Indoor Occupancy Research Articles

Indoor occupancy monitoring using environmental feature fusion and semi-supervised machine learning models

Smart buildings optimize energy consumption and occupant comfort through Heating, Ventilation and Air Conditioning, and lighting management. Nevertheless, large venues require data fusion techniques to improve analysis and forecasting. This study aims to evaluate the effectiveness of using different feature fusion techniques, environmental sensors, and semi-supervised learning to estimate indoor occupancy in a 230 m2 office. Using five Internet of Things devices measuring air temperature, relative humidity, and barometric pressure, data was collected for 99 days with 6800 entries (on average) and only 14% labeled. Eight feature selection methods were evaluated along with three supervised and two semi-supervised classification methods. Results indicate that the Chi-squared-based approach for feature fusion outperformed others. Similarly, the semi-supervised Self-Training model achieved better performance than the supervised methods. This research shows that combining semi-supervised learning and data fusion allows for estimating the occupancy level in large indoor spaces with high accuracy and low labeling costs. Highlights This study pioneers in exploring semi-supervised learning and distinct feature fusion methods for estimating indoor occupancy levels in a 230 m 2 open office using only Internet of Things (IoT) environmental sensors (air temperature, relative humidity, and barometric pressure). A comprehensive comparison of statistical methods, feature selection, and dimensionality reduction techniques are conducted to determine their ability to generate robust feature fusion sets. The feature fusion selected through the Chi-squared test stood out with a high accuracy F1-score (average of 0.95) and an average accuracy of 0.99. The Self-Training model reached the best performance from semi-supervised learning, with an average F1-Score of 0.90 and an average accuracy of 0.97, based on a dataset with a large proportion of unlabelled data (16,847 entries) and only 9367 labels. For supervised learning, Random Forest achieved a high accuracy (average of 0.98) and F1-score (average of 0.93) across various feature sets.

A mmWave Sensor and Camera Fusion System for Indoor Occupancy Detection and Tracking

A mmWave Sensor and Camera Fusion System for Indoor Occupancy Detection and Tracking

An Advanced Explainable Belief Rule-Based Framework to Predict the Energy Consumption of Buildings

The prediction of building energy consumption is beneficial to utility companies, users, and facility managers to reduce energy waste. However, due to various drawbacks of prediction algorithms, such as, non-transparent output, ad hoc explanation by post hoc tools, low accuracy, and the inability to deal with data uncertainties, such prediction has limited applicability in this domain. As a result, domain knowledge-based explainability with high accuracy is critical for making energy predictions trustworthy. Motivated by this, we propose an advanced explainable Belief Rule-Based Expert System (eBRBES) with domain knowledge-based explanations for the accurate prediction of energy consumption. We optimize BRBES’s parameters and structure to improve prediction accuracy while dealing with data uncertainties using its inference engine. To predict energy consumption, we take into account floor area, daylight, indoor occupancy, and building heating method. We also describe how a counterfactual output on energy consumption could have been achieved. Furthermore, we propose a novel Belief Rule-Based adaptive Balance Determination (BRBaBD) algorithm for determining the optimal balance between explainability and accuracy. To validate the proposed eBRBES framework, a case study based on Skellefteå, Sweden, is used. BRBaBD results show that our proposed eBRBES framework outperforms state-of-the-art machine learning algorithms in terms of optimal balance between explainability and accuracy by 85.08%.

New Methodology to Evaluate and Optimize Indoor Ventilation Based on Rapid Response Sensors.

The recent pandemic increased attention to the need for appropriated ventilation and good air quality as efficient measures to achieve safe and healthy indoor air. This work provides a novel methodology for continuously evaluating ventilation in public areas using modern rapid response sensors (RRS). This methodology innovatively assesses the ventilation of a space by combining a quantitative estimation of the real air exchange in the space-obtained from CO2 experimental RRS measurements and the characteristics of and activity in the space-and indoor and outdoor RRS measurements of other pollutants, with healthy recommendations from different organisations. The methodology allows space managers to easily evaluate, in a continuous form, the appropriateness of their ventilation strategy, thanks to modern RRS measurements and direct calculations (implemented here in a web app), even in situations of full activity. The methodology improves on the existing standards, which imply the release of tracer gases and expert intervention, and could also be used to set a control system that measures continuously and adapts the ventilation to changes in indoor occupancy and activity, guaranteeing safe and healthy air in an energy-efficient way. Sample public concurrence spaces with different conditions are used to illustrate the methodology.

