Israeli startup Vorpal specializes in tracking and, ideally, preventing safety incidents where drones fly too close to manned aircrafts. Their drone detection and geolocation tracking solution VigilAir uses a geographically distributed network of sensors that scans relevant frequencies to identify drone transmissions. This allows them to identify and track drones and their operators in near real time.
VigilAir has already been useful to commercial drone monitoring, airports, and public safety law enforcement agencies across the globe. But the FAA expects the number of drones in our airspace to increase as much as threefold by 2023 as commercial drone operations become more and more prevalent.
To prepare for this increase, Vorpal is collaborating with AT&T and Microsoft to test how edge computing could equip them with the ability to track thousands of drones at any given time.
The Answer’s in the CloudsToday, the ability to pinpoint the location of drones in near real time is key to VigilAir’s success. To ensure VigilAir can seamlessly maintain those same capabilities while monitoring thousands of drones, they’ll need robust, low-latency and high-throughput network capabilities right on the device, at the intelligent edge. So Vorpal is looking to the clouds with AT&T and Microsoft.
Each of Vorpal’s sensors is equipped with computing hardware that processes their location-tracking software. The more drones in the sky, the more compute power needed to handle all that data. What if we could pull much of that compute out of the sensors and put it in the network?
That’s where network edge compute (NEC) comes in. Together with Microsoft, the AT&T Foundry is testing how to bring network edge compute (NEC) capabilities into AT&T’s network with Microsoft Azure’s IoT and AI services and Azure Stack hybrid technology. By deploying Microsoft’s advanced cloud services closer to the edge of the network – in this case, within the geographic locations where customers like Vorpal need them – NEC could allow businesses to access low-latency network compute at a fraction of the cost of traditional, embedded processing.