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Design and Implementation of Traffic Monitoring for Indian smart Cities 14

Design and Implementation of Traffic Monitoring for Indian S...

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14th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Shama, B.N. Sathish, Dayakshini Bharath Janardana, K.R. Nachiketh Prabhu, Sandeep Department of ECE St. Joseph Engineering College Mangaluru India

Traffic monitoring is an essential component of smart city development in India. Indian cities face significant traffic congestion, which not only results in economic losses but also affects the quality of life of citizens and death of patients due to the late arrival to the hospitals. Therefore, smart traffic management systems have become a priority for Indian cities to enhance mobility, reduce congestion, and improve the safety of road users. There are several approaches that Indian cities can take to implement traffic monitoring systems. One of the most common approaches is the use of intelligent transportation systems (ITS) that incorporate advanced technologies such as real-time traffic monitoring, predictive analytics, and adaptive traffic signal control. In this article, Real-time traffic monitoring systems are discussed, which includes the use of sensors, cameras, and other monitoring devices to collect data on traffic flow, speed, and density. It provides a safe path for the Ambulance by making use of RFID Technology. Also IR sensors are used near school zones to reduce the accidents, while crossing the roads by alerting the driver to reduce the speed of the vehicle. © Grenze Scientific Society, 2023.

关键词： Traffic congestion

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intelligent Energy Management for Electric Vehicles: Fuzzy Logic and Neural Network Integration 10

Intelligent Energy Management for Electric Vehicles: Fuzzy L...

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10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Thirumalaikumari, T. Princy, D. Lita Uma, S. Sadaieswaran, R. Malathi, P. Legapriyadharshini, N. Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Science Tamil Nadu Chennai India Meenakshi College of Engineering Department of Master of Computer Applications Tamil Nadu Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Mahabalipuram India Tagore College of arts and science Department of Computer Applications Chennai Chromepet India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Data Analytics Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Deemed to be University Department of Computer Application Tamil Nadu Chennai India

ISBN: (纸本)9798350384369

This study suggests a novel methodology for intelligent energy management in electric vehicles (EVs) through the integration of neural networks and fuzzy logic. Achieving enhanced energy efficiency for electric vehicles is a critical concern that is tackled in this research through the integration of neural networks' decision-making and pattern recognition functionalities with fuzzy logic's adaptability and learning capabilities. The proposed method seeks to optimize energy consumption, enhance overall performance, and extend driving range. The integrated system is subjected to rigorous testing using a meticulously planned experimental setup and simulation environment, which enables it to surpass the performance of more conventional approaches to energy management. The results emphasize the potential of the novel methodology to improve environmentally sustainable transportation options and propel the development of electric vehicle technology. This study not only offers valuable insights into efficient energy management but also establishes the groundwork for subsequent developments in the domain. © 2024 IEEE.

关键词： Electric Mobility Electric Vehicles Energy Management intelligent Systems Neural Networks smart Grid

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ENCL: Empirical Evaluation of Consumer Behaviour Analysis Model Using Internet of Things (IoT) Based Enhanced Neural Classification Logic

ENCL: Empirical Evaluation of Consumer Behaviour Analysis Mo...

