Fundamentals, Algorithms, and Technologies of Occupancy Detection for Smart Buildings Using IoT Sensors

Area of Science:

• Smart Building Technology
• Internet of Things (IoT)
• Sensor Networks

Background:

• Smart buildings leverage advanced technologies for automated functions, with occupancy detection being key.
• Occupancy data optimizes energy consumption, indoor air quality, and building security.
• Existing surveys lack comprehensiveness, necessitating a detailed overview of current techniques.

Purpose of the Study:

• To provide a comprehensive survey of state-of-the-art occupancy detection methods and devices for smart buildings.
• To compare traditional and machine learning algorithms, alongside various sensor technologies.
• To offer insights for developing more effective occupancy detection solutions.

Main Methods:

• Review of traditional and machine learning-based occupancy detection algorithms.
• Analysis of various Internet of Things (IoT) sensor technologies used in smart buildings.
• Comparative assessment of device advantages and limitations.

Main Results:

• Identification of key factors limiting occupancy detection accuracy, including sensor placement and privacy concerns.
• Overview of diverse occupancy detection technologies and their performance characteristics.
• Synthesis of current research trends and challenges in the field.

Conclusions:

• A comprehensive understanding of occupancy detection technologies is essential for advancing smart building solutions.
• Addressing limitations such as sensor accuracy and privacy is critical for widespread adoption.
• This survey serves as a valuable resource for researchers and practitioners in smart building development.