## 2 Motivating Case Study: IRIS SETIF Company  
In order to create a clear scientific vision of the research problem, this section analyses the IoT services and fog computing to illustrate their utility and effectiveness in distributed business processes using a real-life scenario. This scenario is based on the production and transport business processes of IRIS Company of SETIF city, Algeria. This company manufactures electronic products and provides wheels and mobile IT services to clients. These services are accessible through the IRIS Web portal. The IRIS services are hosted on a Cloud provider using the SaaS service type to provide additional support for services that are not in-house. This is illustrated in Figure 1, which covers the Cloud and the user application layers. A centralized Cloud ERP proxy selects and composes quality Cloud services based on IRIS company's needs and execution context. The Cloud ERP proxy, however, is in charge of satisfying the company's needs and preferences, deploying and maintaining ERP business processes. When the number of requests becomes enormous, it slows down the execution speed of the Cloud ERP proxy with considerable latency and can thus influence the QoS. Besides, IRIS is facing a continuous increase in business and suffering from resource limitations that can lead to production delays and significant economic losses to control raw materials and products in many different sites with different storage conditions of its growth cycle. Thus, its current traditional Cloud ERP software has become inefficient  
Figure 1: Cloud-based ERP production business process for IRIS. and lacks flexibility and efficiency in production and transport management services. To overcome these constraints, it is necessary to modernize current traditional Cloud ERP services and introduce modern technologies, such as industrial IoT services, in this company to improve production and adopt effective transport. Many of benefits of industrial IoT that have been identified include: (1) robots to optimize the transport of raw materials and products on different locations, (2) smart sites equipped with sensors and actuators to ensure remote control of suitable storage conditions for growing products, (3) production time and energy cost optimization to effectively transport the appropriate quantity of raw materials, and (4) synchronization and monitoring of different robots with ERP services. We aim to dynamically integrate industrial IoT with existing production and transport management processes. All the challenges faced by integrating industrial IoT with existing Cloud ERP are as follows:  
* How could we integrate different mobile robotics and monitoring services to ERP? * How to choose reliable IoT services to be used in Cloud business processes to get better QoS at optimized cost? * How can we manage the increase in IoT services while increasing the amount of data? * How would manage data among IoT services and Cloud ERP affect latency and QoS? As services can be heterogeneous, there will be different data formats. Furthermore, different types of protocols might be used. Thus, it will be difficult to integrate such robotics and compose them with existing business processes. To handle these problems, a semantic ontology model is proposed. The ontology undertakes this problem by hiding a large amount of heterogeneous IoT data and their different communication protocols. However, the selection and integration of quality IoT services are still difficult and differ according to the context of ERP application, time constraints, and evolution of the QoS needs and preferences of the company (non-functional needs and preferences). For example, an IoT service that detects incorrect data in real-time can cause a service failure or synchronization problem detection. Thus, it increases the complexity of IoT integration to the business processes.