# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 5 Discussion: Application, Challenges and Future Perspectives
### Optimal Placement of Sensors in Buildings  
In addition to the most conducive selection of sensor devices, their optimal placement within buildings is equally important. Generally, a larger number of sensors and monitoring technologies are preferred as this provides a more accurate representation of the various physical parameters inside the building. However, this undoubtedly has a direct impact on critical factors such as cost, timelines and complexity (due to additional installations), deployment, commissioning, maintenance and data processing required as part of this process. To address this challenge of reducing the number of deployed sensors and making best use of the space available, several optimization strategies and mathematical models for sensor placement have been presented in recent years. [PERSON] et al. [118] proposed a methodology for the placement of indoor air quality sensors for air contamination prevention, firstly, by formulating a multi-objective optimization problem for minimizing sensor cost, average and worst-case impact damage corresponding to a set of contamination event scenarios, and secondly, by constructing a set of probability-distribution-representative contamination scenarios through grid and random sampling to compute the overall impact of each scenario. The application of the proposed methodology on two different buildings, i.e., a five-room and a 14-room building, and a comparison of the average and worst-case impact showed that as the number of installed sensors is increased, the change in the impact risk objectives is reduced and may not be significant. [PERSON] et al. demonstrated in [119] that by adopting cluster algorithms, data loss approach, and the Pareto principle (also referred to as the 80/20 rule, i.e., for many events 80% of the effects come from 20% of the causes), the optimal number and location of light, humidity and temperature sensors in their case study building could be determined. The systematic approach adopted by the authors (shown in Figure 5a) revealed that the number of deployed sensors can be reduced to 20%, (i.e., from 31 down to 6) with minimum loss of information. Other studies using techniques such as a maximum-likelihood estimator [120], Fisher information matrix [121] and Information Entropy Norm [122] have also proposed sensor placement strategies. However, each of these mentioned techniques determine the placement of sensors in a heuristic manner which does not necessarily provide guaranteed optimum generalizable solutions. In ref. [123], [PERSON] proposed a generalized optimal sensor placement strategy for minimum effective sensors by considering four sensors to be placed in a 16-room building with six different typically used building topologies given below:  
* Rows--two rows of rooms in a corridor. * Star--rooms meet at a central point. * Circular--all rooms have two neighbours and surround a central space. * Island--Similar to circle but with rooms in the central space. * Compact-Grid--All rooms are connected with no corridors. * Dispersed-Grid--All rooms are disconnected by a corridor. The best- and worst-case placement results for this study have been depicted in Figure 5b, where it is shown that it is more favorable to place sensors in central positions within the building as opposed to dispersed positions. The Row and Star topologies highlight that it is favorable to place sensors in rooms with the highest number of connected neighbours to minimize the number of neighbours that do not have sensors, reducing the complexity of correction-less predictions, and to offer an overall systematic placement to maximize the coverage. The Circular, Compact-Grid (c-grid) and Dispersed-Grid (d-grid) topologies show that placement is favorable where higher connections to corridors exist as opposed to outside walls, hence supporting the placement of sensors in more typical rooms (as there are, on average, more inner walls per room than outer).