AI and Edge computing play an important role in building future smart cities. By integrating Edge AI into urban infrastructure, cities are enhancing their data privacy and security, improving traffic control and public safety, as well as fostering collaborative innovation.

When smart cities invest in Edge AI technology, they are also investing in their own cybersecurity. Edge AI can revolutionise city security by ensuring sensitive data, such as CCTV feeds and facial recognition outputs, are processed locally, reducing exposure to external threats and aligning better with local and national privacy regulations. Edge AI also reduces the risk of data breaches and unauthorised access compared to transmitting data to centralised servers for processing.  By deploying Edge AI for smart city cybersecurity, cities can strengthen cybersecurity measures by continuously monitoring for signs of cyber threats and malicious activity. Through real-time monitoring and data processing, paired with rapid response capabilities, Edge AI can support a safer city environment for residents. Edge AI security systems equipped with machine learning enhance threat detection and improve emergency responses by analysing CCTV feeds in real-time, identifying irregularities and potential disasters, and alerting command centres and first responders, all without sending any sensitive data to the cloud.   

In addition to enabling secure, real-time data analysis that can assist a wide range of smart city infrastructures, Edge AI offers an advantage in reducing latency. By processing data locally at the edge (in sensors, cameras, or local servers), Edge AI enables real-time decision-making which is critical for traffic control, public safety, and autonomous vehicles while also providing data security compared to AI using centralised servers. Edge AI can support transportation in smart cities through Edge-based localisation and mapping, in which Edge AI algorithms can perform localisation and mapping tasks directly onto the vehicle using onboard sensors and data, which enables autonomous vehicles to navigate accurately without relying on GPS signals (see our section on Edge AI and Smart Transportation). In addition to Edge AI enabling autonomous vehicle transportation in smart cities, by processing data from IoT sensors, traffic cameras, and vehicle GPS systems, Edge AI can support real-time traffic management and optimise traffic control by regulating traffic signals, providing alternative routes, and reducing congestion, allowing for faster and smoother commutes and reduced CO2 emissions. Likewise, Edge AI has the ability to detect potential issues in city infrastructure, like ageing utility systems and road surface deterioration, which can save cities money by efficiently tackling these issues before they escalate.

Smart cities can also save money and improve efficiency by employing Edge AI in their approaches to waste management. By utilising Edge AI technology, smart cities can also streamline waste management by optimising collection routes, improving recycling processes, and increasing the overall efficiency of waste control in cities. Edge AI algorithms can be employed in recycling facilities to automate the sorting process. By using machine learning technology and computer vision, Edge AI systems can identify and separate different types of materials like plastics, metals, and paper, making recycling operations more efficient and reducing contamination. Edge AI can also be utilised in waste management equipment such as waste collection trucks and compactors, analysing data from sensors and IoT devices to detect maintenance issues early, which improves the lifespan of equipment and saves waste management facilities money.  Smart waste bins also use Edge AI sensors to detect waste levels, classify the type of waste, and support waste collection routes. Smart waste bins analyse real-time data to alert waste management facilities when they need to be emptied, reducing the number of trips to empty these bins and enhancing resource distribution.