4.6 Article

A location-based fog computing optimization of energy management in smart buildings: DEVS modeling and design of connected objects

Journal

FRONTIERS OF COMPUTER SCIENCE
Volume 17, Issue 2, Pages -

Publisher

HIGHER EDUCATION PRESS
DOI: 10.1007/s11704-021-0375-z

Keywords

smart building; energy consumption; IoT; fog omputing Framework; DEVS simulation models

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Nowadays, smart buildings utilize IoT technology from cloud and fog computing paradigms for coordination and collaboration between connected objects. Fog computing provides low latency, wider spread, and geographically distributed nodes to support mobility, real-time interaction, and location-based services. This study uses the DEVS formalism to accurately design and integrate sub-models of connected objects in a building, resulting in improved energy efficiency and reduced latency. The flexibility of DEVS allows for easy addition or removal of sub-models, enabling continuous design improvement.
Nowadays, smart buildings rely on Internet of things (IoT) technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected objects. Fog is characterized by low latency with a wider spread and geographically distributed nodes to support mobility, real-time interaction, and location-based services. To provide optimum quality of user life in modern buildings, we rely on a holistic Framework, designed in a way that decreases latency and improves energy saving and services efficiency with different capabilities. Discrete EVent system Specification (DEVS) is a formalism used to describe simulation models in a modular way. In this work, the sub-models of connected objects in the building are accurately and independently designed, and after installing them together, we easily get an integrated model which is subject to the fog computing Framework. Simulation results show that this new approach significantly, improves energy efficiency of buildings and reduces latency. Additionally, with DEVS, we can easily add or remove sub-models to or from the overall model, allowing us to continually improve our designs.

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