# BPMNE4 IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes +
## 8 Discussion  
In this section, we discuss how the BPMNE4 IoT framework for modeling, executing, monitoring and recording IoT-driven BPs addresses the derived research questions and objectives (cf. Section 1.1).  
Regarding the modeling of IoT-driven BPs (**RQ1**), we identified a lack of support in both BPMN 2.0 and existing BPMN 2.0 extensions. To capture the IoT involvement and IoT characteristics in process models more explicitly, we extended the BPMN 2.0 metamodel with IoT-specific modeling elements and a corresponding notation (_Objectives O1 and O2_). The results of the user study further confirmed that the modeling of IoT-driven BPs with BPMNE4 IoT leads to a positive user experience, takes less time, and decreases the workload in comparison to BPMN 2.0 (_Objective O3_).  
The proposed BPMN 2.0 extension and the presented software architecture together form a holistic framework for modeling, executing, monitoring and recording IoT-driven BPs (**RQ2**). The modeling of IoT-driven BPs is enabled through extensions, which can represent IoT device involvement and IoT device behavior (cf. _Objectives O1 and O2_). The execution of the resulting IoT-driven BPs is enabled based on the implemented BPMS and its architecture (cf. _Objective O4_). In particular, the BPMS is able to interpret and execute the IoT-driven BP modeled in terms of the BPMNE4 IoT extension. The monitoring of the corresponding process instances is enabled by the IoT Process Monitoring component, which is closely coupled with the engine, receiving continuous execution updates from it (cf. _Objective O5_). Both process execution data and IoT-specific execution data are stored in an IoT-enhanced event log (cf. _Objective O6_). Altogether, we have been able to demonstrate the modeling, execution, monitoring and recording of IoT-driven BPs from different domains.  
To assist process engineers in modeling, executing and monitoring IoT-driven BPs (**RQ3**), we developed a tool that integrates the BPMNE4 IoT extension (cf. Section 5.3). To keep the cognitive efforts low and to increase user acceptance, especially for non-experts, we implemented BPMNE4 IoT in an already existing BPMN 2.0 modeler (_bpmn.io_). To increase usability, we have kept the configuration options for IoT devices to a minimum. Furthermore, process engineers are enabled to execute IoT-driven BPs with this tool. As the monitoring system is directly coupled with the execution engine, the process engineers receive direct feedback, e.g., at the occurrence of an error, non-reachability of IoT device, or defective IoT devices. Furthermore, an event log is available that contains more detailed information (cf. _Objective O7_). The tool was evaluated in two real-world scenarios (cf. Section 6). Moreover, we have separated the modeling of IoT-driven BPs from the specification of IoT devices in the tool. This allows BP experts to model processes without the need to deal with IoT-specific characteristics (cf. _Objective O8_).  
As opposed to the existing approaches, we provide a holistic framework that supports all phases of the process lifecycle, i.e., the scope of this work is the modeling, execution, monitoring and logging of IoT-driven BPs. Note that we did not discuss other challenges such as IoT data security, robustness and reliability. Regarding the IoT data security, we made use of the standard mechanisms provided by the middleware, gateways, and IoTdevices. The current approach enables the monitoring of defective and unreachable IoT devices. However, the handling of such failures has not been considered.