Author | Lucía Burbano
This year marks the consolidation of a transformative concept born from the convergence of two of the most powerful technologies in recent times: the Internet of Things (IoT) and Artificial Intelligence (AI). Their integration has given rise to AIoT—the Artificial Intelligence of Things. Joining this powerful duo is a third key player: Edge Computing.
This “three-in-one” fusion ushers in a new era of possibilities, bringing the concept of the smart city closer to reality. There are already real-world applications, with more set to be implemented throughout 2025.
In addition to the interconnectivity and large-scale data collection enabled by IoT devices, increasingly advanced AI and Edge Computing now allow data processing to be decentralized and brought closer to the source—ensuring near-instant response times for these digital systems.
Urban Applications of AIoT + Edge Computing
AI remains essential in transforming cities into smart ecosystems, bringing greater sophistication to data analysis and decision-making processes.
In 2025, AI-driven predictive analytics will play a vital role in urban planning. This technology will enable cities to predict traffic congestion, energy demand, and environmental risks—most importantly, allowing for the adoption of proactive measures to prevent disruptions.
In 2025, AIoT applications will be evident in predictive infrastructure maintenance, real-time traffic management, and personalized public services.
For example, AI algorithms can analyze data from connected sensors to optimize energy distribution, minimizing waste, and promoting sustainability. This synergy enhances the city’s ability to proactively adapt to residents’ needs in real time.
These are essential elements for a cohesive urban digital network, comprised of physical IoT devices with interconnected sensors and systems, AI technology to filter and analyze the data collected by the former, and enhanced autonomous decision-making capabilities.
In this framework, Edge Computing acts as the engine that powers the network. Its decentralization capabilities allow it to significantly reduce latency, operate independently of the cloud, and enhance bandwidth efficiency, critical for ensuring real-time actions and responses are truly immediate.
The combination of AIoT and Edge Computing greatly boosts the efficiency of various smart city applications, particularly in the following operations:
Smart traffic management system
Traditionally, sensors installed on roads transmitted data to a central server, which processed the information and then issued commands to traffic lights, road signs, and other systems. This back-and-forth, though only lasting a few seconds, could still cause delays.
Edge Computing, positioned close to the data source, processes information from the road network in real time, enabling rapid adjustments to traffic signals during peak times, dynamic lane changes based on congestion, and instant hazard alerts to vehicles.
Smart grids in urban environments
A smart grid integrates energy distribution with digital sensor communication technology, enabling a bidirectional flow of both electricity and data. This enables public service companies to optimize power generation, transmission, and distribution, all thanks to AI.
As an integral part of the grid, Edge Computing can monitor energy usage in real time, making instant adjustments to allocate energy where it is most needed or efficiently store surplus energy.
Cities already combining these technologies
Several global cities are already embracing the trends forecasted for 2025, with the Internet of Things, Artificial Intelligence, and Edge Computing taking center stage to elevate their management systems.
London, optimizing congestion with predictive models
Cities like London are already leveraging predictive models to manage traffic congestion effectively. By analyzing real-time data, AI can predict areas prone to major traffic jams, allowing authorities to redirect traffic or adjust public transportation schedules accordingly. Predictive analytics allow cities to plan ahead, optimizing resources and preventing issues before they arise.
Cities like Amsterdam use Edge Computing to monitor air quality and manage noise pollution, allowing them to respond swiftly to environmental changes.
Philadelphia: Sensors and Edge Computing for integrated management
Philadelphia’s SmartCityPHL project is a collaboration between the city, Comcast, and US Ignite, aimed at collecting real-time data on air quality, weather, transportation, and more, by installing sensors in street lighting. Data is processed on the cloud through Edge Computing, speeding up communication.
Copenhagen and Los Angeles also use smart lighting systems that analyze data on pedestrian activity, vehicle traffic, and ambient light to ensure optimal illumination. The most significant advantage is that these systems conserve energy compared to traditional public lighting, while still meeting safety standards.
Songdo, technologies to create a smart city from the ground up
The smart city of Songdo in South Korea incorporates Edge Computing into its urban management systems, showcasing the potential of this technology when integrated into entirely new urban developments.
Furthermore, it integrates AI and the Internet of Things into its infrastructure, connecting everything from homes and workplaces to automated waste collection and energy management through a smart digital network.
Oslo and Nordic countries: pioneers in Edge intelligence
Known for pushing the boundaries in urban management, Oslo and other Scandinavian countries are decentralizing artificial intelligence network systems to enhance the privacy and resilience of smart devices. This initiative has led to the creation of the concept of Edge Intelligence.
The goal is to decentralize AI inference, increasing distribution by creating multiple mini clouds that provide services, rather than relying on a single centralized cloud. This approach means AI will be distributed throughout the entire infrastructure and integrated into the IoT devices, enhancing both privacy and security. This is the focus of the NUEI project, which is currently in trial, with its duration extended until 2028.
Photographs | Unsplash/Steve Johnson, Unsplash/ Alex Knight, Unsplash/Jorge Ramirez, Unsplash/Christoffer Engström
