Insights: The data storage behind smart and safe cities
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Insights: The data storage behind smart and safe cities

Insights: The data storage behind smart and safe cities

While adoption of digital technologies have brought a new era of progress, it can sometimes be hard for the untrained eye to recognise innovations in every-day life

Gulf Business
Data storage

Whilst technological breakthroughs and the adoption of digital technologies have brought a new era of progress, it can sometimes be hard for the untrained eye to recognise cutting-edge innovations in every-day life. However, smart cities are a key area where new technologies, such as smart video, artificial intelligence (AI), machine learning (ML) and the internet of things (IoT) directly and visibly, raise living standards, while hiding in the background.

The UAE has championed smart cities, creating innovative hubs throughout the emirates that support intelligent integration into everyday life. With a government objective to strengthen the country’s position as a global hub by advancing innovation and future technologies, the country is poised to be an epicentre for smart city planning. With the conclusion of Expo 2020, the world was able to see some of these ambitions for the first time. Poised to become a hub for futuristic, innovative and intelligent data, the UAE is building upon an era in which AI, IoT and data analytics are the stepping stones to a successful digital transformation. These intensive activities mean technology needs to evolve to meet the demands of ambitious smart city developments and create strong security practices to keep these cities safe. This is where data infrastructure will play a crucial role in the UAE and beyond.

Smart cities use information and communication technologies to improve operational efficiency, share information with the public and provide a better quality of local authority services. For example, advancements in IoT technologies have enabled connected public transportation systems, which leverage real-time monitoring capabilities, as well as tracking the locations and routes of public vehicles. Not only does this speed up service times and reduce traffic congestion, it also cuts waiting times for passengers and keeps them informed.

There’s also an important security element to smart cities. Smart video cameras utilise AI algorithms and deep learning (DL) to analyse visual data in real-time and can dispatch orders from a hub to AI-powered devices faster than a human can process. Going further than just providing data, smart technologies can actually enable the devices to deploy intelligent insights. For example, cameras and AI-analysed traffic patterns can adjust traffic lights accordingly to improve vehicle flow, reduce congestion and pollution, and, more crucially, increase pedestrian safety.

Smart video is also being deployed in connected cities to deliver critical assistance to help reduce crime. Business owners, for example, need security cameras to help protect their property, reduce shoplifting, and monitor employee or customer incidents. On a larger scale, real-time video analysis is also capable of identifying and differentiating between objects, for example distinguishing humans from animals, and alerting the relevant people or systems if they are in a prohibited location or place.

The process behind smart video
Smart cameras need to ‘learn’ to recognise objects and actions and classify the identified actions into categories such as anomalous or normal. This is where AI and DL are needed for training and learning; DL needs to analyse a tremendous amount of data to be highly accurate. The development of higher video resolutions, such as 4K, is key here, enabling CCTV cameras to capture more data in high quality and from various angles, making analysis easier and empowering the smart video future.

The smart video sector is going through a transitional phase for recording video at scale: it has moved away from recording raw data from a standard camera to carrying out analysis on the AI-enabled camera itself. In the past, the data analysis was only possible at a centralised location, such as a data centre; however, the rise of on-board AI chips used in smart city technology allows the analytical load to be distributed. The ability to distribute the work is crucial when working at the scale of a smart city, enabling the data to be processed more quickly at the endpoints.

As AI and 4K rise in adoption on smart video cameras, higher video resolutions are driving the demand for more data to be stored on-camera. There are many more types of cameras being used today, such as body cameras, dashboard cameras and new IoT devices and sensors. Video data is so rich nowadays, you can analyse it and deduce a lot of valuable information in real-time, instead of post-event.

The role of storage
As a result, storage is critical to the evolution and efficient working of smart video systems. Smart video architectures require innovative data storage technologies, which deliver needed flexibility, performance, capacity and reliability. Robust on-board storage must be specially designed to meet the needs arising from multi-streaming devices, on-device deep learning systems and AI-training solutions. Data storage solutions have evolved to provide high data transfer and write speeds, as well as the capacity to ensure world-class video capture.

Storage-enabled AI
Having the right workload and performance is important in ensuring that drives can keep up with the demands of AI functionality, including pattern matching and object recognition. By combining video stream recording optimisation with top-tier durability and capacity, smart video solutions and AI-analytics have the necessary foundations in place to operate at optimum levels for thousands of hours.

NVRs and VMS (video management systems) are getting smarter. Deep-learning algorithms go beyond simple movement detection to enable advanced capabilities to drive improvements in many industries and settings, including retail, smart cities and entertainment to name a few. AI-enabled VMS are being architected for new graphic processing unit (GPU) and central procession units (CPU) to improve overall deep-learning capability, and speed algorithms related to object identification. NVRs with this deep learning require greater data storage capacity and more sophisticated processing, versus individual cameras, enabling them to perform more advanced analytics, such as finding a particular image from weeks or months of stored video, or creation of traffic heat maps from hours of retail surveillance video.

Behind the innovation
Smart video plays a vital role in many public safety use cases including safe driving, retail, fleet management and home security. However, the success of smart video relies upon a robust and resilient storage architecture that can effectively keep up with the heavy workloads. As smart video use cases proliferate throughout security and logistics landscapes, the hidden data storage complexities shouldn’t be forgotten. As data demands for video continue to evolve, so must the innovations to meet industry challenges.

Khwaja Saifuddin, is the senior sales director – Middle East at Western Digital

Read: DIC, Khazna Data Centers announce two data storage facilities in Dubai

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