# Modeling Trust in IoT Systems for Drinking-Water Management
## 2 Related Work  
Understanding previous research and relevant developments provides crucial insights into the advancements, persistent challenges, and innovative solutions shaping the field of secure IoT systems for drinking-water distribution. This exploration forms the bedrock for our contextual understanding, guiding the formulation of new approaches and the resolution of outstanding issues. In this section, we delve into past research milestones, illuminating the groundwork for our contributions and emphasizing the significance of our proposed approach. In the nascent stages of applying the Internet of Things (IoT) to water management, the emphasis was predominantly on establishing connectivity and facilitating data collection. Early implementations focused on creating networks capable of gathering information about various aspects of water resources. However, security considerations often took a backseat during this period, with primary attention directed towards functional system establishment rather than safeguarding against potential threats [5; 6; 7]. The escalating sophistication of cyber-attacks has catalyzed a paradigm shift in the architecture of IoT systems, particularly regarding security. Evolving security protocols now address the unique demands of industrial applications, including the management of drinking water. Protocols such as TLS/SSL have emerged as indispensable tools for securing communications across diverse IoT network components, therefore bolstering secure communication channels and ensuring data integrity [8; 9]. Incidents targeting critical infrastructures, such as water-treatment facilities, have underscored the imperative to enhance security measures. Heightened awareness of potential threats to water quality and resource availability has spurred research efforts and the development of more resilient security mechanisms [10; 11; 12]. The establishment of standards and regulations tailored to IoT system security, particularly within critical sectors like water management, has played a pivotal role in shaping trust management frameworks. These standards, often formulated in response to identified vulnerabilities, offer guidelines for implementing standardized security practices [13; 14]. In recent times, attention has shifted towards leveraging artificial intelligence (AI) to enhance IoT system security. Ongoing research explores AI's potential to detect anomalies in data, preempt attacks, and bolster the resilience of water management systems. The integration of AI represents a strategic leap towards proactive security, empowering systems to respond effectively to emerging threats [15; 16; 17]. Moreover, recent advancements in utilizing artificial intelligence (AI) have shown promising outcomes in preventing malfunctions and failures in critical industrial equipment. For instance, research demonstrates the application of machine learning algorithms, including Support Vector Machine (SVM) and Multilayer Perceptron (MLP), for fault prediction in centrifugal pumps within the oil and gas industry [18]. Similarly, innovative approaches leveraging deep transfer learning techniques have been proposed for enhanced fault diagnosis of monoblock centrifugal pumps (MCP) [19]. Converting vibration signals into spectrogram images and utilizing pre-trained neural networks such as AlexNet offer efficient and precise solutions for fault detection and diagnosis in MCPs, therefore enhancing reliability and maintenance practices across various industrial settings. Furthermore, in water pump bearings, novel feature extraction and fault detection techniques utilizing extreme gradient boosting (XGB) classifiers have achieved notable success in reliably detecting and classifying minor bearing faults with high accuracy [20]. This signifies a significant advancement in the field, addressing the ongoing challenge of cost-effective fault detection in centrifugal water pumps and underlining the potential of AI-driven solutions in industrial equipment maintenance.