FSPLO: a fast sensor placement location optimization method for cloud-aided inspection of smart buildings

Area of Science:

• * Building Information Modeling (BIM) and smart building technologies.
• * Sensor networks and cloud computing applications.
• * Health and environmental monitoring in built environments.

Background:

• * Increasing demand for healthy and green buildings driven by health awareness.
• * Building Information Modeling (BIM) integrates health, environment, and IT for sustainable design.
• * Sensors and cloud computing are crucial for smart building functionality, monitoring environments and occupants.

Purpose of the Study:

• * To address the challenge of optimizing sensor placement in BIM-enabled smart buildings.
• * To reduce the number of sensors required while maintaining effective monitoring.
• * To develop a fast and efficient method for sensor location optimization.

Main Methods:

• * Proposed a Fast Sensor Placement Location Optimization (FSPLO) approach.
• * Utilized Locality Sensitive Hashing (LSH) techniques to filter redundant sensor locations.
• * Focused on optimizing sensor deployment for cloud-aided smart buildings.

Main Results:

• * FSPLO significantly reduces the number of sensors needed for smart buildings.
• * The method efficiently identifies optimal sensor locations.
• * Simulation experiments validated the excellent performance of the FSPLO approach.

Conclusions:

• * The FSPLO method offers an effective solution for sensor placement optimization in BIM-based smart buildings.
• * This approach contributes to energy savings and reduced sensor deployment costs.
• * FSPLO enhances the development of healthier and greener smart buildings.