# Modeling Trust in IoT Systems for Drinking-Water Management
## 4 Results and Discussion
### Discussion
#### 4.2.4 Comparison with Existing Models in the Literature  
In reviewing previous works, several trust modeling approaches for the Internet of Things (IoT) have been studied. The approach described in \"A model-driven approach to ensure trust in the IoT\" primarily focuses on trust modeling within the system development lifecycle (SDLC), which may limit its ability to effectively capture and mitigate real-time security risks, particularly in critical environments such as IoT-based water-treatment plants [25]. Similarly, other models, such as the one presented in \"A conceptual trust model for the Internet of Things interactions\", suffer from limitations related to their generality and abstraction, making it difficult to adapt to the specific needs and security requirements of critical infrastructures like water-treatment plants [23]. Furthermore, approaches like the one described in \"A dynamic trust model in the Internet of Things\" may be limited by their reliance on historical data to predict trust, which can be ineffective in unforeseen situations or against sophisticated attacks [24]. Additionally, models such as the one presented in \"Modeling trust dynamics in the Internet of Things\" may lack consideration for the specific requirements and dynamics of critical infrastructures such as water-treatment plants, requiring deeper customization and integration to ensure adequate protection against potential threats [25]. As for the CTRUST approach, its limitations lie in its lack of precise modeling of trust degradation and weighted recommendations, as well as its rigidity in defining trust parameters. In comparison, our trust modeling approach for an IoT water-treatment plant acknowledges the dynamic and non-transitive nature of trust, offering a more suitable perspective by recognizing the specific complexity of trust relationships among critical infrastructure elements [28]. Our comparison between existing models and our approach revolves around four key aspects. First, flexibility holds paramount importance in the domain of drinking-water treatment, where conditions within treatment plants can vary significantly. The ability to adapt is crucial for dynamically adjusting trust factors and weights to suit the diverse conditions encountered. Similarly, temporal dynamics play a pivotal role as trust relationships undergo evolution over time due to various factors. Incorporating these temporal variations facilitates improved adaptation and more effective management of water-treatment systems. See Table 10 \begin{table}
\begin{tabular}{c c} \hline \hline
**Domain** & **Criteria** \\ \hline Pumps & Energy efficiency, operational reliability \\ Heat Exchangers & Corrosion resistance, thermal transfer efficiency \\ Pressure Boosting Systems & Automatic control, outlet pressure \\ Sand Filters & Filtration efficiency, maintenance frequency \\ Clarifiers & Treatment capacity, clarification efficiency \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Criteria for Water-Treatment Machines (ISO 24511-26) [34; 35; 36; 37; 38; 39; 40]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Aspect** & **Criteria** \\ \hline Leadership Commitment & Commitment to quality, health, and safety \\ Resource Management & Competence, training, hazard identification \\ Process Control & Planning, operation, monitoring \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Criteria for Personnel (ISO 9001, ISO 45001 [41], ISO 14001, ISO 50001 [42]). Additionally, adherence to ISO standards is imperative to ensure objectivity and transparency in the evaluation of trust models. In the water-treatment field, where quality and safety are top priorities, aligning models with recognized standards helps alleviate potential biases and establishes consistent and dependable evaluation criteria. Finally, model robustness is indispensable in an environment where unexpected scenarios and abrupt changes can arise. Water-treatment plants must maintain operation even in abnormal situations.