Developed with a MERN stack for the final project of 2CP with my team a full webapp that helps adminitsrate the process of attributing housing to the staff of the school (ESI) by automating and scheduling tasks tat are usually done manually, that helped increasing the efficacity of the process.

Personal

Developed energy-efficient solutions for WSNs and IoT using advanced ML/DL techniques. Tasks included model design, training, deployment, and research documentation.

This project focused on addressing energy consumption challenges in Wireless Sensor Networks (WSNs) and IoT environments by integrating advanced Machine Learning (ML) and Deep Learning (DL) solutions. My contributions included:

1.  **Research and Review:** Conducted a thorough study of existing energy-efficient protocols and frameworks in WSNs, including duty-cycling, clustering, and sleep/wakeup scheduling techniques.

2.  **Model Design:** Designed a custom ML/DL architecture tailored to reduce sensor energy usage while maintaining data integrity. I utilized frameworks such as TensorFlow and PyTorch for this purpose.

3.  **Implementation and Debugging:** Developed and debugged the model code, ensuring compatibility with sensor network datasets. This involved preprocessing temporal and spatial data and optimizing the model for scalability.

4.  **Training and Deployment:** Trained the model on real-world sensor data, analyzing its performance to fine-tune hyperparameters. Deployed the model in a simulated WSN environment to evaluate energy savings.

5.  **Documentation and Publication:** Authored a research paper summarizing the findings and a detailed thesis highlighting the project’s technical and theoretical contributions.

The project demonstrated the potential of ML/DL technologies to enhance WSN sustainability by extending sensor lifespan, reducing maintenance costs, and ensuring reliable data collection.

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Energy Saving in Wireless Sensor Networks

 Aweb application connecting students and teachers for managing lost-and-found items and facilitating peer-to-peer sales on campus.

### Developed eFind, a platform that facilitates lost-and-found management and peer-to-peer sales for university students, providing an efficient and interactive user experience.Platform Design:Created a responsive frontend using React.js, offering an intuitive interface for posting and searching items.Built a scalable backend with Node.js, ensuring efficient handling of data and user interactions.Real-Time Communication: Integrated WebSocket to enable instant notifications and real-time communication between users, significantly improving responsiveness and engagement.Deployment and Impact: Launched the platform on a university campus, achieving a 50% increase in user engagement and facilitating hundreds of successful exchanges within the first month.This project exemplifies my ability to design and deploy full-stack applications with a focus on user interaction and real-world impact.



