Survey on Smart Street Light Monitoring and Fault Detection System

Abstract

Urban street lighting infrastructure represents a critical component of public safety and city management, yet conventional systems demonstrate significant operational inefficiencies. This survey examines emerging Internet of Things based approaches for intelligent streetlight monitoring and automatedfault detection. We analyze various architectural frameworks that integrate embedded sensors, microcontrollers, and cloud platforms to enable real-time diagnostics and energy optimization. The reviewed systems employ light intensitysensors,motiondetectors,andpowermonitoring modules to identify malfunctions while dynamically adjusting illumination basedonenvironmentalconditions and traffic patterns. This comprehensive analysisexplores different implementation strategies, communication protocols, and deployment architectures adopted across recent research initiatives. We identify persistent challenges including network reliability, scalability constraints, and deployment costs while highlighting opportunities for artificial intelligence integration and predictive maintenance capabilities. The findings demonstrate that IoT-enabled streetlight networks can substantially reduce energy consumption, accelerate maintenance response times, and contribute toward sustainable smart city development.

Introduction

The paper examines the need for intelligent, IoT-based streetlight monitoring systems due to increasing urbanization, high energy consumption, and the limitations of conventional lighting networks. Traditional streetlights operate inefficiently, lack real-time fault detection, rely on manual inspections, and pose safety risks when faults remain unnoticed. The proposed IoT solution uses ESP32 microcontrollers, multi-sensor fusion (LDR, PIR, temperature, current, vibration, GPS), and cloud platforms to enable real-time monitoring, precise fault localization, adaptive dimming, and predictive maintenance—improving energy efficiency, operational reliability, and public safety.

Despite advances in smart lighting research, key challenges persist: fragmented designs, poor interoperability, simple threshold-based fault detection prone to errors, limited predictive maintenance, basic energy optimization methods, weak integration with renewable energy, and insufficient focus on IoT security and privacy. The paper contributes a structured comparison of streetlight architectures, a taxonomy of fault-detection methods, and an assessment of energy optimization techniques to guide future smart city deployments.

The survey of related work highlights multiple approaches: centralized ESP32-based monitoring frameworks, energy-efficient motion-responsive lighting systems, multi-sensor fault detection for classifying electrical/mechanical failures, adaptive brightness control using Raspberry Pi, and cloud-integrated solutions using Firebase for real-time tracking and automated alerts.

A research gap analysis reveals consistent shortcomings: limited sensor fusion, inaccurate fault classification, slow cloud-dependent detection, poor location accuracy, and systems that separate monitoring from control. The proposed solution addresses these by using multi-sensor fusion, dual-LDR logic, real-time edge processing, detailed fault taxonomy, GPS-based precision tracking, and integrated adaptive control.

The system architecture typically follows a layered design involving sensing, control, communication, and application layers. Sensor integration balances accuracy with cost, using LDRs, PIR sensors, electrical monitors, and GPS. Wi-Fi and MQTT are common communication choices, while cloud platforms like Firebase or AWS support real-time data processing and dashboards.

Evaluation methodologies include threshold detection, pattern recognition, location-based reporting, and predictive analytics using historical data. Machine learning approaches enable advanced diagnostics, fault prediction, and proactive maintenance planning.

A multi-dimensional performance framework assesses fault detection accuracy, false alarm reduction, energy savings, communication reliability, location precision, maintenance efficiency, scalability, and cost-effectiveness—using metrics such as precision, recall, F1-score, energy reduction %, latency, PDR, GPS error, and system uptime.

Future work includes integrating machine learning for predictive maintenance, renewable energy sources (solar + smart grid), scalable communication protocols (LoRaWAN/NB-IoT), mobile apps for citizen reporting, edge AI on ESP32, advanced environmental monitoring, integration with broader smart city systems, and large-scale cloud analytics to support multi-city deployments.

Conclusion

This comprehensive survey examined the rapidlyevolvingfield of IoT-based intelligent streetlight monitoring and faultdetection systems. The analysis encompassed diverse architectural approaches, sensor integration strategies, communication protocols, and deployment methodologies developed across recent research initiatives. Contemporary systems demonstrate substantial capabilities for automatedfault detection, energy optimization, and maintenance efficiency improvements compared to conventional streetlight infrastructure. Integration of light intensity sensors, motion detectors, power monitors, and GPS modules enables comprehensive monitoring withprecisefaultlocalization.Cloud platform adoption provides scalable data management and intuitive visualization interfaces supporting centralized operations management. However, several challenges constrain widespread adoption and optimal performance. Network reliability concerns, scalability costs, environmental durability requirements, and security vulnerabilities require continued attention. Standardization efforts remain incomplete, limiting interoperability and competitive technology evolution. Future research directions including artificial intelligence integration, enhanced sensor capabilities, and advanced communication technologies promise substantial performance improvements. The convergence of declining component costs, improving wireless technologies, and increasing smart city initiatives createsfavorableconditionsforacceleratedintelligentstreetlight deployment.Assystemsmatureandstandardsdevelop, IoT-based lighting networks will increasingly constitute fundamental smart city infrastructure supporting sustainable urban development objectives.

References

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Copyright

Copyright © 2025 Rushikesh S. Desale, Parth R. Chaudhari, Lokesh R. Bhadane, Prof. Nitin Dhamale. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.