Adaptive Reinforcement Learning Strategies for Efficient Parking Space Management in Fully Automated Multi-Level Parking Systems
IoT Enabled Route Optimization for Smart Waste Management using Machine Learning
Smart Polyhouse Robotic System with IoT
An IoT-Enabled Machine Learning System for Efficient Disease Detection and Crop Management in Green Gram Cultivation
Implementation of IoT Based Smart LPG Gas Monitoring and Automatic Booking System
Design and Implementation of Medicine Dispenser System using IoT
Smart Water Management by Smart Sensors and IoT: Enhancing Efficiency and Sustainability with Automatic Pump Control Systems
Integrating IoT, ML and Cloud Computing for Sustainable Agriculture: Opportunities and Challenges
Smart Innovations in IoT Technology
Implementation of IoT Based Forest Fire Detection and Prevention System
Smart Home Security: An IoT-Driven Facial Recognition Door Lock using ESP32-CAM
Adaptive Reinforcement Learning Strategies for Efficient Parking Space Management in Fully Automated Multi-Level Parking Systems
Security Challenges and Measures of IoT Devices and its Networks
IoT and its Evolution in Healthcare
Implementation of IoT Based Forest Fire Detection and Prevention System
A Blockchain Based Cyber Thread Detection System for IIoT Networks
Rapid urbanization and rising vehicle ownership have intensified the demand for efficient parking, especially in dense urban areas. Fully automated multi-level parking systems provide a promising solution, but real-time space allocation remains a major challenge. This paper presents an adaptive Reinforcement Learning (RL) framework using Deep Q- learning to optimize dynamic slot allocation. The state space integrates high-resolution data such as vehicle dimensions, parking duration, demand patterns, and occupancy levels, enabling context-aware decision-making. The action space supports adaptive strategies including priority-based assignment, dynamic rerouting, and load balancing. A novel reward function balances space utilization, vehicle search time, and energy efficiency while prioritizing user- centric metrics like wait time and throughput. Simulations in a realistic 3D parking environment show a 10% reduction in search times and a 15% improvement in throughput compared to heuristic methods. These findings demonstrate the potential of RL-driven approaches to transform automated parking, advancing smart transportation theory while offering practical guidance for next-generation urban infrastructure.
The increasing demand for efficient urban waste management necessitates intelligent systems for optimizing collection routes. This study presents an IoT-enabled route optimization framework, integrating machine learning and metaheuristic algorithms to enhance operational efficiency and minimize environmental impact. Ultrasonic sensors, interfaced with ESP32 microcontrollers, monitor bin fill levels in real-time, transmitting data to a centralized database for analysis. A data-driven decision-making system prioritizes bins requiring immediate collection, reducing redundant trips. Ant Colony Optimization (ACO) dynamically generates optimal routes originating and terminating at a central depot while exclusively targeting filled bins, with Folium-based geospatial visualization providing an interactive mapping interface for collection teams. Additionally, machine learning models analyze historical sensor data to predict waste accumulation trends, enabling proactive route adjustments. By leveraging IoT-driven data acquisition, predictive analytics, and combinatorial optimization, this framework significantly reduces fuel consumption, operational costs, and carbon emissions, aligning with sustainable urban development goals.
The quantity and quality of crop yields determine the growth of the agriculture sector. By offering a regulated environment, polyhouse farming is a successful technique that optimizes yield with the least amount of resources. In order to improve farming precision, this paper suggests an automated smart polyhouse robotic system based on the Internet of Things. The system uses sensors and Internet of Things devices to continuously monitor and analyze conditions in order to increase crop yield and quality by automating tasks within the polyhouse. We suggested an automated method for determining crop- specific water and soil moisture levels. It removes the need for manual surveys by informing farmers about temperature, humidity, and soil conditions. A database containing the best crop management schedules is part of this system, which guarantees resource efficiency and boosts crop yield.
This work presents an advanced plant care system for green gram cultivation using sensors, IoT, and machine learning to monitor real-time environmental factors such as temperature, humidity, and soil moisture. The system's key feature is its ability to predict diseases like powdery mildew and anthracnose by analyzing trends in these parameters, enabling early intervention and preventing yield loss. The machine learning model achieved an overall accuracy of 94%, with high performance across multiple diseases: for Disease 1 (D1), it had a precision of 0.70 and recall of 0.75, while Disease 2 (D2) showed a precision of 1.00 and recall of 0.80. Disease 3 (D3) had perfect precision but lower recall at 0.50, and both Disease 4 (D4) and healthy plants were identified with 100% precision and recall. The system also offers a graphical interface through IoT for remote monitoring, enabling farmers to track key parameters in real time. In critical conditions, it generates alerts, allowing manual control of irrigation to ensure optimal plant health and growth. The combination of IoT and machine learning provides a comprehensive solution to enhance crop care and productivity in green gram farming.
This paper presents a solution to a common issue associated with household LPG gas usage. With increasingly busy lifestyles, monitoring the gas level in cylinders and scheduling timely refills typically becomes difficult. To address this challenge, an IoT-based Smart LPG Gas Monitoring and Automatic Booking System is proposed. Given the high demand for LPG in daily life, the system enables continuous monitoring of gas consumption and provides timely alerts. When the gas flow reaches a predefined threshold, the system sends an alert notification to the user through an IoT-based application. Additionally, the system automates the booking of a new cylinder, enhancing user convenience. A flow sensor is used to measure the amount of gas consumed, while the MQ-4 gas sensor detects any potential gas leakage. Upon detecting a leak, the MQ-4 sensor sends an analog signal to the ESP32 microcontroller, which in turn communicates with the cloud platform to notify the user through the IoT app. This system not only streamlines gas management but also ensures household safety. Given that LPG is highly flammable, timely detection of leaks is crucial to prevent accidents, property damage, or loss of life. By integrating monitoring, alerting, and automated booking, the proposed solution provides an effective, reliable, and secure approach to domestic gas management.
The use of medications serves three main purposes, including illness treatment, chronic disease management, disease prevention, and general health preservation. Health issues emerge because of economic disparities, which decrease patients' ability to obtain essential medicine. The system entails pharmaceutical records as a means to enhance first-aid medication accessibility through dispensaries. The proposed system offers users either automatic or manual functionality when operating it. While operating in manual mode, users can access available medications before they make their selections. The automated dispenser operates with servo motors to dispense prescribed medicines after a valid prescription card connection. Once the expense is applied, the system decreases the value stored in the card account. The system suggests the right over-the-counter drugs whenever prescription-based delivery is not possible. Public areas such as shopping centers, highways, train stations, and bus stops can benefit from having intelligent vending machines, which bring necessary medications to the general public. Such machines provide essential medicines quickly to emergency patients in areas that are distant from healthcare centers. The concept presents a solution to enhance medical emergency support while safeguarding lives through filling underserved healthcare areas.