Exploring occupant detection model generalizability for residential buildings using supervised learning with IEQ sensors

This study explores two modeling approaches for occupancy detection at room level for residential buildings in Denmark. The aim is to assess the performance and generalizability of occupant detection models using XGBoost method trained on a rich dataset comprising indoor environmental quality (IEQ) variables and occupancy ground truth. A global approach (all rooms) and a room-specific approach are considered. After a thorough feature- selection and importance analysis process, the occupancy detection models (ODMs) are trained and tested in a nested cross-validation schema. The time of the day, indoor CO2 concentration, and feature transforms related to short-term IEQ dynamics were found to be the most important features of the ODMs. Both the global and room-specific models show good occupancy prediction performance, especially for bedrooms. When tested for generalizability with an unseen dataset from a different residential building, the ODMs maintain very good performance for the bedroom but not for the office room. This discrepancy could be explained by significant differences in occupancy and ventilation patterns, and large air infiltration from adjacent rooms. Although currently limited in terms of generalizability, XGBoost-based ODMs using IEQ data have the potential to provide robust and scalable occupancy detection for occupant-centric control and occupant-aware building performance assessments. The IEQ dataset with occupancy ground truth collected for this study is made available in open access.

SSIOE: Self-Supervised Indoor Occupancy Estimation for Intelligent Building Management

This paper aims to estimate indoor occupancy given the real-time observed signals from the existing sensors; next, as a practical application, we build a dynamic control schedule for energy saving based on the estimated indoor occupancy. However, several issues need to be addressed. First, it is impossible to train the model with rich labels due to the expensive labeling cost. Second, manual annotation of the continuous occupancy rate is complex. Third, the mapping relationship between sensor data and occupancy will change in the long run. In this paper, we proposed a new algorithm named Self-Supervised Indoor Occupancy Estimation (SSIOE) to overcome the challenges. Specifically, our training scheme aims to (I) generate a set of pseudo labels in a simple way to mark the time periods believed to be either in a high or low occupancy state and (II) utilize these sparse labels for training a network to infer the continuous occupancy ratio. By reformulating the problem as a Wasserstein-distance-like estimation, SSIOE is a novel learning-based method that can rely only on the weak/sparse labels of either “high-occupancy” or “low-occupancy” and learn to estimate the continuous occupancy ratio. Furthermore, to deal with the scarce annotation problem, we proposed a novel physical constraint loss to model the physical prior. (III) Last but not least, to strengthen the adaptability, we integrated Model-Agnostic Meta-Learning (MAML) to train the model for dynamic model updating. Experimental results show that SSIOE can provide reliable occupancy estimation and flexibly adapt to various control modes without retraining the model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the real-time occupancy estimation problem, which is crucial for building management systems to arrange lighting and air conditioning. A dynamic schedule adapted to the occupancy would help balance the user’s comfort and energy saving; however, installing the occupancy sensors raises additional costs. Therefore, this paper proposes SSIOE, a label-free machine learning algorithm that can learn to estimate the continuous occupancy ratio given the sensor status in real time. Prior works trained upon supervised machine learning methods suffer from heavy manual annotation workload, while the unsupervised machine learning approaches fail to provide continuous occupancy estimation values. This paper addresses the above challenges by proposing a novel self-supervised training scheme. Preliminary experiments suggest that this approach is feasible to work in real-world buildings. However, it should be noted that the occupancy estimated by SSIOE is a ratio (0.0-1.0), which is relative to the maximum and minimum occupancy of the training data (or fine-tuning data). For example, if the maximum number of occupants in the training data is 200 and the minimum number of occupants is 50, then the estimated occupancy value of 1.0 is about 200 occupants, and the occupancy value of 0.0 is about 50 occupants. When the maximum and minimum occupants of the space change, the model needs to be updated to capture the new mapping of the estimated occupancy ratio to the number of occupants. In future research, we plan to further improve and simplify the adaptation ability of SSIOE by continuously and automatically updating the system with the latest collected as the system runs.

