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Title |
Component-Oriented Software Engineering Model for Heterogeneous Internet of Things Systems with Connectors using Machine Learning |
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Author |
Shahanawaj Ahamad |
| Citation |
Vol. 22 No. 6 pp. 680-689 |
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Abstract |
Component reuse has been proven both theoretically and empirically to increase software quality and productivity with an economically cost-effective option. This necessitates the use of a graphical editor for project modeling using component-based architecture and development. To aid in the creation of component-oriented software, a graphical editor was proposed for practice. Both machine learning and software engineering employ models based on components architecture. Aside from these smart characteristics, AI models may be able to help with prediction and decision-making. Communication between IoT system components must adhere to a set of guidelines and protocols for effective and predictive perspectives. Components must be able to communicate with one another in the deployed system. The heterogeneity issue in the Internet of Things arises when different IoT devices communicate using distinct sets of rules, features, and contexts. Components that can be reused are found in these or other systems or commercial off-the-shelf. Component-oriented systems rely on connectors to link up their reusable parts with other entities, components, or IoT devices through the use of related interfaces. COSE development tools provide application-level solutions for connectors and component-based development of systems. Linking and hookup ports on connectors are designed to work with the attached component and other interfaces. The communication protocols' packets are identified and organized by the connectors with their installed applications. A simulation feature can be added to the tools in order to show that the idea can be implemented in effective and efficient ways. Connectors allow moving data between different parts of computing systems. ML-based training and prediction have been shown in this work for performance analysis. |
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Keywords |
Component-Oriented Software Engineering Model, Heterogeneous Internet of Things, Machine Learning |
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