Smart Lighting and Power Optimization System Using IoT and Edge Computing

Project Abstract

The rapid urbanization and growing demand for energy efficiency have intensified the need for intelligent lighting and power management systems in commercial, industrial, and residential environments. This research presents a comprehensive framework for a Smart Lighting and Power Optimization System (SLPOS) that integrates Internet of Things (IoT) sensing, edge computing, and adaptable control algorithms to minimize energy consumption while maintaining occupant comfort and illumination quality. The proposed system employs a hierarchical architecture consisting of IoT-enabled luminaires and sensors, edge devices for real-time data processing, and a centralized cloud-based analytics layer for long-term optimization and historical insights. A multi-sensor fusion approach combines ambient light, occupancy, daylight availability, temperature, and glare metrics to generate context-aware lighting policies. The edge computing layer executes lightweight optimization routines locally to ensure ultra-low latency for critical decisions, such as dynamic dimming, occupancy-based switching, and scene management, while securely streaming anonymized data to the cloud for batch processing, trend analysis, and predictive maintenance. Key contributions include the development of an energy-aware lighting model that accounts for luminaire photometric performance, spectral quality, and room geometry, enabling precise calculation of instantaneous and cumulative energy use. A novel adaptive control strategy integrates rule-based logic with model-predictive control (MPC) to balance energy savings with illumination standards and user preferences. The system incorporates daylight harvesting capabilities through real-time measurement of window luminance and automatic adjustability of artificial lighting to exploit natural light, thereby reducing electricity load. To address scalability and interoperability, the architecture leverages standard communication protocols (e.g., MQTT, CoAP, and OPC UA) and a modular software framework that supports plug-and-play sensor and luminaire integration. The research presents a rigorous evaluation methodology comprising simulation-based analysis and a physical testbed with commercially available sensors, lighting fixtures, and edge devices. Performance metrics include total energy consumption, peak demand reduction, luminance uniformity, color rendering index (CRI), correlated color temperature (CCT) stability, occupancy detection accuracy, and user satisfaction in a controlled environment. Results from simulations and experimental trials demonstrate substantial energy savings (projected reductions up to 40–55% under varying occupancy and daylight conditions) without compromising lighting quality or occupant comfort. The edge-accelerated optimization reduces response times and offloads cloud resources, enabling scalable deployment across multiple zones with minimal latency. The system also provides actionable insights through dashboards and alerts for maintenance, spectral tuning, and demand-response participation. Ethical and security considerations are addressed by implementing end-to-end encryption, authentication, and access control, along with data minimization and anonymization strategies to protect occupant privacy. The research discusses potential deployment challenges, including integration with existing electrical infrastructure, retrofitting costs, and standardization barriers, and offers guidelines for retrofit planning, cost-benefit analysis, and energy policy compliance. Overall, the study demonstrates that a tightly integrated IoT-edge framework can deliver robust, scalable, and user-centric lighting and power optimization, contributing to significant energy efficiency gains, reduced carbon footprint, and enhanced occupant well-being.

Project Overview

What This Project Is About

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A practical study on how smart lighting systems use sensors, communication networks, and simple computer logic to light spaces efficiently. It explores how Internet of Things (IoT) devices and edge computing can work together to adjust lighting and power use in real time, based on occupancy, daylight, and user preferences.

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The Problem It Addresses

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Many buildings waste energy due to lights staying on when spaces are empty or when daylight makes artificial lighting unnecessary. Traditional lighting systems lack timely adjustments and centralized control. This project investigates ways to reduce energy use while maintaining comfort and safety.

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Objectives of the Project

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1. Identify energy waste patterns in common indoor spaces.
2. Design a low-cost sensor suite to detect occupancy and ambient light.
3. Develop an IoT framework to send data to a local edge device for quick decisions.
4. Create algorithms to turn lights on/off and dim them based on conditions.
5. Evaluate energy savings and user satisfaction in a simulated or real setup.

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What You Will Do Step by Step

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1. Review literature on smart lighting and edge computing basics.
2. Build or simulate a small network of sensors and smart lights.
3. Set up an edge device to process data locally and run control rules.
4. Implement occupancy and daylight detection algorithms.
5. Test system under different scenarios and collect data.
6. Analyze energy usage, costs, and comfort metrics.
7. Prepare a user guide and demonstration plan.
8. Document results and discuss limitations and future work.

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Expected Outcome

The project should deliver a working framework for a smart lighting system that reduces energy use while maintaining comfort, with clear data showing potential savings and a plan for scale-up.