(untitled) (International Journal of Food and Nutritional Sciences, 2021)
This research paper explores the design of latency-aware Internet of Things (IoT) modules within heterogeneous fog-cloud computing networks. As IoT devices become increasingly ubiquitous, there is a growing need to manage the latency involved in processing vast amounts of data across distributed networks. Fog computing, which extends cloud services to the edge of the network, offers a promising solution by reducing latency and improving real-time processing capabilities. This paper presents a comprehensive framework for designing IoT modules that are aware of and optimized for latency in heterogeneous fog-cloud environments. The proposed system integrates advanced machine learning algorithms and edge computing techniques to predict, manage, and minimize latency. We conduct a detailed literature survey to identify current gaps and limitations in existing models, followed by the introduction of our novel system architecture. Extensive simulations and performance evaluations demonstrate the effectiveness of our approach in reducing latency and improving overall network efficiency. This study contributes to the advancement of IoT module design, with significant implications for smart cities, autonomous vehicles, and other latency-sensitive applications.