# An Interoperable Component-Based Architecture for Data-Driven IoT System
## 5 Experiment Results and Discussions
### Potential Application Domains and Use-Cases  
The application of the proposed IoT system as a MPPT tracker has proved the feasibility of the proposal to be utilized for the real-world scenario, targets to support most IoT system applications, as long as they operate in a \"data collection -> stochastic analysis -> feedback and visualization\" manner. Moreover, the proposed architecture aimed to support large sensors and controllers network of same or different OS. Hence, use cases that use IoT system to interact with a huge number of end devices are suitable to be implemented using the proposed architecture. We suggest three use-cases that are suitable to be implemented using the proposed architecture:  
* Smart factory: Smart factory systems generally interact with a large network of sensors and controllers that requires real-time data sensing and feedback action. Furthermore, these end devices are typically operating under diverse protocols such as RFID, Modbus, and MQTT.
* Household smart grid: Household smart grid typically manages both the supply and demand of electricity of a household. The supply-side consists of the electricity from the national grid and any electricity generated from renewable energy, whereas the demand-side consists of the appliances that consume electricity. In short, a household smart grid system is required to manage sensors and relays of both supply and demand-side, which are two different systems integrated on one platform. For instance, a smart grid system is deemed to provide instant adjustment on household electricity generation during high solar irradiance; or electricity consumption during on/off-peak period throughout the day, or when the user is not within the premise.
* Smart city: Smart city systems are combinations of multiple IoT applications including smart grids, smart traffic systems, and smart home systems with highly condense network of sensors, CCTVs, and controllers. In fact, the smart city is one of the most ideal use cases that is suitable to be implemented using the proposed architecture due to its cross-domain integration and high-density data volume that requires data-driven feedback models.  
However, the proven interoperability and reusability of the proposed architecture may be compromised under different environmental conditions such as drastic weather and geographical conditions, limited network resources and computational resources. Hence, the proposed architecture is not suitable to be implemented in extreme environments such as oil and gas extraction and refinement stations that are usually located in the middle of oceans with low network availability. Another concern of the proposed architecture is the lack of support for distributed machine learning or optimization application. Despite being loosely-coupled and highly distributed, the data-driven models are still stored and operate under one machine which raised concern on the limited computational resources. As a result, the proposed architecture is unable to support a domain that uses highly-complex or distributed optimization models, such as Federated machine learning [42] or distributed machine learning [43]. The possibility to achieve distributed and parallel optimization remain as a challenge for future research.