A Real-Life Evaluation of Supervised and Semi-Supervised Machine Learning Approaches for Indirect Estimation of Indoor Occupancy

A Real-Life Evaluation of Supervised and Semi-Supervised Machine Learning Approaches for Indirect Estimation of Indoor Occupancy

Low-cost data-driven estimation of indoor occupancy based on carbon dioxide (CO2) concentration: A multi-scenario case study

Occupancy levels significantly influence HVAC system operation, making accurate occupancy prediction essential for the advancement of Occupant-Centered HVAC control. This study aims to develop a simple and effective occupant prediction model in buildings using low-cost indoor environmental sensors and artificial intelligence technology. In-situ measurements were taken in two university classrooms in South Korea over a three-month period, collecting data on indoor and outdoor temperature, humidity, and CO2 levels. Five machine learning algorithms, including Linear Regression (LR), Random Forest (RF), Gradient Boosting Regression (GBR), Multi-Layer Perceptron (MLP), and Long Short-Term Memory neural networks (LSTM), were applied to compare models of indoor occupancy. The results demonstrate that, among the five machine learning models evaluated, the LSTM model outperforms the others, achieving an RMSE of 3.43. This result indicates a close match between predicted and actual indoor occupancy based on CO2 concentration. The integration of a multivariate multi-step input method further enhances its accuracy, making it suitable for a variety of real-world scenarios in indoor occupancy prediction. This study reveals that using processed data as input sources leads to improved prediction performance for indoor occupant states. Importantly, this work does not infringe on biometric information, such as human image privacy, and relies on minimal measurement data. Furthermore, it not only emphasizes the model's feasibility and practicality in predicting indoor occupancy but also its potential in HVAC system automation, building energy conservation, and indoor environmental management. This study offers guidance and support for the advancement of smart cities and intelligent buildings in the future.

Energy-saving potential of fresh air management using camera-based indoor occupancy positioning system in public open space

The energy consumption of fresh air systems constitutes a significant proportion of the energy consumption of heating, ventilation, and air-conditioning system therefore, energy savings for fresh air handling units are essential. Typically, fresh air is supplied to a room, regardless of whether the space is occupied. Owing to the development of machine vision technology, camera-based indoor occupancy positioning systems that can provide the correct number and location of indoor occupancies have been developed. Thus, energy conservation for fresh air-handling units can be achieved by not serving vacant areas or by assuming a design load in the working zones. In this study, an occupancy position-oriented fresh air (OPFA) system is proposed to supply fresh air more directly to an occupied zone, thus conserving energy. To determine the location of indoor occupants, a stereo camera-based indoor occupancy positioning system is developed, and its accuracy is verified to be within 50 cm; furthermore, it can operate at only 30 W. To determine the annual energy consumption of the OPFA system, the positions of occupants in a classroom are monitored and recorded for 8 months. Subsequently, the typical occupancy patterns of the areas of interest are extracted via hierarchical clustering analysis. The results show that the total occupied time in the classroom is only 50.6% of the opening time. The most typical usage scenario is a self-study scenario with fewer than three persons. Therefore, typical operation strategies for a fresh air handling unit can be designed based on these typical occupancy scenarios, which can potentially conserve more than one-half of the supplied fresh air volume. The performance of the system is numerically investigated and compared with that of a conventional system. The result shows that the system reduces the total fresh air volume and energy consumption by 60.2% and 67.7%, respectively. This study offers a paradigm for efficiently reducing the energy usage of fresh air systems in multipurpose buildings based on occupancy positions to identify typical indoor occupancy patterns, thus allowing the operation strategies of ventilation systems to be optimized.

A Cost-Effective System for Indoor Three-Dimensional Occupant Positioning and Trajectory Reconstruction

Accurate indoor occupancy information extraction plays a crucial role in building energy conservation. Vision-based methods are popularly used for occupancy information extraction because of their high accuracy. However, previous vision-based methods either only provide 2D occupancy information or require expensive equipment. In this paper, we propose a cost-effective indoor occupancy information extraction system that estimates occupant positions and trajectories in 3D using a single RGB camera. The proposed system provides an inverse proportional model to estimate the distance between a human head and the camera according to pixel-heights of human heads, eliminating the dependence on expensive depth sensors. The 3D position coordinates of human heads are calculated based on the above model. The proposed system also associates the 3D position coordinates of human heads with human tracking results by assigning the 3D coordinates of human heads to the corresponding human IDs from a tracking module, obtaining the 3D trajectory of each person. Experimental results demonstrate that the proposed system successfully calculates accurate 3D positions and trajectories of indoor occupants with only one surveillance camera. In conclusion, the proposed system is a low-cost and high-accuracy indoor occupancy information extraction system that has high potential in reducing building energy consumption.

