If industry and innovation defined the late 20th century, the beginning of the 21st would be defined by data. Around the world, cities everywhere from London and Singapore to Seat Pleasant, Maryland, embrace broad, data-driven initiatives to transform today’s urban centers into the Smart Cities of tomorrow. This involves universalizing the latest 5G and fiber optic networks, expanding digital literacy and infrastructure, and more. However, at the foundations of the globe’s most intelligent Smart Cities are municipal Internets of Things (IoT). 

IoT, by definition, is a network of smart sensors designed to generate and integrate massive amounts of data to serve civic purposes. These diverse ecosystems of devices—superpowered by artificial intelligence (AI)—are built to sift through massive influxes of data in real-time to generate actionable insights for municipal managers. With this data, cities can keep residents safer with more responsive security measures, help traffic flow smoother, manage waste more efficiently, and imagine new ways to respond to citywide emergencies.  

Video Surveillance at the Core 

Video surveillance cameras are among the primary devices aggregating data and delivering key insights for Smart City functions, such as detecting car accidents at major intersections, hazards on the road, and suspicious packages on campus. These insights help traffic officers, waste management officers, and school security directors make real-time decisions to mitigate threats and improve safety.  

However, these data-powered applications require massive amounts of video and data to be filtered, analyzed, and stored. Without a doubt, data management is one of the most essential facets of a Smart City.  

The Right Storage Architecture 

Without strategically designed storage architecture, Smart Cities will not function properly. Without the right storage framework, municipal managers experience latency and dropped frames from their video surveillance solutions. When video is lost, analytics do not perform, which hinders the delivery of real-time intelligence. Poor storage infrastructure design also leads municipal customers to have high capital expenditures and operations expenses anytime they need to add additional storage to support more 4K surveillance cameras, video analytic applications, and increased retention times.   

To mitigate these pain points, many Smart Cities rely on architecture that uses AI-enabled NVRs at the edge as well as IP-SAN servers in the centralized backend or cloud. Specifically, on-site NVRs and servers record video from surveillance cameras and initially process video and metadata to provide immediate alerts, such as identifying a stalled car on the road or detecting an intruder at the perimeter. These devices then hand over the data to the cloud for deep learning and long-term retention. This approach improves recording throughput, streaming, analytical performance and archival. 

Overall Impact  

Cities that rely on pen and paper use information to explain events that have already passed.  On the other hand, Smart Cities leverage data to design, innovate, and administer resources and services proactively, making cities smarter and safer for businesses and residents alike. As sensor, AI, and video technologies evolve, purpose-built, scalable, and efficient data storage design will only become more important.