# A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning +
## 6 Discussion
The architecture proposed in [13] provides a high-level structural view of software, hardware, and communication components placed into layers for facilitating system design and development. However, it does not provide indications on where these components should be deployed. In order to provide a clear view of the different deployment locations for architectural components, they developed an approach of formalizing the highly distributed infrastructure of IoT systems and facilitating the creation of different deployment views for the mapping between layered architectural components into stage locations. In comparison with this work, our solution automated the implementation process and presented a better applicability of the proposed method, as the software and scripts are in the public domain and compatible with any provider that implements the MQTT protocol. The MAPE-K framework (monitoring, analyze, plan, and execute with knowledge base), proposed in [16] requires detailed knowledge about contexts, sensors and actuators, and effects of the process in the real world for the modeling of objectives using the proposed methodology, requiring the use of more sophisticated approaches to formalize and deduce this knowledge in order to reduce the modeling effort. The work used BPM processes and systems that are more self-aware of their executions and self-adaptive through the feedback loop. However, applying feedback loops to ensure the result of process execution also introduces additional computational overhead, which can be counterproductive with respect to real-time constraints. The adopted solution requires additional resources for operation and communication, which may not be feasible in resource-constrained computing environments. Researchers in [17] presented an alternative to improve the data transmission validation process. They implemented the test-driven development methodology (TDD) for the build out of validation application algorithms. As a result, they developed validation application that captures and analyzes data frames, in addition, compares the values of the monitored variable in each layer of the implemented model. Furthermore, the development reports the errors detected in the data transmission. This work has limitations, such as the high dependence on networks to monitor the performance of the designed application and also, the work did not perform testbeds to verify the approach in the wild with a real IoT implementation. The Open-Multinet (OMN) framework [18] is based on ontologies to support the resource management in federated and distributed computing infrastructures. One of the known limitations of explicit knowledge-based systems is the laborious process of knowledge retrieval and update. Once the information is known, the queries may be acceptable to end-users, but its underlying update process may not be so dynamic. We could consider our proposed framework as a method to obtain resource information regarding specific scenarios in an automated way, thus providing more dynamism than the OMN framework. Simulations and its underlying mathematical apparatus [19; 20] are also relevant and may be used as an alternative to the proposed TDD approach with machine learning based on real testbed data. To the best of the authors' knowledge, it is the first TDD approach for IoT module automatic assessment which uses machine learning based on real testbed data.