Comparative analysis of heating characteristics of convective-radiant systems using various terminal air source heat pumps

The escalating demand for efficient heating solutions in hot summer and cold winter area has directed substantial attention towards convective-radiant combined systems. These systems offer enhanced comfort while conserving energy. In the context of office buildings, it becomes imperative to consider structural attributes. Under identical operational strategies, disparities in energy consumption and thermal comfort arise from variations in water supply temperature and terminal configurations. Here, we have established a dedicated laboratory to examine convective-radiant heating systems utilizing air source heat pumps. The primary objective is to analyze the impact of distinct heating terminal types and water supply temperatures on indoor thermal comfort, energy consumption, and energy efficiency. By analyzing the dynamics of indoor parameter, and interrelations among air temperature, average radiant temperature, operative temperature, and comfort levels, optimal strategies are determined. Empirical findings demonstrate that independently opening the Fan coil convection system (FC) leads to significant fluctuations in air temperature and creates an unstable heat environment. However, the Radiant floor system (RF) and Convective-radiant combined heating systems (FC + RF) exhibit superior stability in maintaining indoor air temperature. Compared among the three systems, while FC displays the lowest energy consumption upon activation, it compromises comfort levels. Remarkably, there is a strong relationship between operative temperature and Predicted Mean Vote (PMV) for the three systems, with high correlation coefficients of 0.998, 0.999, and 0.997, respectively. Conversely, the association between air temperature and PMV proves weak, with correlation coefficients of only 0.415, 0.469, and 0.841. Consequently, it is advised to prioritize operative temperature as the benchmark for winter air temperature control. For sustained adherence to Level II comfort zone criteria during indoor occupancy, it is recommended to employ (FC + RF) or RF for winter heating. Notably, energy savings of 26.87 %, 30.07 %, and 32.86 % are respectively achieved for (FC + RF), FC, and RF configurations when utilizing a water supply temperature of 35℃ as opposed to 45℃. Hence, the adoption of low-temperature water heating during winter is strongly advocated.

Occupancy estimation using IoT sensors and machine learning: Incorporating ventilation system operating state and preprocessed differential pressure data

Indoor occupancy should be measured to reduce energy consumption in buildings and predict infection risk. Various studies used machine learning to measure occupancy using carbon dioxide concentration, Wi-Fi probes, and camera image data. The carbon dioxide concentration depends on factors such as whether doors and windows are open and whether ventilation systems are running. Carbon dioxide concentrations fluctuate depending on variables, even with the same occupants in the room. We estimated the number of occupants using indoor environmental data for considering the changes in the carbon dioxide concentration caused by these variables. The differential pressure and operating state of a ventilation system were used as input variables for machine learning. Data were acquired from a living lab, which is used for seminars, lectures, etc. For up to 50 people. We used 85,366 data for training and 8418 data for validation without training. The accuracies of Artificial Neural Network (ANN), random forest, and support vector machine models were compared for different input variables. This study considered the carbon dioxide concentration fluctuation due to leakage as an input variable coupled with differential pressure sensor data. We compared whether the accuracy increases when differential pressure data are used. In the ANN model, the lowest root mean squared error is obtained when the carbon dioxide concentration, differential pressure data, and operating state of the ventilation system are used as input variables. In the future, we plan to increase the accuracy by utilizing various Internet of Things sensors and adopt diversify the input variables.

Adaptive HVAC System Based on Fuzzy Controller Approach

Heating, ventilation, and air conditioning (HVAC) system performance research has received much attention in recent years. Many researchers suggest a set of appropriate fuzzy inputs that can be used to design fuzzy rules-based smart thermostats or controllers that can respond to demand-controlled ventilation, which in turn optimizes HVAC energy usage and provides satisfactory indoor temperatures. Previous research has focused on limited input parameters, such as indoor occupancy status, ambient temperature, and humidity constraints, which cannot efficiently and precisely manage thermal comfort. Hence, this study proposes a novel fuzzy controller with additional input parameters to keep indoor thermal comfort consistent with the corresponding number of occupants. The process employs an automatic fuzzy rule generation method to simplify the task of generating rules in the fuzzy inference system (FIS) using Mamdani FIS. A design-builder is used for modeling the HVAC systems. Local weather data were used to conduct simulations via EnergyPlus. The thermal comfort analysis using the Fanger model for three different scenarios shows that the proposed FIS controller can successfully respond to the indoor comfort variation in all possible scenarios and ensure a satisfactory comfort level. The proposed method demonstrates up to 50% energy savings if occupants do not worry about comfort.

