Enhancing the Power and Speed of Edge Computing
Global smart city technology investments are on track to hit roughly $6 trillion by 2030 as state and local government leaders take on public safety, traffic congestion and environmental issues while trying to reduce costs and promote local economic competitiveness. The key to satisfying these requirements lies in combining multiple advanced technologies such as the Internet of Things (IoT), AI and machine learning (ML) with the benefits of edge computing.
Edge computing is a growing industry that brings computing, storage and connectivity capabilities closer to the user. This process leads to lower latency, higher bandwidth and better security. Edge computing also positions software applications and data sets at locations where it makes more sense from a business, regulatory or performance standpoint.
All these benefits are magnified, and latency is further slashed when connectivity is upgraded to lightning-fast 5G networks that are increasingly available in urban areas. Ultimately, as IoT sensors, dynamic AI/ML applications and other advanced tooling are set up to run on these advanced edge computing architectures, the combined capabilities turn into something greater than the sum of their parts: multi-access edge computing.
MEC Drives Transformative Capabilities
A MEC deployment can power a smart city with all the flexibility, computing, sandboxing and multitasking capabilities one would expect from the cloud, but it is happening on the edge. Think of MEC as an industry cloud at the edge capable of doing more things, and more things at once, through a foundation of virtualized, cloud-native and 5G-ready applications and infrastructure.
Urban applications for MEC range from automated maritime vessel berthing and airport baggage handling to real-time situational awareness around public safety, road infrastructure and railway patterns. MEC also can boost operational efficiency at local sports stadiums and improve parking and safety on university campuses. The most advanced MEC architectures combine data from multiple systems for predictive and prescriptive analytics, rendering a more complete and proactive picture of safety, sustainability and other smart city priorities.
For example, a MEC deployment can more quickly and securely combine Department of Transportation data with police and ambulance response logs to analyze how certain traffic patterns may relate to increased vehicle or pedestrian accident rates. The resulting insights can spur proactive steps, such as changing traffic or signaling patterns, to reduce the risk of accidents and save lives. Indeed, multiple studies suggest that such MEC-driven programs are already reducing the proportion of motor vehicle accidents caused by human error.