# Indoor Occupancy Sensing via Networked Nodes (2012-2022): A Review
## 6 Accuracy and Suitability Analysis
Moreover, solution accuracy does not meet ADP accuracy criteria, i.e., 75% < 90%. It is a data-driven method and has a dataset pre-requisite. Ref. [5]: Suitable for the author-claimed _HVAC Control and Occupant Comfort_ and _Energy and Space Utilization_ applications. The solution can scale up as the spatial NDD is low, i.e., 364 ft\({}^{2}\)/node. It is a data-driven method and thus requires a labeled dataset to be collected. The method also needs historical sensor data for correlation evaluation. The solution is optimized in terms of node power consumption, local processing at nodes via an IoT device. The ML pre-processing inference is made on an IoT device, which is mentioned to be a CPE. The solution is also alternatively suitable for _Health and Safety applications_ as it meets the desired ADP guidelines for this application area and can track occupancy at room level. For reference, a system level diagram for the solution is illustrated in Figure 4.  
Ref. [36]: Not feasible for the author-claimed _HVAC Control and Occupant Comfort_ application. Algorithm execution platform for the algorithm is a PC or Server. Moreover, solution accuracy does not meet ADP accuracy criteria, i.e., 84.0% < 90%. It is a data-driven method and requires historical sensor data for time-series data correlation evaluation. Figure 4: PF-based human occupancy detection method flow chart. Networked sensor nodes (SLEEPIR) generate voltage, ambient temperature, and PIR data. The voltage is converted to binary occupancy observations via an ML-based thresholding algorithm. The node-level occupancy observations then update a system-level occupancy estimate via a PF. Ref. [41]: Not feasible for the author-claimed _HVAC Control and Occupant Comfort_ application. The solution cannot scale up well with the spatial NDD of 14 ft\({}^{2}\)/node. It is a data-driven method and thus, requires a labeled dataset. Although the method uses a standard protocol by ASHRAE for sensor communication, the regression algorithm execution is not optimized for IoT execution which makes the feasibility of the algorithm questionable to be used as a solution for occupancy detection. Ref. [37]: Suitable for the author-claimed _HVAC Control and Occupant Comfort_ application. The solution can scale up well as the spatial NDD is sufficient to cover an average-sized room, i.e., 140 ft\({}^{2}\)/node. It is a data-driven method; thus, it requires a dataset to be collected pre-deployment. Moreover, a human activities layer is incorporated in the learning model which requires domain knowledge about the occupancy patterns. The solution has matured to the point that it has been implemented over an IoT device. Ref. [7]: Suitable for the author-claimed _HVAC Control and Occupant Comfort_ and _Energy and Space Utilization_ applications. The solution can scale up as the spatial NDD is low, i.e., 296 ft\({}^{2}\)/node. It is a data-driven method and thus, requires a labeled dataset to be collected. The solution is optimized in terms of node power consumption and local processing at nodes via an IoT device. The ML pre-processing, training and inference, however, is made at a back-end machine. Since a two FFNN is relatively simple to implement over an IoT-compatible ML framework, such as TensorFlow Lite, a case can be made that the solution is suitable for an IoT-based implementation. The solution is also alternatively suitable for _Health and Safety applications_ as it meets the desired ADP guidelines for this application area. Ref. [38]: Not feasible for the author-claimed _HVAC Control and Occupant Comfort_ application. The solution can also not scale up well with the spatial NDD of 54 ft\({}^{2}\)/node. It is an analytical method; thus, it may only require domain knowledge, yet certain thresholds and parameters for Zero Lag Exponential Moving Average algorithm require empirical tuning. The algorithm can be easily ported to IoT for execution which makes the method suitable for IoT execution, but the accuracy needs to meet ADP accuracy criteria, i.e., 77.8% < 90%. Ref.