# Indoor Occupancy Sensing via Networked Nodes (2012-2022): A Review
## 1 Introduction  
In the last decade, there has been a considerable shift from high-performance and energy-efficient buildings towards co-optimizing occupant comfort and building energy demand [1, 2]. However, multiple studies show that a significant proportion of occupants in U.S. office buildings (up to 75%) are dissatisfied with their thermal environment [3, 4]. The primary motivation behind this review article is to assess the suitability of networked node occupancy detection methods for a standard set of application areas. Our focus on networked nodes-based occupancy sensing methods derives from existing stand-alone occupancy sensors providing limited performance that can cause false negatives (switching off heating and lights during occupancy), resulting in occupant dissatisfaction [2]. Stand-alone occupancy sensor-based methods struggle to achieve the same level of improvement in occupant comfort level compared to networked occupancy sensor nodes while deployed under the same configuration [5]. Among recent review articles [2, 6, 7, 8, 9], an overwhelming majority focus on the methods that are based on stand-alone occupancy sensors. As such, no review exists that is dedicated to an algorithmic aspect of multi-node occupancy sensing [10]. Thus, in the past decade, a marked shift in the trend is observed where agrowing number of methods propose more thorough and accurate occupancy detection models for applications such as HVAC Control and Occupant Comfort [11], involving an interconnected network of occupancy sensors. These models are aware of the room connectivity, time-sensitive occupancy behavior, and expected use of each space under observation. The use of multiple sensing nodes is a common technique for improving detection performance. A few examples of how this can be achieved include extracting ML network-level features from a multivariate raw-sensor data [12] or determining occupancy via a PF [5] that fuses the node-level ML inference to estimate an occupancy belief. We also observed that among all the reviewed articles, a basic premise is missing, i.e., the actual occupancy behavior depends upon the building design, sensor node positioning, room connectivity, purpose of each space in a residential or office unit, and occupant priorities, which tend to be highly time sensitive. Thus, any review study that lists out the subjective accuracy measures comparisons in a non-standardized form leaves a certain margin of uncertainty for the intended audience. While there are review studies that use evaluation metrics from the cost [9] of the proposed solution, to the accuracy and the failure rate [13] of the solution, our review study aims to evaluate the occupancy sensing methods for networked sensor nodes from an application's perspective while using suggested standard parameters that help in the evaluation process. Methods are assessed based on the ADPs, i.e., accuracy requirement, information requirement, maximum sensor failure, minimum observation rate, and feasible detection area. While we value the importance of underlying sensor technologies toward greater occupancy detection accuracy, it is not the focus of our review study. We instead focus and comment on the employed occupancy detection measure by the methods, network-level data filtering and fusion techniques, NDD, and the spatial and temporal resolution of occupancy detection. The presented review is novel in assessing the impact, the gaps, and the enhanced accuracy networked-node occupancy detection systems offered. In Section 2, gaps in closely related review articles from the literature are highlighted. Section 3 outlines the methodology for conducting this review. Section 4 identifies occupancy sensing gaps, application areas, and their ADPs based on the searched literature. Section 5 presents various networked node sensor-based occupancy detection solutions and their suitability to the already presented application areas.