## 2 Related Work
### General Model-Driven IoT Studies
In [41], a solution to address model-driven communication heterogeneity through abstraction layering is discussed. This approach relies on a networking language and a rule-based language for modeling and controlling the behavior of the IoT network. Several studies propose a methodology for a leveraging model-driven and service-oriented architecture (SOA) to develop software components for IoT [42]. They offer varying levels of abstraction for constructing IoT software components using SOA. In IoT, network resources are dynamically allocated using application programming interfaces (APIs). Various studies in the literature adopt the SOA structure [11; 12; 16; 43]. Studies in [11; 12] aimed to provide an executable modeling language and development environment for SOA-based IoT systems. For the SOA structure, a technique consisting of four stages with different levels of abstraction, perspective, detail, and service orientation is proposed in [43]. The conceptual development framework introduced in [14] facilitates the separation of IoT applications into different topics, with support for stakeholders and developers. This framework abstracts the complexities of scalability and heterogeneity. [PERSON] et al. [44] proposed a model-driven approach for defining user interface components and design patterns specific to the IoT domain. In the realm of wireless sensor networks (WSN), operating systems like TinyOS and Contiki have gained widespread adoption, with hardware nodes and software platforms becoming standard. The utilization of model-driven techniques in WSN has proven more effective compared to other IoT domains, with numerous studies addressing this aspect documented in the literature [45; 46; 47; 48]. Highly specialized metamodels are employed in systems engineering, particularly in IoT and embedded systems, including firmware development. [PERSON] et al. [49] introduce a novel associative modeling language designed to integrate numerous custom metamodels, enhancing firmware synthesis for ultra-thin IoT devices through model integration. The literature also encompasses studies focusing on IoT system management [50; 51; 52; 53]. GeneSIS [50] offers a tool-supported framework for continuous management and deployment of IoT systems. It addresses heterogeneity across IoT, edge, and cloud levels, facilitating management and continuous deployment of software systems on corresponding infrastructures. The extended version of GeneSIS, incorporating security and privacy concerns [52], is introduced with risk control mechanisms [54] to enhance reliability. [PERSON] et al. [51] present a model-driven approach to systems management, bridging visualization and automation requirements, offering a successful solution to IoT network and system management challenges. In [55], a MDE tool named CHESSIoT is introduced, which combines high-level visual design languages, software development, security analysis, and deployment approaches for multi-layered IoT systems. [PERSON] et al. [56] propose UCM4 IoT as a modeling environment for uncovering and specifying the requirements of IoT systems. UCM4 IoT takes into account the heterogeneity of IoT systems and provides DSL constructs to model various aspects of IoT. [PERSON] et al. [57] suggest using a DSL called Monitor-IoT, supporting developers to design multi-layer monitoring architectures for IoT systems with high abstraction, expression, and flexibility. A model-driven development approach is defined in [58] for simulating, generating code, and deploying IoT systems. This approach enables the design of complex IoT simulation environments and their deployment without coding. Another DSL-based simulation approach is proposed in [59] for designing, generating code, and simulating IoT systems deployable to the FIWARE infrastructure. [PERSON] and [PERSON] [60] propose IoTMoF as a model-driven framework for designing and generating code for IoT systems.