# Indoor Occupancy Sensing via Networked Nodes (2012-2022): A Review
## 4 Occupancy Sensing Gaps and ADP Identification  
We first focus on identifying the occupancy detection gaps found in the literature for networked sensor node methods. Table 2 provides a non-exhaustive, but representative list of the most common gaps and the corresponding occupancy detection application area found within a set of representative studies. The gaps identified in Table 2 point  
Figure 2: A phased methodology chart outlines the review strategy. First, occupancy sensing gaps that require networked occupancy sensors and corresponding estimation techniques are identified. ADPs for the application area are identified. Solutions from the literature are explored and an analysis is performed to see whether the solutions are suited to the claimed application area. towards a specific set of incapabilities that are present either within the underlying sensing technologies, the occupancy estimation method, or the communication and integration framework that enables the networking between these sensor nodes. As mentioned, this review only focuses on the shortcomings of the occupancy estimation methods. The impact of underlying sensing technologies and wireless sensor networks' communication reliability are separate topics with dedicated studies [8; 21] in the literature dealing with these topics. The shortcomings present within occupancy estimation methods for any given application area can be overcome through a set of parameters to which the estimation method must conform. We term these parameters ADPs. Table 3 transforms the identified gaps into ADPs with specific application values. Standard documents from various associations and agencies, such as the ASHRAE [22], CEC [23], IBC [24], NFPA [25], and IECC [26], are used in the table. The table lists IoT and Edge AI devices among the potential execution platforms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Application Area** & \multicolumn{2}{c}{**Gaps**} \\ \hline  & a. & Stationary human detection \\  & b. & Real-time occupancy detection \\  & c. & Privacy concerns \\  & d. & Infrastructure overhead \\  & e. & False Negatives \\  & f. & Historical and expected occupancy data required. \\  & g- & Reliability and fault tolerance required \\ \hline  & a. & Stationary human detection \\  & & b. & Real-time occupancy detection \\  & c. & Privacy concerns \\  & d. & Infrastructure overhead \\
[12; 20; 21; 22; 25; 26; 27; 28; 29; 30; 31] & Health and Safety & e. & False positives \\  & f. & Zone-level detection \\  & & g. & Historical and expected occupancy data required. \\  & & h. & High accuracy required \\  & i. & Reliability and fault tolerance required \\ \hline  & & a. & Stationary human detection \\
[23; 25; 26; 32; 33; 34] & Energy and Space Utilization & b. & Occupant count required \\  & c. & Zone level detection \\  & d. & Infrastructure overhead \\ \hline  & & a. & Stationary human detection \\  & & b. & Infrastructure overhead \\  & & c. & Realtime occupant tracking required. \\  & d. & False negatives \\
[35; 36; 37; 38; 39; 40] & Security & e. & High accuracy required \\  & & f. & Zone-level detection \\  & & g. & Infrastructure overhead \\  & & h. & Reliability and fault tolerance required \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Occupancy sensing gaps for Networked Sensor Nodes-Based Estimation Methods. The ADPs listed in Table 3 serve as the suitability criteria when selecting a particular occupancy estimation solution for a specific application. These ADPs also bring to light certain exciting insights. For example, the accuracy requirement for occupancy sensors used for HVAC controls varies depending on the specific application and building type. However, the occupancy sensor accuracy should generally be high enough to correctly detect the presence or absence of occupants in a particular area. The accuracy requirement becomes more stringent in the case of both safety and security applications.