## 4 Integration of the PCS with the ASSIST-IoT Reference Architecture
### Concept of AI-Based Personalized Cooling  
Ensuring the privacy of the user's biometric parameters in the system (Figure 11) is a very challenging task. It should be stressed here that one of the main requirements for the system is for it to be able to personalize the cooling power based on the user's preferences. Although there is some literature on preserving the privacy of users in personalized learning systems [31; 32; 33; 34], those studies mostly tackle a different formulation of the problem. Usually (e.g., in federated learning systems), we can assume that the model can be trained on a device fully controlled by the user, and then the model's parameters can be sent in a private way to the server (e.g., using differential privacy [35]). This is not the case in the application considered in this work--the user, in fact, does not fully control any device in the system. The PCS itself has very little computational capacity, and thus any work must be deferred to the GWENs. However, the user is not assigned to any specific GWEN--by moving around the construction site, the worker can encounter dozens of different GWENs. Therefore, there must exist some centralized location (a server) that stores the personal preferences of users. Bearing in mind the presented limitations of the use case, an AI system was designed that aims to maximize the users' privacy while retaining the element of personalization (Figure 12). The proposed system hinges on the GWEN not storing any data permanently, which can be guaranteed by adequate routing rules in the Edge Data Broker. The GWEN gathers four types of data: the user identifier (needed for personalization), the cooling power (target variable), weather data describing the environmental conditions, and the user's biometric data. It is the biometric data that are particularly sensitive and thus must not be associated with the user identifier (as depicted in the lower half of Figure 12). We can, however, build two separate models: one which includes the user's identifier but not biometric data, and another which includes the biometric data but not the identifier. This way, the system would be able to learn (1) the user's preferences depending on the outside conditions, and (2) the aggregated preferences of all users depending on the outside conditions and the biometric parameters. This, of course, limits the potential for personalization, but still allows the system to consider all available measurements when making the decision. The presented models can be implemented using any regression  
Figure 11: Overview of the proposed system, integrating the PCS with the rest of the construction site infrastructure, using the ASSIST-IoT reference architecture. technique. Particularly useful should be the k-nearest neighbors algorithm due to its very elastic training procedure (simply adding an observation to the database) and flexibility in the face of non-linear relationships. It should be stressed here that although the separation of the user identifiers and the biometric data does improve the privacy of the users, it could still be compromised. The two models share the target variable and three of their inputs (the weather data). It might be possible to work out the correspondence between the user and their biometric data if there is a direct, one-to-one match between the weather data and the cooling power in both models. Although formally determining the degree of privacy given by such a system is out of the scope of this contribution, additional provisions can be made to increase the privacy level. Firstly, the GWEN can apply noise and bucketing to the observed data independently for each model. This reduces the chance that a direct correspondence between the models can be established, especially when there are many users. Furthermore, the two models can be updated asynchronously. The users can also opt out entirely of their biometric data being collected, thus not contributing to the second model.