According to UN estimates, 68% of the world’s population will live in urban areas by 2050, up from 55% today. When we factor in the overall global population growth, it is clear that cities must transform to effectively accommodate the growing population.
With this in mind, there is recognition of the importance of making cities smarter, more sustainable, and more efficient. In 2015, the government of India launched the Smart Cities Mission with the aim of improving the quality of life in 100 cities by providing efficient services, robust infrastructure, and sustainable solutions.
Smart cities are urban environments that can offer the promise of better infrastructure, public services and improved overall quality of life for residents by leveraging interconnected technologies. In addition to internet of things (IoT) sensors and data analytics, artificial intelligence (AI) plays an increasingly central role in driving these smart cities, especially as they grow more complex and densely populated.
AI can help streamline the management of real-time operations such as traffic control, emergency response, energy distribution, and public safety. However, since these functions demand instantaneous, fail-proof decision-making, even milliseconds matter. Therefore, highly responsive, and accessible storage becomes critical to support this.
Relying solely on cloud computing can introduce latency, connectivity risks, and potential bottlenecks. Edge storage can help AI systems to operate with speed, reliability, and autonomy, enabling smarter, faster, and more resilient urban environments. Since it allows for storage and processing of data locally at the edge of the network, close to where it is generated, it supports fast, decentralised processing and decision-making.
{{/usCountry}}Relying solely on cloud computing can introduce latency, connectivity risks, and potential bottlenecks. Edge storage can help AI systems to operate with speed, reliability, and autonomy, enabling smarter, faster, and more resilient urban environments. Since it allows for storage and processing of data locally at the edge of the network, close to where it is generated, it supports fast, decentralised processing and decision-making.
{{/usCountry}}In addition to reducing latency, edge storage solutions help keep bandwidth costs in check by filtering and processing data locally. The other, equally critical aspect, is data security. Since edge storage allows sensitive or mission-critical information can be kept on-site, it reduces exposure to potential breaches or connectivity failures.
Edge storage supports several applications in smart cities, including:
- Autonomous public transport: One of the most visible and impactful applications of AI in smart cities can be in the area of autonomous public transportation. In this scenario, Buses and shuttles will continuously process vast amounts of data to navigate dynamic urban environments in real-time. This will include data from cameras, LiDAR, GPS, and other sensors. They must also detect pedestrians, interpret traffic signals, respond to road conditions, and coordinate with other vehicles. Edge storage will allow for real-time AI inference directly on the vehicle. For example, critical decisions such as braking for a pedestrian or rerouting due to an obstacle can be made instantly. Sending this data to the cloud for processing would introduce unacceptable delays and pose the risk of potential failures due to network interruptions. The SANDISK® automotive e.MMC™ and UFS cards are designed for automotive, prioritising superior reliability.
- Intelligent surveillance: AI-driven surveillance systems use computer vision and machine learning to identify suspicious behaviour, unattended objects, crowd density, and flag potential security incidents. This not only enhances situational awareness but also enables faster responses from authorities. Through real-time threat detection and behavioural analysis in public spaces such as transit hubs, parks, and busy intersections, these can transform public safety.
Since this requires constant access to high-resolution video feeds and metadata, on-site edge storage is essential. This ensures minimal latency for time-sensitive alerts and enables real-time analytics, such as facial recognition or licence plate detection. Such use cases demand high endurance and reliable storage.
Overall, embracing resilient edge infrastructure is critical to ensure scalable smart city innovation to power the cities of the future.
This article is authored by Subind Kumar, vice president & country manager, Sandisk.