Smart cities is a broad industry category for 5G use cases, within this is surveillance. Surveillance cameras, including body-worn cameras, can transmit real-time Ultra-High-Definition and 360-degree video streams, over the 5G network. Specifically, these video streams can be sent to a control room that monitors busy public places and critical infrastructure.
Thousands of Internet of Things sensors, that connect to the 5G network, can be placed across major infrastructure, such as bridges. In turn, these sensors constantly monitor the vibrations caused by vehicles and trains that cross those bridges every day. Vibrations detected that are deemed abnormal, could be the first sign that part of the bridge is not performing as it should, and an inspection crew would be dispatched.
Edge compute allows application developers and enterprises to enable large-scale, latency-sensitive use cases at the edge. Two important examples of these initial edge compute use cases include autonomous vehicles and the Internet of Things.
Edge compute delivers computing resources closer to where the needs are. Instead of housing these critical resources in a large cloud data center that could be hundreds, or even thousands, of miles away from where the data will ultimately be delivered, this new architecture puts it all at the edge of the network. In turn, edge compute is propelling the growth of autonomous vehicles and the Internet of Things.
Autonomous driving can be classified into six different levels, from traditional vehicles being Level 0 to fully autonomous vehicles being Level 5. Additionally, within this classification, Advanced Driver-Assistance Systems (ADAS) start at Level 1 and extend to Level 4. ADAS offers semi-autonomous features to the vehicle and is the first step towards fully autonomous vehicles.
At present, vehicles are not fully autonomous. However, as vehicles increasingly shift from Level 1 to Level 5 autonomy, more decision-making capability will be given to the vehicle. In turn, the data consumed by the vehicle will increase, as the level of autonomy increases, because it has to make more decisions. Specifically, Level 1 vehicles consume 3 gigabytes per hour, whereas Level 5 vehicles consume 50 gigabytes per hour. Indeed, Level 5 vehicles consume almost 17 times more data than Level 1 vehicles.
Examples of the data consumption by autonomous vehicles includes i) analyzing traffic patterns, ii) observing road conditions and iii) helping the driver make decisions. For example, Cruise, which is majority-owned by General Motors is producing very sophisticated autonomous vehicles. Furthermore, Cruise plans to begin testing these vehicles in San Francisco in late 2020.
In effect, autonomous vehicles are driving computers, functioning like a mini-data center. Autonomous vehicles are constantly aggregating, creating, sending, and receiving data. Numerous applications run on a vehicle and thus, significant Internet of Things information is being sent from a diagnostics perspective.