引用

2024 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2024

作者： Merlin, S. Sudha Malathy, K. Ravi, S. Arun, M. Raut, S.P. Fawareh, H. Dmi College of Engineering Department of Electrical and Electronics Engineering Palanchur Chennai India S.A. Engineering College Department of Mca Chennai India Department of Vlsi Microelectronics Chennai India Kings College of Engineering Dept of Computer Science and Engineering Punalkulam Thanjavur India Yeshwantrao Chavan College of Engineering Department of Civil Engineering Nagpur India Zarqa University Faculty of Information Technology Software Engineering Dept Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The proliferation of consumer data can be attributed to 'the Internet of Things (IoT),' which makes it easier to access and more detailed than before. In this research our purpose is to evaluate the effectiveness of a consumer behavior analysis model using data from IoT. The quality of the model is evaluated on multiple parameters - accuracy, predictability and business sense for marketing. It uses the analytics of huge amounts for data from IoT devices to predict patterns, customer preferences and trends. The proposed method called as Enhanced Neural Classification Logic (ENCL) is cross-validated with the already existing method, which is termed as Consumer behavior Forecasting Strategy (CBFS), and helps users to share gather or process information in a fast and cost-effective way through the proposed method. The results are useful to grasp the promise of IoT in more effective consumer behavior analysis. The research portrays the strengths, weaknesses and possible areas of improvement in this model which will be beneficial for both academic and industry practitioners. As such, the objective of this study is to show how an IoT-based consumer behavior analysis can increase business growth and customer satisfaction. The experimental evaluations demonstrate significant improvements in predictive performance over traditional methods, providing actionable insights for targeted marketing and strategic decision-making. The results highlight ENCL's potential to transform consumer behavior analysis by delivering more precise and nuanced understandings of market dynamics. © 2024 IEEE.

关键词： CBFS Consumer Behavior data Analysis ENCL Internet of Things IoT Marketing Strategy Model Evaluation

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Integrated smart Dam Control System Using Machine Learning 6

Integrated Smart Dam Control System Using Machine Learning

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6th international conference on Advancements in computing, ICAC 2024

作者： Lekamge, L.R.S.T. Samaranayake, N.P.T.D. Weerawarna, N.V. Dissanayake, D.M.J.C.B. Rathnayake, H.M.S.C. Amarasena, Nelum Sri Lanka Institute of Information Technology Department of Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

Efficient management of dams and reservoirs is essential for optimizing water resources, preventing floods, and ensuring structural reliability of the dams. In Sri Lanka, manual dam operations introduce inefficiencies and potential risks. This study aims to develop an intelligent dam control system that integrates ML (Machine Learning) and IoT (Internet of Things) for dam gate automation, flood prevention, and dam crack analysis. The system automates sluice gate operations based on water demand predictions using LightGBM, enhances flood prevention through a pre-release strategy, and employs CNNs (Convolutional Neural Networks) for accurate crack detection in dam structures. Real-time data analytics, enabled by IoT sensors and cloud-based storage, enhance the system's scalability and reliability, allowing for more responsive and adaptive management. The results indicate significant improvements in water release scheduling, flood prevention accuracy, and crack analysis precision, demonstrating the practical viability of ML and IoT integration in dam safety. This data-driven, scalable approach provides a robust solution for optimized dam management, particularly suited to regions facing similar challenges where resources and oversight are limited. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT Revolutionizing Healthcare: A Survey of smart Healthcare System Architectures

IoT Revolutionizing Healthcare: A Survey of Smart Healthcare...

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2023 IEEE international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Delsi Robinsha, S. Amutha, B. Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chengalpattu District Tamilnadu Kattankulathur603 203 India

ISBN: (纸本)9798350305708

The Internet of Things (IoT) has had a profound impact on the healthcare sector by paving the way for the creation of smart healthcare systems that improve patient monitoring, diagnosis, and individualized care. The purpose of this study is to provide a review of the state of the art in IoT-based healthcare system architectures. In this study, we take a look at the current state of the art of Internet of Things (IoT) technologies, protocols, and communication models used in healthcare settings. The survey digs into the roles and interconnections of key components and layers of IoT architecture, such as sensor nodes, gateways, cloud platforms, and data analytics, in a smart healthcare ecosystem. Furthermore, it investigates the potential for incorporating wearable devices, medical sensors, and other smart healthcare gadgets that enable real - time data gathering and transmission. In addition, the paper explores the dangers and vulnerabilities inherent in IoT - based healthcare systems, as well as the current solutions available for addressing these issues and safeguarding sensitive patient data. The challenges of interoperability and standardization are also explored in order to guarantee the smooth integration and communication of various healthcare equipment and systems. Using examples like remote patient monitoring, chronic illness management, fall detection, and elderly care, the survey emphasizes the beneficial effects of IoT on patient outcomes and healthcare efficiency. © 2023 IEEE.