Physisorption-Mediated Charge Transfer in TiS2 Nanodiscs: A Room Temperature Sensor for Highly Sensitive and Reversible Carbon Dioxide Detection.

Real-time and high-performance monitoring of trace carbon dioxide (CO2) has become a necessity due to its substantial impact on the global climate, human health, indoor occupancy, and crop productivity. Two-dimensional materials such as transition metal dichalcogenides (TMDs) have gained significant interest in gas sensing applications owing to their intrinsically high surface-to-volume ratio. However, the research has been limited to prominent TMDs such as WS2 and MoS2. Specifically, the chemiresistive sensing performance of titanium disulfide (TiS2) has rarely been investigated. We present an electric-field-assisted TiS2 nanodisc assembly for the fabrication of a low-cost, low-power CO2 gas sensor based on charge transfer between physisorbed CO2 analyte molecules and TiS2 nanodiscs operating at room temperature. The physiochemical properties of the synthesized TiS2 nanodiscs were investigated via scanning electron microscopy (SEM), electron diffraction spectroscopy (EDS), transmission electron microscopy (TEM), X-ray diffraction (XRD), and Raman spectroscopy. The fabricated sensor demonstrated an ultra-high sensor response of 60%, a fast response time of 37 s toward 500 ppm CO2 gas, and the lowest detection limit of 5 ppm under ambient conditions. The low adsorption energies and vdW interaction between CO2 molecules and TiS2 resulted in easy desorption, allowing the sensor to self-recover without the need for external stimuli, which is hardly been witnessed in other 2D material analogues. Furthermore, the sensor has excellent reproducibility and stability for successive analyte exposures, as well as excellent selectivity for CO2 over other interfering gases. This reported sensor based on 2D TMDs is the first of its type to integrate such a broad range of sensor characteristics (such as high sensor response and sensitivity, rapid response and recovery times, a high signal-to-noise ratio, and excellent selectivity at room temperature) into a single, revolutionary device for CO2 detection.

Device-Free Human Motion Detection Using Single Link WiFi Channel Measurements for Building Energy Management

Human motion is a vital parameter signalling indoor occupancy in occupant centric building energy management systems. In this letter, a WiFi based device free sensing model employing ESP32-a low cost embedded IoT device, is proposed as a suitable retrofit for human motion sensing in new and existing buildings. A low complexity feature set derived from WiFi received signal strength indicator and channel state information samples collected by ESP32 is utilized for training ensemble machine learning models. The proposed model shows an accuracy of up to 99.4% in classifying human motion in 3 different rooms of a residential building, with a single WiFi access point. The proposed model can be employed for motion driven smart load control and for a typical residential occupancy scenario, this approach exhibits a potential monthly electricity savings of up to 66.26% and a three times reduction in CO2 emissions.

Improving Indoor Occupancy Detection Accuracy of the SLEEPIR Sensor Using LSTM Models

We recently developed a synchronized low energy electronically chopped passive infrared (SLEEPIR) sensor node to detect stationary and moving occupants. It uses a liquid crystal shutter to modulate the infrared signal received by a traditional passive infrared (PIR) sensor and thus enables its capability to detect stationary occupants. However, the detection accuracy of the SLEEPIR sensor can be easily influenced by infrared environmental disturbances. To address this problem, in this paper, we propose two Long Short-Term Memory (LSTM) models to filter infrared environmental disturbance, named baseline LSTM (Base.LSTM) and statistical LSTM (Stat.LSTM). They use the sensor node raw output and statistical features as their respective input. For comparison, we propose two other models: the occupancy state switch detection (SSD) algorithm that directly uses a predetermined threshold voltage value to classify the occupancy state and its status change; and the multi-layer perception (MLP) classifier with statistical feature inputs (Stat.ML). To validate their detection performance, we designed two testing scenarios in different environment settings: (i) Daily occupancy tests and (ii) EDGE case tests. The first scenario intends to restore complex real-life environmental situations as much as possible in the lab and apartment rooms. The second scenario aims to verify their detection accuracy under different environmental temperatures. This scenario also considers different occupancy postures, such as lying down. Experimental results show that the detection accuracy of both LSTM models (> 95%) in both testing scenarios outperforms that of the SSD (around 82% to 94%) and the Stat.ML(around 80% to 90%).

