# Indoor Occupancy Sensing via Networked Nodes (2012-2022): A Review
## 6 Accuracy and Suitability Analysis
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Solution** & **ADPs** \\ \hline
[36] & **Accuracy: 84.0\%** \\
**Information requirement:** time-series data correlations need to be evaluated pre-deployment. \\ Execution Platforms: wireless measurement nodes, PC/Server \\ Sensor observation rate: 20 s \\ Failure rate: Unspecified PIR sensor \\ Detection Area: \textless{}50 \(\text{ft}^{2}/\text{node}\) \\ \hline
[41] & **Accuracy: 94.9\%** \\
**Information requirement:** Labeled dataset for ML \\ Execution Platforms: BACnet TM for sensor connectivity, R on Workstation \\ Sensor observation rate: 60 s \\ Failure rate: Unspecified PIR sensor \\ Detection Area: 14 ft\({}^{2}/\text{node}\) \\ \hline
[37] & **Accuracy: 99.0\%** \\
**Information requirement:** Domain knowledge, Labeled dataset for ML \\ Execution Platforms: NM Compact DAQ, scikit-learn on ARM based Beaglebone Black SoC \\ Sensor observation rate: Variable \\ Failure rate: Unspecified PIR sensor \\ Detection Area: 140 ft\({}^{2}/\text{node}\) \\ \hline
[7] & **Accuracy: 94.3\%** \\
**Information requirement:** Labeled dataset for ML \\ Execution Platforms: Arduino Uno, ARM based Kerlink® IoT Wirnet 868 Station \\ Sensor observation rate: 60 s \\ Failure rate: \textgreater{}10000 h (Panasonic® PaPIRs EKMB) \\ Detection Area: 296 ft\({}^{2}/\text{node}\) \\ \hline
[38] & **Accuracy: 77.8\%** \\
**Information requirement:** Some method parameters and thresholds are set empirically for each sensor node. \\ Execution Platforms: Arduino Mega, PC/Server \\ Sensor observation rate: 5 min \\ Failure rate: High MTBF as per datasheet (RE 200 B) \\ Detection Area: 54 ft\({}^{2}/\text{node}\) \\ \hline
[39] & **Accuracy: 85\%** \\
**Information requirement:** Historical sensor data required for past twenty-four days. \\ Execution Platforms: PC/Server \\ Sensor observation rate: 60 min \\ Failure rate: High MTBF as per datasheet (HPC005 infrared people counter) \\ Detection Area: 731 ft\({}^{2}/\text{node}\) \\ \hline
[40] & **Accuracy: 87.5\%** \\
**Information requirement:** No historical data required. \\ Execution Platforms: SmartThings cloud platform, IoT devices \\ Sensor observation rate: 5 min \\ Failure rate: High MTBF as per datasheet (T3L-SS014, IM6001-MTP01, STS-IRM-25) \\ Detection Area: 122 ft\({}^{2}/\text{node}\) \\ \hline
[42] & **Accuracy: 99\%** \\
**Information requirement:** Prior ground-truth and historical sensor data required for parameter training. \\ Execution Platforms: MATLAB 2016a, Pycharm \\ Sensor observation rate: 60 s \\ Failure rate: High MTBF as per datasheet (AMG8853) \\ Detection Area: 18 ft\({}^{2}/\text{node}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Ref. [32]: Suitable for the author claimed _HVAC Control and Occupant Comfort_ application on a smaller scale. The solution cannot scale up well as the spatial NDD is high, i.e., 49 ft\({}^{2}\)/node. It is a data-driven method. Ref. [33]: Not feasible for the author-claimed _HVAC Control and Occupant Comfort_ application even on a smaller scale. This is because the execution platform for the algorithm is a PC or Server, and the implementation is not optimized for an IoT or Edge AI device. In addition, the solution cannot scale up well as the spatial NDD is high, i.e., 28 ft\({}^{2}\)/node. It is a data-driven method and has a dataset pre-requisite. Alternatively, the method is suitable for _Energy and Space Utilization_ applications. Ref. [35]: Not feasible for the author-claimed _HVAC Control and Occupant Comfort_ application. Although the sensor node data is logged via MCU, the ML algorithm execution platform for the algorithm is a PC/Server. Moreover, solution accuracy does not meet ADP accuracy criteria, i.e., 87.6% < 90%. It is a data-driven method and has a dataset pre-requisite. Ref. [34]: Not feasible for the author-claimed _HVAC Control and Occupant Comfort_ application. Sensor data is logged via third party loggers, the ML algorithm execution platform for the algorithm is MATLAB/WEKA on a PC or Server.