IEEE 2023 : Machine Learning with Internet of Things

IEEE 2023: Freshness of Food Detection Using IoT & Machine Learning

Abstract: In today's world, food spoilage is a crucial problem as consuming spoiled food is harmful for consumers. Our project aims at detecting spoiled food using appropriate sensors and monitoring gases released by the particular food item. A micro controller that senses this, issues an alert using internet of things, so that appropriate action can be taken. This has wide scale application in food industries where food detection is done manually. We plan on implementing machine learning to this model so we can estimate how likely a food is going to get spoiled and in what duration, if brought from a particular vendor. This will increase competition among retailers to sell more healthy and fresh food and create a safe world for all consumers alike.

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IEEE 2023: Enhanced Air Quality Monitoring using Machine Learning and IoT

Abstract:  The main goal of this project is to develop a model for Air Pollution Monitoring system using Machine learning with Internet of Things (IoT is basically a network of physical nodes at internet server) which facilitates the monitoring of air pollution by reading the values of Carbon Monoxide (CO), Carbon Monoxide (CO), Benzene, Nitrogen Dioxide (NO2), etc from environment using embedded system, values are send to Web Server through IOT Cloud service. In web server Air Pollution classification model will be build using Machine Learning process. For the IOT Cloud Service the environment data are collected by web server and it will pass to ML Air pollution classification trained model, which process the data and gives out the Air pollution classification result.

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IEEE 2023: An IoT-Enabled Worker Safety Helmet with Web-Based Monitoring and Alert System                 

Abstract:  This system proposes an Internet of Things (IoT)-enabled worker safety helmet equipped with a web-based monitoring and alert system. The helmet integrates sensors for temperature, heart rate, humidity, and harmful gases, alongside GPS for real-time location tracking. Data collected by the sensors is transmitted to the Thing Speak platform and visualized on a web interface, enabling supervisors to monitor worker safety in real-time. The system analyzes data and triggers alerts when unsafe conditions like high temperature, excessive heart rate, or gas presence are detected, facilitating proactive responses to ensure worker well-being. This novel application of IoT technology promotes workplace safety and protects workers from environmental hazards. 

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IEEE 2023: Integrated IoT Solutions for Smart Urban Infrastructure

Abstract: As urbanization accelerates globally, the need for sustainable and efficient urban infrastructure becomes paramount. This paper explores the integration of Internet of Things (IoT) technologies in the development of a smart city, focusing on three key components: street lighting, smart bridges, and traffic control. The proposed smart city framework leverages interconnected sensors, actuators, and data analytics to enhance the overall urban experience. The proposed integrated IoT solutions for street lighting, smart bridges, and traffic control collectively form a comprehensive framework for the development of a smart city. Through the seamless connectivity and data-driven decision-making offered by IoT technologies, cities can enhance their resilience, sustainability, and overall quality of life for their residents.