The problem ElectroBrain solves

Electric vehicles (EVs) are gaining traction, necessitating access to specialized charging stations. However, station availability and congestion fluctuate, posing challenges for drivers. Our innovative solution employs AI to forecast charging station traffic, aiding EV users in locating optimal stations.

Unlike centralized systems vulnerable to data manipulation, we utilize decentralization via the InterPlanetary File System (IPFS) to uphold data integrity. This safeguards against tampering, ensuring accurate information on station load and traffic, critical for informed decision-making.

Integrating push protocol notifications, our system proactively alerts drivers when their EV battery levels drop, recommending nearby charging stations based on real-time data analysis. Updates on station congestion, closures, or maintenance further enhance user experience and journey planning reliability.

Challenges we ran into

During the development of our decentralized smart grid project, we encountered several hurdles in integrating push protocols, IPFS, and algorithm development.

Push Integration: Ensuring compatibility and scalability posed significant challenges. We addressed these by meticulously following cross-platform integration documentation and seeking mentor guidance for scalable solutions.

IPFS Integration: Network latency and data availability issues arose during IPFS integration. By studying IPFS documentation and leveraging GitHub resources, we implemented caching mechanisms and optimized node selections to mitigate latency. Mentor support was instrumental in ensuring continuous data availability.

Algorithmic Complexity: Developing efficient algorithms presented its own set of obstacles. We tackled this by breaking down the problem, referring to algorithm design documentation, and consulting mentors for optimization strategies. Real-world examples on GitHub provided valuable insights.

Addressing Specific Bugs: A synchronization bug within the decentralized nodes caused particular concern. Through thorough replication of the issue, researching similar cases on forums and GitHub, and applying a discovered patch, we successfully resolved the bug.

Our approach involved a combination of thorough documentation review, mentor consultation, and leveraging community resources on GitHub, enabling us to overcome these challenges and deliver a robust solution.