AI and IoT Applications for Safety in Smart Cities

Smart cities face an ongoing challenge: how to keep millions of residents safe without overwhelming public safety resources. The answer increasingly lies in artificial intelligence and Internet of Things technologies. From intelligent cameras that detect suspicious activity to autonomous robots patrolling public spaces, AI and IoT are transforming urban security from reactive systems into proactive, data-driven safety networks. These technologies work together to monitor public spaces, control access to sensitive areas, and respond to emergencies faster than ever before.

Video analytics for real-time monitoring 🔗

Traditional surveillance relied heavily on human operators watching multiple camera feeds simultaneously. This approach was inherently limited-a person can typically monitor footage from about 16 cameras during a 4-6 hour shift. Fatigue, distraction, and the sheer volume of visual information made it impossible to catch every potential threat. AI-powered video analytics fundamentally changes this equation.

Modern AI video analytics systems can automatically analyze, detect, and determine events in video footage without requiring constant human attention. These systems use deep learning algorithms to identify specific objects, behaviors, and patterns that may indicate criminal activity or safety hazards.

How AI transforms ordinary cameras 🔗

The technology works by transforming existing cameras into smart sensors-automating alerts, improving safety, and unlocking insights without requiring new hardware. This is significant because cities have already invested billions in camera infrastructure. Rather than replacing these systems, AI software can be layered on top to add intelligent capabilities.

Key capabilities of AI video analytics include:

• Intrusion detection: Systems can identify when someone enters a restricted area or crosses a virtual boundary, immediately alerting security personnel.
• Object recognition: The technology can detect abandoned bags, vehicles in prohibited zones, or weapons with remarkable accuracy.
• Behavioral analysis: AI can recognize suspicious behaviors such as loitering, fighting, or erratic movement patterns that might precede criminal activity.
• License plate recognition: Advanced systems can achieve up to 99% accuracy in reading license plates, even in poor lighting or bad weather conditions.

Cities worldwide are deploying these technologies at scale. AI plays a critical role in public safety by enabling real-time monitoring and analysis in urban environments. For example, AI can analyze traffic camera feeds to identify violations, spot accidents, and predict potential congestion-all without human intervention.

Crowd monitoring with sensors and cameras 🔗

Managing large crowds presents unique safety challenges. Events like concerts, festivals, sports matches, and even daily commutes through busy transit stations require constant monitoring to prevent dangerous situations like stampedes or crushing. IoT sensor networks are crucial for gathering real-time data from event areas and public spaces where crowds gather.

Multi-sensor approach to crowd safety 🔗

Effective crowd monitoring combines multiple technologies working together. Sensors and video surveillance can monitor crowd levels, while AI algorithms analyze the data to predict potential problems before they occur.

Footfall counting is a fundamental capability. IoT-enabled sensor nodes can count pedestrians with 95% accuracy while protecting privacy by not capturing images or video. This approach uses specially tuned infrared sensors that detect human movement patterns without recording identifiable information.

Cities like London utilize vision-driven crowd monitoring in their transport systems, and AI-enhanced surveillance was employed for safety at the Paris 2024 Olympic Games. These systems can generate heat maps showing crowd density in real time, allowing authorities to redirect pedestrian flow before dangerous overcrowding occurs.

Detecting threats in dense crowds 🔗

Scanning devices can detect objects made of specific materials and identify their shape, even if they are concealed. When combined with real-time AI analysis, these systems can determine whether detected objects pose a threat. This is particularly valuable at entry points to stadiums, transit stations, and public venues where manual screening would create prohibitive delays.

The integration of crowd monitoring with emergency response systems is equally important. If footbridges become overcrowded, IoT systems issue warnings to the public and authorities, prompting necessary actions to manage the situation before it becomes dangerous. This proactive approach represents a fundamental shift from responding to incidents after they occur to preventing them entirely.

RFID and sensor-enabled access control 🔗

Securing entry points to critical infrastructure-airports, government buildings, data centers, and industrial facilities-requires sophisticated access control systems. RFID access control provides a contactless entry method, allowing users to simply approach the reader to complete identity verification. This convenience significantly enhances user experience, especially in high-traffic areas.