Novel Robust On-Line Indoor Occupancy Counting System Using mmWave Radar

In this work, a novel robust on-line indoor occupancy counting approach is proposed using the millimeter wave (mmWave) frequency-modulated continuous-wave (FMCW) radar. The acquired radar data can be represented as sparse three-dimensional (3D) radar point-clouds. The conventional indoor occupancy-counting schemes suffer from various types of errors and uncertainties emerging from mmWave radar sensors. Thus, we propose a novel feature-extraction strategy to obtain the robust features for training the machine-learning driven occupancy-counter. We formulate the underlying indoor occupancy-counting problem as the multi-classification problem such that the k nearest neighbor (KNN) classifier is adopted to identify the number of occupants on line. Experimental results from realworld data demonstrate that our proposed new approach leads to a promising classification-accuracy of 95.8% for indoor occupancy counting. In the comparative study based on the realworld data, our proposed novel indoor occupancy-counting method greatly outperforms other existing schemes.

Indoor occupancy detection and counting system based on boosting algorithm using different sensor data

ABSTRACT This research examined how to estimate a room's occupancy utilizing datasets using data collected from various sensors with boosting methods. Different sensors were used to extract features that indicate the occupancy level and the relative changes (PIR motion detectors, CO2 sensors, plug loads, lighting loads, electricity use and Wi-Fi access points). The proposed two-layer method increases the reliability and accuracy of occupancy detection. Five machine learning models were trained and tested using data on occupancy (Logistic Regression, Multi-Layer Perceptron Classifier, Support Vector Machine, Light Gradient Boosting Machine and Gradient Boosting). Accuracy, Precision, F1-score, Recall and ROC were the performance parameters used. Thus, using the Python tool, the proposed optimized model gives better detection and estimated accuracy of occupancy in the indoor environment. As a result, data fusion in conjunction with the Light GBM algorithm's gradient boosting-based categorical features and PSO optimization techniques may be used to predict occupancy data with high accuracy and F1-score (both 99%).

A novel occupant-centric stratum ventilation system using computer vision: Occupant detection, thermal comfort, air quality, and energy savings

Traditional ventilation and air conditioning systems typically operate on a predetermined schedule with fixed operating parameters. Occupant-centric control (OCC) strategies have been proposed to reduce system operation energy consumption without sacrificing thermal comfort. Indoor occupancy detection in real time is a critical step in successfully implementing the OCC strategy. Thus, the deep learning-based computer vision method was adopted in the first step of the study, and the detection performance and camera position were analyzed in an office scenario. Next, the proposed OCC strategy was used to regulate the supply air parameters and outdoor air volume in stratum ventilation based on the monitored occupant number. The traditional static control strategy was then compared to two control strategies: constant air volume and variable air volume. Occupant detection performance results showed the mean NRMSD for the five most common relative positions of the occupants and camera was 0.1109, with sitting back to camera having the lowest accuracy. Subjective response results demonstrated that, when compared to the traditional control strategy, thermal comfort was improved by 43%–73%, perceived air quality was maintained at an acceptable level, CO2 concentration was less than 700 ppm, and energy could be saved by 2.3%–8.1%. Furthermore, the lower the occupancy, the greater the improvement in comfort and the greater the energy savings. This research focused on how the stratum ventilation system responds to dynamic changes in occupancy and provided insights into reducing unnecessary energy waste while maintaining comfort.

Indoor Occupancy Sensing via Networked Nodes (2012–2022): A Review

In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human presence within more expansive areas while delivering enhanced accuracy compared to a system that relies on stand-alone sensor nodes. The present work reviews the studies from 2012 to 2022 that use networked sensor nodes to detect indoor occupancy, focusing on PIR-based sensors. Methods are compared based on pivotal ADPs that play a significant role in selecting an occupancy detection system for applications such as Health and Safety or occupant comfort. These parameters include accuracy, information requirement, maximum sensor failure and minimum observation rate, and feasible detection area. We briefly describe the overview of occupancy detection criteria used by each study and introduce a metric called “sensor node deployment density” through our analysis. This metric captures the strength of network-level data filtering and fusion algorithms found in the literature. It is hinged on the fact that a robust occupancy estimation algorithm requires a minimal number of nodes to estimate occupancy. This review only focuses on the occupancy estimation models for networked sensor nodes. It thus provides a standardized insight into networked nodes’ occupancy sensing pipelines, which employ data fusion strategies, network-level machine learning algorithms, and occupancy estimation algorithms. This review thus helps determine the suitability of the reviewed methods to a standard set of application areas by analyzing their gaps.