# A Neural-Symbolic Approach to Extract Trust Patterns in IoT Scenarios
## 3 The Scenario  
In this Section (for a complete list of symbols used in this paper, please refer to Appendix A), we present the considered environment (\(E\)) populated by smart IoT objects and users. In such a context, let \(o\) represent a smart IoT object and \(O\) denote the set of smart IoT objects living within \(E\). Indeed, in networks with 16,000 smart objects, the average error is approximately 5%. Each object \(o\in O\) is associated with a user and acts on their behalf as a prosumer, both producing and consuming data through interactions with other devices in \(O\).  
To enable smooth interactions within \(E\), smart IoT objects should establish trust in one another concerning one or more _trust issues_, each representing a distinct aspect of their mutual interactions within \(E\). We define \(\Delta=\langle\delta_{1},\delta_{2},\ldots,\delta_{n}\rangle\) as a set of \(n\) trust issues associated with \(O\) in \(E\). Each trust issue \(\delta\) is characterized by properties such as _Expertise, Honesty_, _Security_, and _Reliability_, which hold the following meanings for a generic smart IoT object \(o\):  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Trust** & **Direct Dependencies** & **Uncertainty** \\  & **Interdependence** & **Extraction** & **Treatment** \\ \hline T-Model & Y & Y & Y \\ [33] & N & N & Y \\ [34] & N & N & Y \\ [37] & Y & N & N \\ [36] & Y & N & N \\ [38] & Y & N & Y \\ \hline \hline \end{tabular}
\end{table}
Table 1: Differences between T-model and the other reviewed approaches.  
* _Expertise._ Refers to the level of competence \(o\) has in providing knowledgeable opinions within a specific domain. For example, the \(o\)'s ability to advise its user on corporate stocks.
* _Honesty._ Indicates \(o\)'s commitment to truthful behavior, free from deception or misleading practices.
* _Security._ Relates to \(o\)'s handling of confidential data, including safeguarding it from unauthorized access.
* _Reliability._ Measures the consistency and dependability of the services provided by \(o\), with respect to efficiency and effectiveness. To enable each smart IoT object to quantitatively assess each trust issue in \(\Delta\), we introduce the notion of _confidence_\(\phi\) in a trust aspect within \(E\). We define \(\phi\) with respect to a _group of smart objects_ in \(E\), denoted as \(\Lambda\), which, in some cases, may encompass the entire set \(O\in E\) of smart IoT objects (i.e., \(\Lambda\subseteq O\)). Specifically, the confidence that a smart IoT object \(o_{1}\) assigns to a trust issue \(\delta_{i}\in\Delta\) of another smart IoT object \(o_{2}\) within the group \(\Lambda\) is represented by \(\phi_{o_{1}o_{2},\Lambda}(\delta_{i})\). This confidence can take either (\(i\)) a real value within \([1,5]\), where \(1\) (resp., \(5\)) represents the minimum (resp., maximum) trust level of \(\phi_{o_{1}o_{2},\Lambda}(\delta_{i})\), or (\(ii\)) a value of \(null\) if \(\phi\) has not yet been assessed.  
Moreover, within a group \(\Lambda\subseteq O\), the confidence perceived by the members of \(\Lambda\) concerning a trust issue \(\delta_{i}\) for a smart IoT object \(o_{1}\) (denoted \(\phi_{o_{1}o_{1},\Lambda}(\delta_{i})\)) may serve as the _group reputation_ of \(\Lambda\) with respect to \(\delta_{i}\). Like individual confidence, this value can range within \([1,5]\) or remain \(null\) if it has not yet been evaluated. It is worth noting that if \(\Lambda\) coincides with \(O\), then \(\phi_{o_{1}o_{1},\Lambda}(\delta_{i})\) will represent the _community reputation_ of the all smart IoT objects regarding the trust issue \(\delta_{i}\).