Modernizing and Securing Smart City Infrastructure Using Real-Time Data


By Jason Bonoan, Global Product Marketing Manager, Seagate Technology

In 2018, the global datasphere netted out at 33 zettabytes and was hypothesized to reach 175 zettabytes by 2025, according to IDC. Our connection and reliance on data is growing exponentially, fueled by the maturation of cognitive systems—such as machine learning, natural language processing, and AI. Layer in the Internet of Things (IoT) and the advent of 5G to move information along at rapid speed, and we are seeing an explosion of vital, unmined information that can be analyzed for proactive decision-making. Safe and smart cities can only be – well, safe and smart – if they have access to real-time information, allowing for a data-driven approach to public safety, healthy living, mobility, and economic growth. 

There are countless applications for real-time data to forge more efficient operations within today’s modern city. The topline goal is to analyze data aggregated by IoT devices, sensors, and security solutions and use it to better optimize operations, provide cost-efficiencies and ensure human satisfaction and safety.

Keeping Urban Centers Secure

One of the most important uses of real-time data in smart and safe cities is within surveillance systems, providing a level of public security that was previously unimaginable.

Take Shanghai, China’s most populated city. It’s home to approximately 24 million residents, as well as a thriving business and tourist community. When Shanghai hosted the World Exposition in 2010, the city welcomed over 70 million visitors. To improve public safety and security, Shanghai implemented a safe city surveillance system that utilized 15,000 cameras to monitor nearly 2,500 square miles. To ensure a sound recording process that would provide continuous coverage and monitor and detect crime, Shanghai pursued a partnership with Rasilient Systems to develop a reliable storage system. The result was the ability to measure a massive amount of obtained data – six petabytes – and use it to provide proper protection mechanisms based on the information at hand.

Real-Time Data for Other Smart City Applications

Beyond surveillance, smart cameras, IoT sensors, and edge computing devices with AI are being deployed in smart cities to equip businesses and citizens with data that can enhance the urban experience.

Take, for instance, the transportation and mobility sector, which is plagued by traffic congestion and outdated infrastructure. To combat this, the city of Atlanta developed an ecosystem of smart sensors as part of the North Avenue Smart Corridor initiative, which deployed hundreds of IoT sensors at over two dozen crowded intersections. HD cameras with video analytics were used to enable adaptive signal timing, where signals adjust to traffic patterns. This resulted in a significant reduction of accidents by one-quarter. Smart city technology also lays the groundwork for autonomous vehicles and car-to-car communication, helping pedestrians safely cross intersections and avoid a dangerous situation. Sensors help vehicles become equipped to make the same split-second, risky decisions as human drivers – whether merging onto a highway, changing lanes or discerning between living beings and objects.

Other improvement areas include healthcare, where IoT sensors are used to preemptively monitor patient health and provide remote care. There’s also functionality within the economic development, housing and community engagement sectors – for instance, smart energy meters to provide cost savings and lifestyle changes. 

AI in Security Storage

As seen in the Shanghai example, the increased use of AI systems in security has implemented an industry-wide shift in recording and storage technologies. Standard surveillance systems that primarily recorded footage were typically write-only applications. Today, surveillance systems with AI typically have mixed read/write capabilities.

As data proliferates and becomes increasingly critical to daily life and premeditative decision-making, the way we preserve and protect data must also evolve. Therefore, cloud storage alone is not sufficient – we must be thinking holistically and look at an ‘IT 4.0’ environment that has a dual-approach, relying on storage infrastructure that utilizes both edge and cloud computing for data analysis. AI-enabled servers and appliances on the edge allow for data processing to occur on-site, as they are closer to camera and sensor endpoints.

Smart cities should consider three primary best practices for effectively implementing surveillance AI:

  1. Deploy high-performing storage solutions on-site before transferring the data to the cloud for deep learning and archival to support the influx of video and metadata and improve the delivery of quick insights.
  2. Replace standard hard drives with surveillance-optimized hard drives that can support multiple high-definition cameras, store the thousands of hours of video, and accommodate numerous AI events simultaneously. We need to support our 24/7, always-on data world.
  3. Employ monitoring software that can offer real-time insights into the video storage system’s health, as well as to detect any issues. This will help mitigate potential data failure or cybersecurity concerns.

Future-Proofing Technological Infrastructure

Gartner predicted that global IT spending will reach $3.9 trillion this year. As the industry continues to progress, it’s imperative to take a holistic view at transformation, reduce dependence on outdated physical security and IT infrastructure, and embrace strategies that optimize data processing, management, and preservation.

Jason Bonoan has over 10 years of experience in IT storage and is the Global NAS and Surveillance product marketing manager at Seagate Technology.


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