ReviewSensors (Basel, Switzerland)2024
Fundamentals, Algorithms, and Technologies of Occupancy Detection for Smart Buildings Using IoT Sensors.
Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- ADS Guard: A Generalizable Defense Framework for Adversarially Robust Occupancy Detection in Smart Buildings.Sensors (Basel, Switzerland) · 2026Article
- A Method for Optimized Monitoring of Indoor Air Quality in Public Buildings.Sensors (Basel, Switzerland) · 2026Article
- An Integrated IoT-Based Multi-Sensor Framework for Real-Time Indoor Environment and Safety Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Smart room occupancy detection using neural networks and the puma optimization algorithm.Scientific reports · 2025Article
- Hybrid approach for evaluating dynamic scheduling mechanisms in IoT cognitive sensors for enhanced network performance.Scientific reports · 2025Article
- From Street Canyons to Corridors: Adapting Urban Propagation Models for an Indoor IQRF Network.Sensors (Basel, Switzerland) · 2025Article
- AI-Powered Building Ecosystems: A Narrative Mapping Review on the Integration of Digital Twins and LLMs for Proactive Comfort, IEQ, and Energy Management.Sensors (Basel, Switzerland) · 2025Review
- Design and Framework of Non-Intrusive Spatial System for Child Behavior Support in Domestic Environments.Sensors (Basel, Switzerland) · 2025Article
- Respiration Signal Pattern Analysis for Doppler Radar Sensor with Passive Node and Its Application in Occupancy Sensing of a Stationary Subject.Biosensors · 2025Article
- Internet of Robotic Things: Current Technologies, Challenges, Applications, and Future Research Topics.Sensors (Basel, Switzerland) · 2025Review
- Advanced Functional Materials for Electrochemical and Biosensors.Biosensors · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Smart buildings use advanced technologies to automate building functions. One important function is occupancy detection using Internet of Things (IoT) sensors for smart buildings. Occupancy information is useful information to reduce energy consumption by automating building functions such as lighting, heating, ventilation, and air conditioning systems. The information is useful to improve indoor air quality by ensuring that ventilation systems are used only when and where they are needed. Additionally, it is useful to enhance building security by detecting unusual or unexpected occupancy levels and triggering appropriate responses, such as alarms or alerts. Occupancy information is useful for many other applications, such as emergency response, plug load energy management, point-of-interest identification, etc. However, the accuracy of occupancy detection is limited by factors such as real-time occupancy data, sensor placement, privacy concerns, and the presence of pets or objects that can interfere with sensor reading. With the rapid development of IoT sensor technologies and the increasing need for smart building solutions, there is a growing interest in occupancy detection techniques. There is a need to provide a comprehensive survey of these technologies. Although there are some exciting survey papers, they all have limited scopes with different focuses. Therefore, this paper provides a comprehensive overview of the current state-of-the-art occupancy detection methods (including both traditional algorithms and machine learning algorithms) and devices with their advantages and limitations. It surveys and compares fundamental technologies (such as sensors, algorithms, etc.) for smart buildings. Furthermore, the survey provides insights and discussions, which can help researchers, practitioners, and stakeholders develop more effective occupancy detection solutions for smart buildings.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.