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ARTICLE
MULTI-LAYERED FEATURE MODELS FOR ENHANCED IOT APPLICATION DEPLOYMENT IN EDGE ENVIRONMENTS
Issue Vol. 1 No. 01 (2024): Volume 01 Issue 01 --- Section Articles
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Abstract
The pervasive growth of Internet of Things (IoT) applications necessitates robust and efficient deployment strategies, particularly within the constrained and dynamic environments of edge computing infrastructures. Traditional cloud-centric models often suffer from high latency and bandwidth limitations, making edge computing a crucial paradigm for processing data closer to its source [6, 17, 45, 46]. This article explores the application of multi-layered feature models as a sophisticated approach to support and optimize the deployment of diverse IoT applications on heterogeneous edge-based infrastructures. Feature models, a cornerstone of Software Product Line Engineering (SPLE), provide a structured way to represent commonalities and variabilities within a system [12, 20]. By extending these models to multiple layers, we can capture the intricate interdependencies between IoT application features, underlying edge infrastructure capabilities, and deployment configurations. This approach facilitates automated reasoning, configuration, and optimization of deployment decisions, addressing challenges such as resource allocation, energy efficiency, and latency reduction in dynamic edge environments [24, 25, 34, 49]. We discuss the theoretical foundations, methodological considerations, potential benefits, and future research directions for leveraging multi-layered feature models to achieve flexible, scalable, and performant IoT deployments at the edge.
Keywords
References
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