Adaptive Smart Streetlighting: A Review, Mathematical Modeling, and Performance Evaluation

  • Unique Paper ID: 185707
  • PageNo: 2519-2524
  • Abstract:
  • This paper presents an extensive and in-depth review of adaptive smart streetlight systems, highlighting their evolution, architecture, control techniques, and emerging research directions. It discusses a comprehensive mathematical framework integrating Model Predictive Control (MPC), Reinforcement Learning (RL), and joint communication-control optimization strategies for energy-efficient illumination management. A systematic literature review encompassing more than 30 recent studies is provided, detailing advancements in sensing technologies, communication protocols (LoRaWAN, NB-IoT, PLC, Mesh), and control architectures. The paper also defines a detailed evaluation methodology comprising both simulation-based experiments and a pilot-scale emulation. Results and analytical comparisons among three key control strategies—Rule-based, MPC, and RL—under varying traffic and occupancy conditions demonstrate that MPC and RL significantly outperform traditional rule-based approaches, achieving higher energy savings while maintaining illuminance and safety constraints. Furthermore, joint optimization of lighting control and communication scheduling is shown to yield additional system-level efficiency gains. The findings reinforce the promise of adaptive lighting as a cornerstone for sustainable, intelligent urban infrastructure.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article 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.

BibTeX

@article{185707,
        author = {S. B. Warkad and Pratiksha Berad and Sanika Umap and Sejal Likhar and Sakshi Khode and Sanika Korde and Siddhi Sonar},
        title = {Adaptive Smart Streetlighting: A Review, Mathematical Modeling, and Performance Evaluation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {2519-2524},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185707},
        abstract = {This paper presents an extensive and in-depth review of adaptive smart streetlight systems, highlighting their evolution, architecture, control techniques, and emerging research directions. It discusses a comprehensive mathematical framework integrating Model Predictive Control (MPC), Reinforcement Learning (RL), and joint communication-control optimization strategies for energy-efficient illumination management. A systematic literature review encompassing more than 30 recent studies is provided, detailing advancements in sensing technologies, communication protocols (LoRaWAN, NB-IoT, PLC, Mesh), and control architectures. The paper also defines a detailed evaluation methodology comprising both simulation-based experiments and a pilot-scale emulation. Results and analytical comparisons among three key control strategies—Rule-based, MPC, and RL—under varying traffic and occupancy conditions demonstrate that MPC and RL significantly outperform traditional rule-based approaches, achieving higher energy savings while maintaining illuminance and safety constraints. Furthermore, joint optimization of lighting control and communication scheduling is shown to yield additional system-level efficiency gains. The findings reinforce the promise of adaptive lighting as a cornerstone for sustainable, intelligent urban infrastructure.},
        keywords = {adaptive streetlight, smart lighting, IoT, MPC, reinforcement learning, energy optimization, TALQ.},
        month = {October},
        }

Cite This Article

Warkad, S. B., & Berad, P., & Umap, S., & Likhar, S., & Khode, S., & Korde, S., & Sonar, S. (2025). Adaptive Smart Streetlighting: A Review, Mathematical Modeling, and Performance Evaluation. International Journal of Innovative Research in Technology (IJIRT), 12(5), 2519–2524.

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