Smart Dual-Compartment Waste Separator Bin for Sustainable Waste Management
Intelligent Waste Management System for Smart Cities
Optimization of Waste Classification System: Leveraging Sensor Technologies for Advanced Segregation and Waste Reduction
Designing a Hybrid Technology for Real-Time Driving Monitoring System
Anti Sleeping Alarm for Drivers using GSM Module
Verilog Based UART System Design
Intel ® Processor Architectural and Integrated Development Environment Exploration
IoT based Smart Agriculture Monitoring Framework with Automation
Designing of an Embedded system for Wireless Sensor Network for Hazardous Gas leakage control for industrial Application
An Integrated Model of Digital Fuel Indicator and GPS Tracking System for Vehicles
Hardware Implementation of Artificial Neural Networks
Fault Analysis on Grid Connected MPPT BasedPhotovoltaic System
High Efficiency Hybrid Intelligent Street Lighting Using A Zigbee Network And Sensors
Design of Dual-Band Bandpass Filter Using Interdigital Embedded Open Loop Triangular Resonator Loaded with Stubs
License Plate Localization Using Novel Recursive Algorithm And Pixel Count Method
Rapid urbanization has increased the generation of mixed household waste, where wet and dry materials are commonly discarded together. Improper segregation reduces recycling efficiency, contaminates recyclable materials, and accelerates landfill saturation. To address these challenges, this paper presents a Smart Dual-Compartment Waste Separator Bin, a low-cost, sensor-based prototype that performs automatic real-time segregation at the source. The system uses a moisture sensor and ultrasonic sensor integrated with an Arduino microcontroller to classify waste as wet or dry and mechanically direct it into the correct compartment through a servo-controlled flap. A secondary ultrasonic sensor provides real-time fill-level monitoring and triggers alerts as compartments approach capacity. Experimental evaluation was conducted on 200 waste samples, and system performance was assessed using standard evaluation metrics. The prototype achieved 92.4% sorting accuracy, validated through a confusion matrix and accuracy formula, while the fill-level module recorded a mean error of 4.8% across repeated trials. Results confirm that the system operates reliably, responds quickly, and maintains stable performance under varying moisture conditions. The modular design also supports future enhancements, including IoT connectivity for cloud-based monitoring and integration with municipal waste management platforms. Compared with earlier sensor-based smart bins, the proposed system offers improved automation, higher accuracy, and better scalability. Overall, the findings demonstrate that a sensor-driven, real-time monitoring approach can significantly enhance waste segregation efficiency and support sustainable smart-city waste management.
This paper presents the design and development of an Intelligent Waste Management System (IWMS) for smart cities, integrating the Internet of Things (IoT), Artificial Intelligence (AI), and sensor-based automation to enhance urban waste disposal. The proposed system utilizes smart bins equipped with real-time sensors for monitoring waste levels, enabling source-level waste segregation, and optimizing collection routes through AI-driven predictive analytics. Furthermore, the system incorporates automated waste-to-resource conversion, including composting and energy generation, to improve sustainability. IoT-enabled dashboards provide municipal authorities with real-time insights, reducing operational costs and minimizing environmental impact. Data privacy and ethical considerations are addressed using secure cloud-based encryption and compliance with smart city data regulations. The proposed IWMS supports sustainable waste management and circular economy principles and contributes to smart city infrastructure advancement.
This paper focuses on designing a Smart Waste Segregation System to promote cleanliness and environmental sustainability. Smart solutions to waste management can significantly boost productivity as technology continues to advance. This system uses an Arduino Uno microcontroller to detect and classify waste into three categories: wet, dry, and metallic. Other components include IR sensors, proximity sensors, raindrop sensors, and stepper motors. The automated system makes sure that waste is separated and disposed of in the right way, reducing environmental pollution and health risks from improper waste disposal. Additionally, the system regularly logs waste data, making it useful for analysis and monitoring. By making proper waste disposal accessible to individuals from all economic backgrounds, this cost-effective and scalable solution not only enhances waste management processes but also contributes to a cleaner and healthier society.
This research examines a driver monitoring system developed using hybrid technologies such as YOLO, CNN, and Haar Cascade, integrating IoT sensors, computer vision, AI, and embedded systems to evaluate driver actions along with vehicular and environmental conditions in real time. By applying deep learning techniques, including CNN and Haar Cascade, the system effectively detects driver fatigue, drowsiness, and distraction. IoT sensors further improve accuracy by capturing physiological and vehicular movement patterns through both wearable and non-wearable approaches, enabling comprehensive behaviour analysis and timely accident-prevention alerts. The study reviews emerging AI-driven driver monitoring solutions and highlights the advantages of AI-embedded detection models implemented with Arduino Uno for efficient sensor data processing. It also discusses challenges related to data quality, computational requirements, and system integration, along with potential mitigation strategies. The conclusion offers recommendations for further research to advance real-time monitoring systems and strengthen road safety.
Driver fatigue is a major contributor to road accidents, frequently leading to serious outcomes. This initiative suggests an anti-sleep alarm system for drivers, incorporating a GSM module to improve road safety. The system monitors the driver's state of awareness and triggers warnings when signs of fatigue are identified. It employs a combination of sensors, such as an eye-blink sensor or head position detector, to continuously assess the driver's state. When the system identifies fatigue, it triggers a sound alarm. Additionally, the integrated GSM module sends an alert message to a preconfigured emergency contact, providing real-time updates about the driver's status and location. This dual-layer alert mechanism ensures timely intervention and reduces the risk of accidents. The system is affordable, simple to install, and appropriate for both commercial and personal vehicles, providing an effective solution to enhance driver safety on the road.
