CYBERSECURITY RISKS IN SMART STREET LIGHTING SYSTEMS: FROM IoT EFFICIENCY TO CRITICAL INFRASTRUCTURE PROTECTION

Smart street lighting integrates energy-efficient illumination with networked sensing, cloud services, remote control, and automated decision-making. While this improves operational efficiency, it also transforms conventional lighting assets into cyber-physical components of urban infrastructure. This study evaluates cybersecurity risks in a low-cost smart street lighting architecture based on an ESP32-class controller, motion and ambient-light sensors, Wi-Fi connectivity, a Firebase-style cloud backend, and a web dashboard. The assessment combines architecture decomposition, asset and trust-boundary identification, STRIDE threat modeling, and threat-to-control mapping. Six principal threat categories are identified: spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege. The analysis shows that hardcoded credentials, insecure provisioning, misconfigured cloud rules, sensor spoofing, firmware compromise, unauthorized remote control, botnet recruitment, and lateral movement can cause consequences ranging from energy waste and loss of system availability to disruption of public safety.