# A Neural-Symbolic Approach to Extract Trust Patterns in IoT Scenarios
## 6 Conclusions  
An important emerging aspect in IoT smart object communities is the representation of mutual trust among community members. To address the limitations of current approaches in the literature, which often struggle to capture complex trust relationships in IoT smart object communities, we previously developed a model called the T-pattern. This model maintains trust information through a weighted graph, where nodes represent trust dimensions and edges represent relationships between these dimensions. The strength of the T-pattern model lies in its ability to derive one unknown trust dimension from another by leveraging their interdependence. To validate the effectiveness of T-patterns in inferring trust dimensions, this paper presents experiments conducted with an implementation of this model that uses a neural-symbolic approach as inference engine, which is based on CILIOS. These experiments, designed to estimate trust values for three specific trust aspects--honesty, safety, and competence--based on trustworthiness values, demonstrate the effectiveness and accuracy of this T-pattern implementation in reliably inferring trust. We note that the calculation of trust measures is the hard part of the presented approach and that the use of neural networks is performed offline. However, we have shown how the training of the model can be executed in the background, while the agents interact in the IoT scenario, using the cloud level of the IoT system without interfering with the ordinary agent processes. The trained model is used when the training is completed and the simulation we have performed show that its exploitation is useful to improve the capability of the system to accurately determine the trust of the different smart objects. Building on this line of research, after having verified the potentiality of our T-pattern model, our future studies will focus on implementing the proposed model by adopting new inference engines and considering additional scenarios. Moreover, for a more exhaustive analysis, we aim to evaluate our model under different error metrics and assessing its effectiveness with respect to other issues like computational efficiency (e.g., inference time and memory usage), robustness under noise or malicious input, and sensitivity to parameter changes. Finally, we highlight that our proposal to apply T-patterns to the IoT environment is the first attempt we make at applying our trust model to a complex and distributed Information Systems scenario, and the simulations we have performed should be considered only as a verification of the feasibility of the idea. In our ongoing research, we are planning to apply the T-pattern model to a real IoT environment, directly observing the behavior of smart objects when interacting with each other, constructing the T-patterns based on these observations, and then computing the effectiveness of the model. Moving from simulations to a real environment implies facing novel issues that we did not deal with in this paper, as the management of real-word data noise and the scalability of the system with respect to the dimensions of the smart object population. Secondly, we highlight that the architecture of our system, which is delegated to the cloud-level construction of T-patterns with an inference engine that is used in the background with respect to other ordinary IoT activities, possess the capability to integrate with edge or fog computing frameworks as a system for performing real-time trust estimation. Moreover, it is also possible to study the possibility of combining T-pattern with reinforcement learning integration to create hybrid trust models that might enhance inference accuracy levels. **Author Contributions:** All authors collaborated equally on this article. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported in part by the Italian Ministry of University and Research (MUR) Project \"T-LADIES\" under Grant PRIN 2020 TL3X8X and in part by Pia.ce.ri.