# Indoor Occupancy Sensing via Networked Nodes (2012-2022): A Review
## 6 Accuracy and Suitability Analysis  
There are broad fusion implementation categories mentioned in [10], namely: (1) analytical methods, (2) knowledge-based methods, and (3) data-driven methods. Each of these \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Solution**} & \multirow{2}{*}{\begin{tabular}{c} **Data Filtering** \\ **and Fusion** \\ **Techniques** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Input Data** \\ **Streams** \\ \end{tabular} } & \multirow{2}{*}{**Detection**} & \multirow{2}{*}{**NDD**} & \multicolumn{2}{c}{**Spatial/Temporal**} & \multirow{2}{*}{\begin{tabular}{c} **Author Claimed** \\ **Application** \\ **Areas** \\ \end{tabular} } \\  & & & & & & & \\ \multirow{4}{*}{[39]} & \multirow{4}{*}{\begin{tabular}{c} Gaussian \\ Distribution \\ Model \\ \end{tabular} } & \multirow{4}{*}{PIR} & \begin{tabular}{c} Gaussian \\ distribution \\ used to fit the \\ occupancy \\ \end{tabular} & & & & & \\ \cline{3-5}  & & & & & & & \\ \cline{3-5}  & & & & & & & \\ \cline{3-5}  & & & & & & & \\ \hline \multirow{4}{*}{[40]} & \multirow{4}{*}{\begin{tabular}{c} Multi-sensor \\ Aggregation \\ \end{tabular} } & \multirow{4}{*}{PIR} & \begin{tabular}{c} Aggregation of \\ PIR triggers \\ over 5 min \\ duration \\ \end{tabular} & \multirow{4}{*}{122 ft\({}^{2}\)/node} & \multirow{4}{*}{\begin{tabular}{c} Multiple Zones, \\ 5 min, 87.5\% \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Energy and Space \\ Utilization \\ \end{tabular} } \\  & & & & & & \\ \hline \multirow{4}{*}{[42]} & \multirow{4}{*}{\begin{tabular}{c} Inhomogeneous \\ HMM \\ \end{tabular} } & \multirow{4}{*}{PIR} & \begin{tabular}{c} Posterior \\ probability \\ evaluated via \\ Maximum a \\ posteriori and \\ \end{tabular} & \multirow{4}{*}{18 ft\({}^{2}\)/node} & \multirow{4}{*}{\
\begin{tabular}{c} Single Zone, 60 s, \\ 99\% \\ \end{tabular} } &
\begin{tabular}{c} HVAC Control \\ and Occupant \\ Comfort, Energy \\ and Space \\ Utilization \\ \end{tabular} \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._categories has its own set of shortcomings that either emanate from node-level detection errors or are an artifact of the network-level occupancy detection method. The analytical methods study the physical behavior of occupants and its impact on the indoor environment. These methods exploit the relationship between environmental variables and human presence to derive occupancy decisions. For example, Ref. [22] presents an occupancy detection method based on various indoor climate sensor data trajectories. Data from CO2 and VOC sensors were used to evaluate vacancy, while data from PIR sensors were used to judge occupancy. No prior information about the testbed or dataset prerequisites is required by this method. Despite relying on three different sensor modalities, this method reported as much as 43.5% false negatives and 11.8% false positives for a dormitory occupancy scenario. The knowledge-based methods, also known as expert systems, use specialized knowledge represented by rules to solve complex problems. A good example is a Finite State Machine-based State Switch algorithm [43] that utilizes SLEEPIR [44] nodes capable of detecting stationary occupants. Yet, it is not able to robustly handle the node-level detection errors, and a transition to the wrong state would be produced in case a false occupancy determination was made at the node level. Similarly, any network-level aggregation algorithm, e.g., the union of outputs of stand-alone sensor nodes, will fail to handle a false positive detection determination made at the node level. The data-driven methods include the following sub-categories of methods. 1. Statistical and deep learning ML methods
2. Bayesian inference methods
3. HMM-based methods  
ML-based network-level occupancy detection methods that process statistical features extracted from raw sensor-node observations can handle uncertainty, but have limited application due to the pre-requisite of acquiring labeled training dataset [37].