关键词： Global System for Mobile Communication (GSM) Healthcare Industry intelligent Healthcare Systems Internet of Things (IoT) Remote Patient Monitoring

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Energy Efficiency in IoT-Based smart Healthcare 3rd

Energy Efficiency in IoT-Based Smart Healthcare

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3rd international conference on computing, Communications, and Cyber-Security, IC4S 2021

作者： Sangra, Pallavi Rana, Bharti Singh, Yashwant Department of Computer Science and Information Technology Central University of Jammu Jammu and Kashmir Samba181143 India

ISBN: (纸本)9789811911415

IoT-based smart healthcare is a new technological shift toward efficient, convenient, cheaper, and faster medical services using artificial learning, big data analytics, sensor technologies, and cloud computing. smart healthcare reduces the time and cost to avail the services region-wise rather than to get the clinical services at distant locations. Along with this, smart healthcare poses many challenging issues. One of the greatest challenges in smart healthcare is to accomplish the energy-efficient services as smart nodes are energy constrained. Therefore, this study addresses the energy consumption challenges in smart healthcare sector. Then, we focus on energy preserving mechanisms to reduce the energy consumption. The various energy-conserving mechanisms such as intelligent techniques, duty cycling techniques, collision resolution techniques, and edge techniques in context to smart healthcare have been discussed for reducing energy consumption. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy consumption Energy efficiency Internet of things smart healthcare

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Next Generation intelligent IoT Use Case in smart Manufacturing

Next Generation Intelligent IoT Use Case in Smart Manufactur...

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international conference on data analytics and Management, ICDAM 2023

作者： Rathore, Bharati Birmingham City University BirminghamB5 5JU United Kingdom

ISBN: (纸本)9789819965526

smart manufacturing has become a significant topic of interest among manufacturing industry professionals and researchers in recent times. smart manufacturing involves the incorporation of cutting-edge technologies, including the Internet of things, cyber-physical systems, cloud computing, and big data. This is evident in the Industry 4.0 framework. Henceforth, next generation smart manufacturing showcases a deep amalgamation of artificial intelligence (AI) tech and highly developed production technologies. It is woven into each stage of the design, production, product, and service cycle and influences the entire life cycle. The subsequent form of smart manufacturing is the central propellant for the novel industrial revolution and is expected to be the principal catalyst for the transformation and betterment of the manufacturing industry for the generations ahead. Through this research, we proposed a ‘4*S Model’ via conceptualization of smart, sensorable, sustainable, and secure concepts at various stages of manufacturing. The evolution of smart manufacturing for Industry 4.0 is an ongoing process and this research will provide insights for further developments in manufacturing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Industry 4.0

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10th EAI international conference on Mobility, IoT and smart Cities, Mobility IoT 2023

10th EAI International Conference on Mobility, IoT and Smart...

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10th EAI international conference on Mobility, IoT and smart Cities, Mobility IoT 2023

ISBN: (纸本)9783031565328

The proceedings contain 29 papers. The special focus in this conference is on Mobility, IoT and smart Cities. The topics include: Sustainable Mobility for the Sustainable Development of Modern Urban Agglomerations;mathematical Model of Transport Stream Sustainability at Road Network Areas During Traffic Jams;air Quality Improvement Based on the Application of Emission Monitoring System with Triangulation;Machine Learning Regression Model in MATLAB for Predicting Gas Emissions During the Pellet Combustion;intelligent Devices Used in Healthcare;strategic Decision-Making for Evaluating Risks Significance in Degreasing and Pickling;methods of Choosing Technology for Improving the Environmental Indicators of Vehicles;an Engaging Mobile Application for Tourists with Gamification Elements that Promotes Lesser-Known Coffee Shops in the Copenhagen Suburbs;Image Transmission in LoRaWAN: An Experimental Study on Transmission Time;the Effect of a Face Wall on a Cellular Structure During Bending;big data analytics: A Systematic Literature Review on Fields of Application As Well As Costs and Risks in Management Reporting;sustainable Development of Rural Areas: Machine Learning for Semantic Segmentation of Agricultural Fields;Measurement of data Transmission Speed in 5G and 4G LTE Mobile Networks Between a Moving Vehicle and the Environment;optimization of 3D Computer Model Parameters for Spherical Elements Modeling;the Clean Energy as Solution for Global Challenges and Sustainable Development;virtual Influencer—A New Phenomenon in the Marketing Communication or Illusion?;tools for Creating Business Models in smart Cities;current Challenges of Autonomous Vehicles in Relation to Regulatory Issues;use of Artificial Intelligence in Supply Chain Management.

