Journal of Sensor and Cloud Computing (e-ISSN: 3048-9199)

Smart Trolley System for Automated Billing and Enhanced Shopping Experience

2026-01-14T08:56:59+00:00

In modern retail environments, customer satisfaction and operational efficiency heavily depend on fast, accurate, and transparent billing processes. Conventional supermarket billing systems rely on manual barcode scanning and cashier-assisted checkouts, which often result in long queues, especially during peak hours. These delays increase customer frustration, staff workload, and the likelihood of human errors such as missed scans or incorrect billing. To address these challenges, this research proposes a Smart Trolley System that enables automated billing directly at the shopping trolley, eliminating the need for traditional checkout counters. The system utilizes IoT and AI technologies to reduce manual intervention and improve the overall shopping experience. Each trolley is equipped with RFID and barcode sensors controlled by an ESP32 microcontroller, enabling automatic detection of products when they are added or removed. RFID technology enables faster, contactless identification compared to conventional barcode systems. Product details, such as name, price, and quantity, are transmitted in real time to a cloud-based MongoDB database, ensuring accurate and synchronised billing. A MERN stack web application provides a user interface for customers to monitor live cart details and expenses, while administrators can manage inventory and analyze sales data. Additionally, the system integrates a Generative AI assistant powered by Google Gemini, offering personalized recommendations, recipe suggestions, and basic navigation assistance. Experimental results show a 70% reduction in checkout time and improved billing accuracy. Overall, the Smart Trolley System enhances efficiency, transparency, and customer satisfaction, making it a promising solution for next-generation smart retail environments.

A Real-Time GPS-Based Bus Tracking and Passenger Information System for Smart Urban Public Transportation

2026-04-10T11:44:31+00:00

Urban public transportation plays an important role in city mobility, but it often struggles with irregular bus timings and a lack of real-time updates. Many mid-sized cities still depend on fixed schedules and manual coordination, which reduces efficiency. With the widespread use of smartphones, GPS, and cloud technology, there is a practical opportunity to improve transport monitoring through software-based solutions. This paper presents the design and development of a real-time GPS-based bus tracking and passenger information system. The system gathers live bus location data through a mobile app used by drivers and sends it to a cloud server. Passengers can check bus locations, routes, and estimated arrival times through a simple mobile interface connected to digital maps. By avoiding dedicated GPS hardware, the system lowers costs and allows easy expansion. The study explains the system architecture, implementation process, and its practical use for passengers and transport authorities. The proposed solution improves operational visibility and supports better transport management, while also preparing the system for future smart city integration.

AI-based Helmet Detection System for Enhanced Road Safety

2026-04-10T12:18:36+00:00

This study presents an innovative approach to motorcycle helmet violation detection by leveraging the power of artificial intelligence (AI) and deep learning techniques. The global increase in traffic accidents, especially those involving motorcycles, underscores the urgent need for effective helmet use enforcement. Traditional methods of monitoring helmet compliance are often labor-intensive and inefficient. This research explores the use of AI for automatic helmet detection, addressing the limitations of manual monitoring and aiming to improve road safety outcomes. The study builds upon previous research in the field, acknowledging the effectiveness of deep learning in various computer vision tasks, including object detection. Drawing on these advancements, the paper focuses on developing an accurate, efficient, and real-time system capable of identifying motorcycle riders who are not wearing helmets. This research holds significant implications for policymakers, traffic enforcement agencies, and road safety advocates, offering a promising solution for automated helmet violation detection and promoting safer roads for all.

AI News Aggregator: An Intelligent Real-Time News Curation and Verification System

2026-04-16T13:57:33+00:00

To address these challenges, this paper presents an AI News Aggregator, an intelligent system designed to deliver real-time global news that is curated, analyzed, and verified using Artificial Intelligence. The platform automatically collects news from various trusted sources, processes the data using Natural Language Processing (NLP), and filters out duplicate, irrelevant, or misleading content. With the rapid growth of digital media, the volume of news content available online has increased exponentially. While this ensures accessibility, it also creates major challenges such as information overload, redundancy, and the spread of misinformation. Users often struggle to identify reliable and relevant news from multiple sources, leading to confusion and misinterpretation. The system incorporates machine learning algorithms to classify news, detect fake information, and provide sentiment analysis. Additionally, it offers personalized recommendations based on user preferences and reading behavior, enhancing user experience. The frontend of the application is developed using modern web technologies such as HTML, CSS, and JavaScript (or React.js), while backend processing is handled using frameworks like Spring Boot or Python-based services. The system ensures secure data handling, fast processing, and real-time updates. The system introduces a structured monitoring mechanism through a dynamic dashboard that provides real-time visibility of event progress, completed tasks, and pending activities. This improves workflow transparency and enables users to maintain better control over timelines and resource allocation. By presenting organized event data in a clear format, JoyNest enhances planning accountability and reduces the likelihood of last-minute scheduling conflicts. By combining intelligent automation with advanced analytics, the AI News Aggregator improves the reliability, accessibility, and quality of news consumption while reducing misinformation and digital noise.

LibConnect: A Smart Online Library Management System for Colleges

2026-04-16T14:18:43+00:00

Library management systems (LMS) play a crucial role in modern educational institutions by ensuring efficient organization, tracking, and accessibility of resources. However, many libraries continue to depend on manual or partially digitized systems, which often lead to operational delays, data inconsistencies, and difficulties in managing records. This study introduces LibConnect, a web-based library management system developed to address these challenges and enhance overall efficiency. The system leverages cloud computing technologies to provide real-time data synchronization and seamless resource management. It incorporates role-based access control for librarians, faculty members, and students, ensuring secure and structured system usage. A user-friendly interface featuring dashboards and real-time notifications significantly improves user interaction and accessibility. The implementation of LibConnect reduces manual effort, minimizes human errors, and streamlines book issuance and return processes. Furthermore, the system is designed to be scalable, allowing future integration of advanced features such as AI-driven recommendations, plagiarism detection, and smart campus connectivity.