Algosense Arena

Inspiration

During real-world disasters like floods, wildfires, and earthquakes, response time is critical. We realized that choosing the right algorithm for routing and decision-making can drastically impact rescue efficiency. However, most systems use a one-size-fits-all approach. AlgoSense Arena was inspired by the need for a smarter system that dynamically selects the best algorithm based on the situation.

What it does

AlgoSense Arena is an intelligent simulation platform that models different disaster scenarios and automatically selects the most suitable algorithm for each case. It evaluates conditions like terrain, obstacles, and risk factors, then dynamically switches between algorithms such as pathfinding and search strategies to generate optimal routes for emergency response.

How we built it

We built AlgoSense Arena using a modular architecture with vanilla JavaScript, HTML, and CCS for the frontend simulation engine. Each algorithm was implemented from scratch and integrated into a central decision layer that evaluates scenario parameters in real time. The system is event-driven, allowing seamless switching between disaster simulations like floods, wildfires and earthquakes.

Challenges we ran into

One of the biggest challenges was ensuring smooth switching between algorithms without breaking the simulation state. Designing a fair comparison layer between different algorithms was also complex since each operates under different assumptions. We also faced performance tuning issues while rending dynamic grid-based environments in real time.

Accomplishments that we're proud of

We successfully built a functional multi-algorithm simulation engine without relying on external libraries. The system can dynamically choose and visualize the best algorithm per scenario. We're especially proud of the real-time visualization and the clean separation between scenario logic and algorithm logic.

What we learned

We learned how different algorithms behave under varying constraints and how critical system design is when combining multiple decision-making strategies. We also gained deeper experience in simulation architecture, event-driven programming, and performance optimization in the browser.

What's next for Algosense Arena

Next, we plan to introduce AI-based scenario prediction to pre-select algorithms even before execution. We also aim to add multiplayer "command centre mode, and real-word map integration" (like GIS layers) and extend support for more complex disaster models with live data inputs.

Built With

• a*
• bfs
• css
• dfs
• dijkstra-style-logic
• git
• github
• html5
• javascript
• vanilla