## 3 Related Work
### Ontology Models for Representation of IoT and Cloud ERP Services  
Our work deals with a semantic dynamic approach that produces and optimizes context-aware IoT business processes for industry 4.0 based on an ontology model and multi-agent strategies. Many IoT and Cloud ERP ontology models have been proposed in recent decades [3; 4; 5; 6; 7; 8; 9; 25; 26]. They have been used for different business domains but do not have enough concepts to manage context-aware constraints, describe all business changes, and manage quality semantically equivalent IoT services. However, they all share the following issues: (1) low coverage of business domain-independent semantic service information that describes the Cloud, Fog, and IoT services; (2) lack of consideration of flow sharing (e.g., data flow sharing, event flow sharing), sensor mobility, and usage constraints (e.g., service dependency, energy consumption, and time-space service accessibility), which helps to reduce the impact of context changes on service composition. We distinguish three main categories: IoT-based ontology model, Cloud ERP services ontology model, and combine both ontology models.  
[PERSON] et al. [3] proposed an ontology model for digitalizing different business activities, including IoT services and Cloud ERP APIs. They aim to integrate IoT with various Cloud ERP APIs that can be used to add automated functionalities to ERP systems. They connect various resources with IoT devices and integrate them with Cloud ERP APIs in one module. However, this model neither includes the semantic concepts of contextual management constraints and preferences nor the execution context. Moreover, this work does not consider the classification of Cloud ERP APIs and neglects the semantic grouping of IoT services by functionality, category, role, and QoS, which is considered very important in services selection.  
In [4], the authors present a new ontology model called OntoSLAM based on autonomous robots for solving simultaneous localization and mapping (SLAM). It described all aspects related to autonomous robots as well as temporal-spatial movement regions. They integrated it into the Robot Operating System (ROS) to increase the application flexibility. The ontology model relies on many ontology models, including ISRO ontology [5], POS ontology [6], CORA ontology [7], and KnowRob ontology [8], which leads to standard robotics metadata. However, the authors wasted most of their work in the classification of robots and physical things, and hence it neglected significant contextual and QoS constraints for efficiently managing robotic services.  
Authors in [9] used semantic Web and ontologies (OWL-S and SAWSDL) for describing OPC UA industrial applications to ensure semantic service annotation and interoperability. The approach builds a common ontology that integrates the production abstract services with concrete IoT-based services. Concrete services will be used on the shop floor. This common ontology makes the approach more interoperable in all levels of automation and infers the potential information. It provides relevant information accurately that helps to describe business processes. Unfortunately, it suffers from contextual information and custom conditions at each stage of the production business process.