Traffic Sense

Inspiration I originally entered this hackathon to work on an RF device that could monitor anomalies like human stampedes in crowded stadiums. However, Dedrone showed me how my technology could also be used for drone detection. Since I still wanted to focus on anomaly detection for stampedes, I sought a middle ground. Both MAC spoofing for stampedes and drone detection pose challenges for devices, so I decided to spend my time building and optimizing an algorithm to defeat MAC spoofers by tracing and comparing RSSI patterns. What it does Traffic Sense aims to detect anomalies such as human stampedes in crowded environments and unauthorized drones by analyzing RF signals. The algorithm I developed is designed to defeat MAC spoofing by tracing and comparing RSSI patterns, enabling more accurate detection and identification of devices. How I built it I spent my time during the hackathon optimizing an algorithm that can effectively combat MAC spoofing. The algorithm works by tracing and comparing RSSI patterns to identify devices accurately, even when they attempt to spoof their MAC addresses. Challenges I ran into One of the main challenges I encountered was finding a middle ground between stampede detection and drone detection, as both MAC spoofing and drone detection can be problematic for the device. Additionally, optimizing the algorithm to effectively trace and compare RSSI patterns while maintaining efficiency was challenging. Accomplishments that I'm proud of I successfully developed an optimized algorithm that can defeat MAC spoofing by tracing and comparing RSSI patterns. This achievement is significant as it enhances the accuracy and reliability of my anomaly detection system, making it more effective in detecting both human stampedes and unauthorized drones. What I learned Through this project, I gained valuable insights into the challenges of MAC spoofing and the importance of developing robust algorithms to combat it. I also learned about the potential applications of my technology beyond my initial focus on stampede detection, such as in the field of drone detection. What's next for Traffic Sense Moving forward, I plan to continue refining and improving my algorithm to enhance its performance and accuracy. I will also explore additional applications for my technology, such as integrating it with existing security systems or developing standalone devices for anomaly detection. Collaborating with organizations like Dedrone could help me further develop and deploy my solution in real-world scenarios.

Built With

• c++
• javascript