mart Street Light Fault Identification For Multiple Smart Streets: A Review Of Iot Monitoring, Anomaly Detection And Maintenance Automation

Street lighting is essential for urban safety and accounts for a significant part of municipal electricity use. Traditional maintenance depends on manual night patrols or public complaints, so faulty lamps may stay dark for days, wasting energy and creating safety risks. This paper reviews technologies for a smart street light fault identification system that covers many streets. It examines IoT nodes that measure current, voltage, power and light intensity at each pole, wireless networks including ZigBee mesh and LoRaWAN for city-wide coverage, and control platforms that switch and dim lamps. Fault detection methods are reviewed, from threshold rules on electrical parameters to unsupervised anomaly detection that identifies abnormal behaviour such as flickering, partial failure, cable faults and power theft. Smart lighting deployments, India's national LED street-lighting programme and the cybersecurity risks of networked lighting are discussed. A five-layer architecture comprising pole-level sensing, communication, a central monitoring platform with fault analytics, a maintenance workflow and a city dashboard is proposed, with research gaps, objectives, a comparison of communication technologies and challenges such as cost, interoperability and security. The review concludes that automatic fault identification reduces downtime, maintenance cost and energy use while improving public safety.