DriveWise
DriveWise: Smart Vehicle Maintenance Application
DriveWise is an innovative smart vehicle maintenance application designed to simplify and enhance the vehicle upkeep experience for everyday users. The core idea of DriveWise is to create a seamless connection between vehicle diagnostics, users, and trusted service providers using a technology-driven approach. The application integrates with vehicle sensors to monitor performance and health in real-time, decoding trouble codes (like OBD-II) to detect potential issues before they escalate.
One of DriveWise’s key features is its intelligent alert system, which notifies users immediately when a problem is detected, along with a clear explanation and potential causes. To reduce stress and time spent on repairs, the app automatically matches users with nearby, verified service providers who specialize in the identified issue. Users can view repair shop ratings, estimated costs, and even schedule appointments directly from the app.
DriveWise also keeps a digital maintenance history, helping users track past repairs, routine services, and upcoming maintenance schedules. By leveraging data analytics and predictive maintenance, the app aims to reduce unexpected breakdowns and extend vehicle lifespan.
The project is developed by a team of six undergraduate students and focuses on providing a user-friendly, efficient, and reliable solution for vehicle maintenance. DriveWise stands out for its combination of real-time diagnostics, smart recommendations, and integration with local mechanics — making car maintenance smarter, faster, and stress-free.
AuraMirror
Aura Mirror is a smart mirror project designed to seamlessly integrate technology into everyday routines, offering a personalized and informative user experience. Developed using a Raspberry Pi 4, a display screen, and a two-way mirror, it functions as both a conventional mirror and a digital display for essential daily information such as time, date, weather updates, calendar events, news headlines, and to-do lists.
The mirror supports facial recognition to deliver tailored content for each user. It also includes entertainment features such as music and video playback. A mobile application developed in Kotlin listens to notifications from the user’s smartphone and forwards them to the mirror. The backend is developed using Spring Boot, which handles data processing and communication between the mobile app and the mirror.
The front end is built using React and React Native, ensuring a responsive and user-friendly interface. Aura Mirror combines IoT components, modern web technologies, and mobile integration to create a functional and visually appealing smart device that enhances everyday living with convenience and personalization.
Cradlers
The Smart Baby Cradle is an innovative full-stack project that integrates IoT technology, web development, and mobile application design to enhance infant care and parental convenience. This end-to-end system is designed to monitor a baby’s environment and ensure their safety and comfort through intelligent automation and real-time communication.
At the heart of the system is an IoT-enabled cradle equipped with sensors that detect humidity, motion, and other environmental factors. These readings are processed to assess the baby’s comfort level and identify unusual conditions, such as prolonged stillness or excess humidity, which could indicate a potential risk. When such events are detected, the system can trigger alerts or adjust cradle functions (e.g., vibration or music playback) to soothe the baby.
Complementing the hardware is a responsive website built with React, TypeScript, and Tailwind CSS. The website serves as an educational and promotional platform, offering detailed insights into the cradle’s features, technical architecture, and user benefits. It also provides instructions for setup and usage, making the technology accessible to non-technical users.
The project also features a mobile application that connects with the cradle in real-time. Parents can receive instant alerts, track environmental metrics, and remotely monitor cradle activity. This seamless connectivity ensures peace of mind and empowers parents with actionable data to make informed caregiving decisions.
Overall, the Smart Baby Cradle project demonstrates a practical and impactful application of IoT in everyday life, combining hardware innovation with software usability to solve real-world parenting challenges.
TankTrack
Across the world, especially in developing regions, septic tanks lead to frequent overflows, environmental contamination, and public health hazards. With over 44% of global wastewater discharged untreated, there is an urgent need for innovative, real-time monitoring solutions that can proactively manage sanitation infrastructure, reduce health risks, and promote sustainable urban living. To address the global challenge of unmanaged septic tank overflows, we propose a smart septic tank monitoring system that uses IoT technology to provide real-time data, predictive alerts, and weather-integrated insights. By leveraging sensors, mobile applications, and cloud-based analytics, this solution empowers users to take preventive action, enhances maintenance efficiency, and supports safer sanitation practices, particularly in underserved and flood-prone communities worldwide.
Sparse-MDI: Adaptive Sparsification for Communication-Efficient Model Distributed Inference in The Edge
The rapid growth of edge computing applications, such as autonomous vehicles and
IoT systems, necessitates real-time, low-latency inference of deep neural networks in
environments with limited computational resources. Most existing distributed inference methods face significant communication overheads when handling large, complex models, especially in resource-constrained edge networks.
This research explores an adaptive sparsification technique by introducing a novel peer-
2-peer (P2P) model distributed inference (MDI) framework to address these communication
challenges. Adjusting the sparsity of activations based on network and device conditions can be adopted by any peer-2-peer MDI framework to improve communication overhead. By dynamically adjusting data loads, adaptive sparsification enhances performance, reduces latency, and preserves model accuracy.