Summary
- Modeled EV charging infrastructure as a wireless sensor network with distributed nodes.
- Implemented using C, MPI, and OpenMP for parallel and distributed coordination.
- Base station dynamically redirected demand when nodes or areas reached full capacity.
- Demonstrated load balancing and resource allocation through simulation logs.
- Highlighted how distributed systems can support smart grid and IoT applications.
This project simulated an electric vehicle (EV) charging infrastructure using a wireless sensor network model. Each EV node represented a charging station with limited ports, able to communicate with neighboring nodes and a central base station. The simulation captured real-world challenges of capacity management, where local nodes could become fully occupied and needed to coordinate with nearby stations for load balancing.
Using C with MPI and OpenMP, I implemented a distributed system where each node reported its state, exchanged availability with its neighbors, and escalated “area full” alerts to the base station. The base station then calculated the nearest available charging node using Euclidean distance and directed traffic accordingly. This iterative process mimicked how a smart EV grid might dynamically reallocate demand under constraints, highlighting the power of parallel and distributed programming in infrastructure modeling.
The outcome was a functioning simulation that produced detailed execution logs, showing nodes filling their ports, detecting saturation, and successfully rerouting demand to available neighbors or the nearest open station. By combining parallel processing and communication protocols, the system achieved robust coordination across dozens of simulated charging nodes, demonstrating how computational methods can model and optimize real-world sustainability challenges.
