# Indoor Occupancy Sensing via Networked Nodes (2012-2022): A Review
## 7 Discussion and Future Trends  
One of the important ADP indicators used in the suitability analysis is NDD. It is interesting to note here that this density is a simple indicator that is evaluated by dividing the total area of the monitored indoor space by the number of sensor nodes employed by the method. This indicator has no direct relationship with the sensor FoV or range, which is usually significantly smaller than the NDD. There is a list of factors that contributes to determining the NDD. The most impactful ones include the node positioning strategy, estimation method accuracy, network/communication reliability, environment-contributed sensor noise, and the floor plan of the monitored area. Among these factors, the node positioning strategy, estimation method accuracy, and network reliability are the factors that can be optimized to decrease NDD. In effect, NDD can be considered a proposed optimization measure by a method for the node positioning strategy, estimation method accuracy, and network reliability. However, dedicated studies exist for the node-positioning strategy [47] and network reliability [21], but none of the reviewed articles devised their positioning strategy.  
During the review effort, it was noticed that most studies focused on _HVAC Control and Occupant Comfort_ and _Energy and Space Utilization_ applications rather than applications such as _Health and Safety_ and _Security_. This is because the latter have ADPs that require high reliability and accuracy, which is difficult to achieve given the challenging task of occupancy detection and tracking in dynamic environments. Most of the reviewed works attempted to tackle the challenge of highly noise-prone and dynamic environments by adding to the suite of sensor modalities. However, a small minority of methods [7; 38] presented the sensor data responsible for false positives or negatives and proposed consequent solutions to resolve the errors.  
Among the researched literature, one of the significant gaps for data-driven occupancy detection methods was the need for periodic collection of training sets to incorporate novel occupancy scenarios. The dataset also includes ground-truth occupancy data. This is a problem because the collection and labeling of new training datasets are far from ideal tasks for an end user or, in some cases, infeasible. To address this issue, certain studies [48; 49; 50] have suggested unsupervised methods, as these algorithms do not need to label the dataset. Yet, such methods have limited applicability since error-prone prior expert knowledge is used to initialize classes. This knowledge may be based on assumptions or sensor data distribution that may only be valid once the occupancy patterns evolve.  
The future for tracking such a complex issue lies in employing more capable IoT devices, such as Edge AI devices [51], so that on-device ML training and inference can be produced incorporating newer occupancy scenarios. The dedicated field of ODLL [46; 52; 53] offers benefits, such as a local learning approach where occupancy patterns are learned on the fly, thus making such methods suitable for practice. Moreover, privacy-preserving  
Figure 5: Raw data is obtained through infrared array sensors along with a camera to build occupancy transition probability matrix A. The parameter learning process of the IHMM model is introduced and the emission probability matrix B is calculated by Softmax Regression Model. Finally, according to the constructed IHMM, the Forward Algorithm and the Viterbi Algorithm are used to realize the online estimation and offline estimation of the indoor occupancy of a building.  
automated labeling techniques are the flip side of the coin when an OODL approach is used, as the collected dataset also needs to be labeled. The literature needs reliable privacy-preserving techniques, but video or image-based automated yet privacy-compromising ground-truth collection techniques [54; 55] can be found.