeter security and response efficacy.

关键词： Optical fibers Optical fiber sensors Transducers Intrusion detection Real-time systems Sensors Classification algorithms

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Research and design of intelligent monitoring system for greenhouse based on internet of things 2021

Research and design of intelligent monitoring system for gre...

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4th international conference on Information Systems and computer Aided Education, ICISCAE 2021

作者： Wang, Xiaojun Department of Mechanical and Electrical Engineering Weifang Vocational College Weifang262737 China

ISBN: (纸本)9781450390255

Greenhouse is an artificial growth environment for building crops. Through intelligent control and optimization of the growth environment of crops in greenhouse, crops can always be in the best environmental conditions at different growth stages. Greenhouse based on Internet of Things technology can change the limitation of traditional agriculture by many factors such as region, natural environment, climate and so on, which is of great significance to agricultural production. China has the world's third largest land area and the world's largest population, but China's arable land area is insufficient and has a decreasing trend year by year. Food supply is facing prominent problems. The traditional greenhouse monitoring system uses wired form, which not only increases the complexity of greenhouse lines, but also is detrimental to crop growth. The intelligent control technology of crop greenhouse environment provides a new power for agricultural intelligence. The greenhouse based on Internet of things technology breaks through the limitation of traditional agriculture by many factors such as region, natural environment, climate and so on. © 2021 ACM.

关键词： Internet of things

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AI-Driven Traffic monitoring System for Real-Time Congestion Detection and Route Optimization in 6G-Enabled Smart Cities

AI-Driven Traffic Monitoring System for Real-Time Congestion...

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international conference on Inventive Computation Technologies (ICICT)

作者： S. Sridevi Alex David S Pujari Mahi Pranathi Kunamneni Sravya Prabhu Shankar B Sakthi Karthi Durai B Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Urban development and rising vehicular traffic patterns create major issues with congestion together with environmental problems. Activity Management via Deep Learning uses computer vision reinforcement alongside deep learning to study current traffic videos and identify traffic blockages while offering best route alternatives. The system achieves fast and low-latency traffic analysis through YOLO-based vehicle detection and graph-based pathfinding algorithms while taking advantage of 6G network capabilities to perform dynamic rerouting. The system uses MongoDB to store data together with Twilio API notifications and relies on AWS cloud services for ensuring scalability and reliability. Urban mobility reaches enhancement through autonomous traffic management thanks to this solution which outperforms static sensor-based traffic systems and manual control features. This system benefits smart cities by selecting intelligent routes and analyzing future traffic flow data which leads to shorter journeys and reduced carbon emissions and lower fuel costs thereby optimizing transportation networks

关键词： Deep learning 6G mobile communication Smart cities Scalability Heuristic algorithms Transportation Real-time systems Optimization monitoring Traffic congestion

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Optimizing the energy cost function for a wastewater treatment plant model by employing dynamic generation of a two-dimensional reference point 28

Optimizing the energy cost function for a wastewater treatme...

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28th IEEE international conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Roşu, Bogdan Murariu, Gabriel Roşu, Adrian Iticescu, Cǎtalina Mocanu, George Tupu, Eliza Arseni, Maxim Dunǎrea de Jos University of Galaţi Faculty of Automation Computer Sciences Electronics and Electrical Engineering Galati Romania Dunǎrea de Jos University of Galaţi REXDAN Research Infrastructure Faculty of Sciences and Environment Galati Romania Dunǎrea de Jos University of Galaţi Faculty of Physical Education and Sport Galati Romania Rǎsvan Angheluţǎ Natural Sciences Museum Complex Galati Romania SPINOFF GEOMAX S.R.L. Galati Romania

ISBN: (纸本)9798350339918

This article presents a study on determining the optimal reference values for two proportional-integral (PI) type controllers taking also into account the impact on a simulated wastewater treatment plant (WWTP). In this regard, we considered a predictive control algorithm for a distributed model, which includes correlation between subsystems. Additionally, we have taken into consideration the comparative testing of two interpolation methods for the purpose of dynamically generating reference points, applied on Benchmark Simulation Model No.2 (BSM2) with an implemented control strategy. This allowed us to determine the global minimum points and propose a method for aligning the global minimum points obtained for both cost functions. © 2023 IEEE.

关键词： Cost functions

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