Sense Cam Meets IoT: A Smarter Approach to Electrical Control

Abstract

This paper presents an innovative approach that integrates Sense Cam technology with the Internet of Things (IoT) to enable intelligent and automated electrical control systems. The proposed model allows electrical appliances to be controlled based on visual sensing, contextual awareness, and remote IoT connectivity, providing greater energy efficiency, convenience, and security. A prototype system was designed using a Sense Cam module for motion and presence detection, connected via an IoT platform for cloud-based monitoring and control. Experimental analysis demonstrates effective real-time operation with high detection accuracy and notable energy savings compared to conventional manual or sensor-based systems. The study highlights the potential of integrating visual sensing with IoT frameworks for next-generation smart home and industrial automation applications.

Introduction

The paper explores the integration of SenseCam technology with IoT for intelligent, context-aware electrical control, addressing limitations of traditional systems like PIR or LDR sensors. SenseCam, a vision-based device, captures environmental changes, human presence, and activity patterns. When combined with IoT, it enables real-time monitoring, remote control, and automated energy-efficient responses in smart homes, offices, and industrial settings. Edge computing and lightweight AI enhance local decision-making, reducing latency and reliance on cloud servers.

Objectives of the research include:

1. Designing a prototype SenseCam-IoT system for electrical automation.

2. Evaluating detection accuracy, latency, and energy efficiency.

3. Demonstrating advantages over traditional sensors.

4. Creating a scalable framework for diverse applications.

System Architecture:

• SenseCam captures images → computer vision detects human presence → decision logic evaluates conditions → Arduino controls relays → devices like lights/fans are activated → optical control units adjust in real time.

Comparative Analysis:

• PIR-based IoT: low-cost, basic motion detection, limited accuracy.

• Vision-based IoT: more accurate, detects multiple users, higher computational needs.

• SenseCam-based IoT: highly accurate, context-aware, adaptive to user behavior, suitable for advanced automation.

Challenges:

• Privacy/security concerns, large data storage, high computational and energy needs, latency issues, higher costs, integration/interoperability, and scalability for large deployments. Mitigation includes on-device processing, edge computing, standardized protocols, and hierarchical IoT structures.

Future Directions:

• AI-driven predictive control, privacy-preserving visual sensing, TinyML/edge computing, integration with renewable energy and smart grids, multi-modal sensor fusion, scalability solutions, and standardization for interoperability.

Conclusion

The integration of Sense Cam technology with the Internet of Things (IoT) presents a transformative approach to intelligent electrical control systems. Traditional electrical control systems are largely reactive, relying on fixed schedules, motion sensors, or manual operation. These methods, while functional, often lack the contextual awareness required to optimize energy consumption and adapt to human behavior dynamically. SenseCam, as a context-aware visual sensor, captures not only motion but also environmental conditions and activity patterns, enabling a deeper understanding of user behavior. When combined with IoT, this information can be leveraged to create smart, adaptive, and energy-efficient control systems. The review of recent literature demonstrates that SenseCam-IoT systems outperform conventional sensor-based systems in several key aspects: 1) Context-Aware Automation: By analyzing visual and environmental data, the system can distinguish between different types of activities (e.g., sitting, walking, leaving a room), enabling more precise control of electrical devices. 2) Energy Efficiency: Event-driven operation and predictive AI algorithms reduce unnecessary energy consumption, providing measurable savings in lighting, HVAC, and other electrical loads. 3) User Comfort and Personalization: Systems can learn user patterns over time and customize device operation according to individual preferences, enhancing comfort and convenience. 4) Scalability and Integration: When combined with edge computing and cloud analytics, SenseCam-IoT systems can be scaled to manage large environments such as campuses, office buildings, and industrial facilities. However, the review also highlights key challenges, including privacy concerns, high computational demands, cost, latency, and integration issues. Addressing these challenges through privacy-preserving algorithms, TinyML, standardized IoT protocols, and hierarchical edge-cloud architectures will be essential for practical deployment. Looking forward, the integration of SenseCam-IoT systems with AI-driven predictive control, multi-modal sensor fusion, renewable energy management, and smart grid frameworks presents a fertile area for future research. Pilot deployments and real-world validation will be crucial for evaluating system performance, energy savings, and user acceptance. In conclusion, SenseCam-enabled IoT systems represent a next-generation approach to electrical control, combining context awareness, adaptability, and energy efficiency. They have the potential to revolutionize smart homes, smart campuses, and industrial energy management, providing sustainable, user-friendly, and intelligent solutions. By addressing current challenges and leveraging advancements in AI, edge computing, and privacy-preserving technologies, these systems can achieve widespread adoption and significantly contribute to energy-efficient and sustainable smart environments.

References

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Copyright

Copyright © 2025 Y. D. Shahakar, Kanhaiya D. Dudhe, Anushri A. Bhatkar, Shrawani S. Bhusate, Tanvi S. Kalmegh, Krushna N. Mankar, Swaraj A. Hadole. 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.