How RFID access control works 🔗

RFID (Radio Frequency Identification) technology uses electromagnetic fields to automatically identify and track tags attached to objects-or in security applications, carried by people via cards or badges. RFID enhances airport security by automating access control for restricted areas. RFID-enabled ID badges allow for easy authentication of airport personnel, reducing reliance on manual verification while improving security protocols.

The technology offers several advantages over traditional access methods:

• Speed: Contactless verification takes fractions of a second, essential for facilities with high personnel throughput.
• Durability: RFID tags are generally waterproof and dustproof, making them suitable for various environments including outdoor and industrial settings.
• Scalability: Systems can easily accommodate thousands of users across multiple access points.
• Audit trails: Every access event is logged, providing comprehensive records for security reviews and investigations.

Multi-factor authentication for maximum security 🔗

For the most sensitive areas, RFID alone may not provide sufficient security. Combining RFID and biometrics provides an additional layer of security, as it ensures that the person using the RFID card is indeed the authorized individual. For instance, an employee’s RFID card can be linked to their fingerprint, requiring them to scan both for access.

IoT-enabled systems such as real-time surveillance cameras, biometric access control, and RFID sensors provide enhanced security measures that work together as an interconnected ecosystem. Data flows smoothly between systems, allowing security personnel to monitor access patterns and immediately detect anomalies such as attempted unauthorized entry or unusual access timing.

Patrol robots and drones 🔗

Perhaps the most visible manifestation of AI and IoT in urban security is the emergence of autonomous patrol systems. These range from ground-based robots to aerial drones, each designed to extend the reach of security personnel while reducing risks and costs.

Ground-based security robots 🔗

Robots employed for surveillance, patrolling, and emergency response add an extra layer of vigilance that complements conventional policing approaches. These machines can operate continuously, covering areas that would require multiple human guards to monitor effectively.

In April 2023, the New York Police Department introduced Digidog-a pair of patrolling robots designed to enhance security in Times Square. These advanced robots come equipped with LiDAR, cameras, GPS, speakers, and microphones. The pilot initiative assessed the effectiveness of robotic patrollers, with human officers providing oversight to ensure proper integration of technology into law enforcement practices.

Other notable deployments include Goalie in Seoul, South Korea, which uses thermal imaging to patrol at night, and Xavier in Singapore, which monitors public areas to deter poor social behavior. AI-driven autonomous security robots like the Knightscope K5 patrol commercial properties and public spaces 24/7, providing mobile perimeter protection and physical deterrence with intelligent sensors.

Aerial drone surveillance 🔗

Aerial drones provide real-time surveillance through aerial photography and videography, enabling monitoring of large crowds by law enforcement authorities. They can even live-stream incidents such as hostage situations, giving commanders immediate situational awareness.

Equipped with thermal cameras and other sensors, drones generate visual maps of infrared radiation emitted by various objects. This capability enhances authorities’ ability to visualize situations, particularly in adverse weather conditions like fog or smoke, as well as in hazardous environments.

Patrol robots and drones can monitor vast areas-from large warehouses to stadiums and even streets. They navigate autonomously with the help of 2D and 3D cameras, making them truly self-sufficient. This is critical for perimeter security, especially at night when human security staff might not be available.

Reducing human risk in dangerous situations 🔗

One of the most compelling arguments for robotic security is the protection of human personnel. Whether patrolling hazardous areas or monitoring large crowds, robots take on tasks that would otherwise put human lives at stake. During protests or potential terrorist threats, sending in a security robot mitigates danger while maintaining oversight. This allows human personnel to focus on strategic decision-making rather than immediate threat response.

The integrated future of urban safety 🔗

These technologies are most powerful when they work together. Video analytics can detect a potential threat, triggering an automated drone response for closer investigation while simultaneously alerting human operators and activating access controls to lock down affected areas. Video surveillance systems with AI image recognition can check how many people are in a crowd, notice if someone is breaking in, and highlight any suspicious behavior, all helping to create a complete and up-to-date understanding of the surroundings.

The shift from reactive to proactive security represents a fundamental transformation in how cities protect their residents. Rather than responding to incidents after they occur, smart city security systems can predict and prevent problems, allocate resources more efficiently, and provide authorities with the information they need to make better decisions faster.

What do you think? As AI and IoT become more prevalent in urban security, how should cities balance the benefits of enhanced safety with concerns about privacy and surveillance? And what role should citizens play in shaping the policies that govern these powerful technologies?

References

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