# Indoor Occupancy Sensing via Networked Nodes (2012-2022): A Review
## 5 Solutions to Application Mapping  
This review aims to establish the suitability of state-of-the-art networked sensor nodes-based occupancy estimation solutions to occupancy detection application areas. It is critical to mention that the sensor nodes can be exposed to phenomena that interfere with sensor measurements. The phenomena can include pronounced variations of temperature, pressure, radiation, IR shielding [27], EM shielding [28], IR noise [5], and EM noise [29]. In short, sensor measurements are error prone. Data fusion techniques have been widely employed in the literature to overcome such errors. Data fusion is \"the use of techniques that combine data from multiple sources and gather this information to achieve inferences\" [30]. The inferences are expected to be more accurate and robust than if these were achieved via simple aggregation techniques, such as average, maximum, minimum, or a union of outputs of multiple sensor nodes. Moreover, strong inferences can be achieved through networked sensor nodes whenever a node-level communication breakdown occurs as these may observe common observation zones. In practice, networked sensor nodes commonly suffer from communication breakdowns. The literature contains data fusion methods that can be centralized or distributed systems. In centralized systems, raw sensor data is sent to a central hub or sink node, and the data fusion method processing is performed at the central node. In distributed systems, the distributed components of the fusion method would execute on distributed nodes in the design, with each node utilizing its local data. It is well known that data fusion caters to the spatial and temporal coverage blind spots of sensor nodes [5]. For occupancy sensors, the spatial coverage of a sensor usually means the sensor's FoV or its effective volumetric detection range. Their temporal coverage usually depends on the sensor's sampling rate, node's duty cycle, and communication delays [30]. Table 4 lists the reviewed methods along with the employed occupancy detection measure, network-level data filtering and fusion techniques, NDD, and the spatial and temporal resolution of the occupancy detection. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Solution**} & **Data Filtering** & \multirow{2}{*}{ \begin{tabular}{c} **Input Data** \\ **and Fusion** **Techniques** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Detection** \\ **Streams** \\ \end{tabular}  } & \multirow{2}{*}{**NDD**} & \multirow{2}{*}{ \begin{tabular}{c} **Spatial/Temporal** \\ **Resolution and** \\ **Average** \\ **Accuracy** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Author Claimed** \\ **Application** \\ **Areas** \\ \end{tabular}  } \\ \hline \multirow{4}{*}{[31]} & \multirow{4}{*}{ \begin{tabular}{c} Bayesian \\ Occupancy \\ Model \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} PIR sensor \\ nodes \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} Bayesian \\ **Probability** \\ computed over \\ historical data \\ and Sensor \\ Model output \\ \end{tabular}  } & \multirow{4}{*}{403 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro\SIUnitSymbolMicro 2}\)/node} & \multirow{4}{*}{\ \begin{tabular}{c} Multiple Zones, \\ 60 s, 71\% \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} Energy and Space \\ Utilization \\ \end{tabular}  } \\ \cline{1-1}  & & & & &  
Some specific observations can be made about the data presented in Table 4.  
a. A solution can only be used in Energy and Space Utilization applications if and only if these are scalable, i.e., NDD is low. Energy and Space utilization is usually measured across an entire commercial or residential unit. Any solution with a relatively high NDD is essentially non-scalable due to additional infrastructure costs. b. Health and Safety and Security applications require high occupant tracking and detection accuracy. The solutions usually achieve this at the expense of high NDD.