Technology

When we first began building our solution, we knew that immense amounts of data and millions of data
points would come into our solution. This information would be needed to ingest, cleansed and
processed. Subsequently, we chose an architecture that allows near real-time processing of vast
amounts of data, and smart data distribution algorithms to ensure we would meet this challenge

Using cellular network gives us the flexibility to connect mobile phones together, but this has its own
limitations including network latency, which is extra critical when building a lifesaving solution. To
overcome this challenge, we took the unique and unprecedented approach of turning the mobile phone
into an independent computing unit. The prediction algorithm as well as the consumption algorithm
reside within the mobile client, allowing us to notify our users in real time of any incoming collision

Since we are working in the low resource environment of mobile phones, we are forced to be extra
careful with battery consumption and CPU usage to maintain normal phone function. Eye-Net runs in
the background using a sophisticated algorithm to monitor user behavior and dynamically adapt the
application usage based on movement patterns. This allows us to reduce battery consumption and CPU
usage providing a seamless user experience

Mobile phone GPS has an accuracy between 5-6 meters, but in some cases this accuracy is not enough
to pinpoint the exact user location on the road. We have developed our Predictions & Extrapolations
algorithm that is especially designed to tackle this challenge. Based on our researches and field trials,
our solution solves most cases of front-side collisions, and helps save lives