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An intelligent Energy-Efficient Routing Protocol for Wearable Body Area Networks

An Intelligent Energy-Efficient Routing Protocol for Wearabl...

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international conference on data analytics, intelligent computing, and Cyber Security, ICDIC 2020

作者： Vadlamudi, Muniraju Naidu Hussian, Md. Asdaque Department of Computer Science and Engineering School of Engineering Malla Reddy University Telangana Hyderabad India Faculty of Computer Studies Arab Open University Manama Bahrain

ISBN: (纸本)9789811941610

The wireless body area network (WBAN) is a wireless sensor network that calculates natural constraints for special applications using wireless sensor nodes in and around the human body. Routing protocols used in wireless body networks influence energy, topology, temperature, location, sensor radio choice, and appropriate quality of service in sensor nodes. The achievement of energy effectiveness in wireless body networks (WBANs) is one of these challenges, as energy efficiency eventually affects the durability of the network. The majority of applications in the wireless body sensor network (WBAN) currently require efficient transmission processes for remote monitoring of wearable system data on demand and in a timely manner. This paper proposes a reliable, power efficient, and highly stable network for wireless body area sensor networks. The simulation has been completed, and the findings have been found to be consistent. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Routing protocols

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IoT-Enabled Dual-Sensing smart Waste Management System: Enhancing Urban Cleanliness and Sustainability in smart Cities 8

IoT-Enabled Dual-Sensing Smart Waste Management System: Enha...

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8th SLAAI - international conference on Artificial Intelligence, SLAAI-ICAI 2024

作者： Rathnayake, R.M.P.M.D. Chanaka, T.M.M. Dabare, P.T.R. Hewagamage, K.P. The Open University of Sri Lanka Department of Mechanical Engineering Nawala Sri Lanka The Open University of Sri Lanka Department of Electrical and Computer Engineering Nawala Sri Lanka Univercity of Colombo School of Computing Colombo Sri Lanka

ISBN: (纸本)9798331509170

The management of trash in urban areas encounters substantial obstacles because of the increasing population density and ineffective collection methods, resulting in environmental and health risks in urban cities. Current Internet of Things solutions generally concentrate on monitoring bin fill levels, but fail to include important factors such as garbage decomposing and route optimization. This study presents a novel data-driven artificial intelligence method for garbage management by combining dual-sensing smart bins with real-Time analytics. The system we use utilizes ultrasonic sensors to detect the fill-level and methane sensors to monitor spoiling in bins located in Dehiwala, Sri Lanka. The data is transmitted by ESP32 WiFi modules to a cloud-based platform. An individualized Node-RED integration enables the processing of data, while machine learning models enhance the efficiency of collection routes and forecast trends of trash generation. During the pilot implementation involving three smart bins over a four-month duration, the system exhibited several critical capabilities: precise real-Time fill-level detection with 95% accuracy, dependable spoilage monitoring with 92% accuracy in methane detection, and effective pattern recognition for weekly and monthly waste generation trends. The route optimization technique demonstrated a 15% enhancement in path efficiency relative to fixed routes during simulation testing. Analysis of temporal patterns identified constant peak fill times and spoiling trends, facilitating predictive scheduling possibilities. This study demonstrates the capacity of data-driven intelligence formulate a paradigm for scalable intelligent waste management systems, showcasing the potential for biogas system integration and adaptive capacity planning. © 2024 IEEE.

关键词： Health risks

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