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Title	A Machine Learning-Based Framework For Energy-Efficient Load Balancing In Sustainable Urban Infrastructure And Smart Buildings
ID_Doc	2482
Authors	Sharma A.; Kumar N.; Diwaker C.; Sharma B.; Baniwal R.; Bhattacharjee S.B.; Rani S.
Year	2024
Published	International Journal of Sustainable Building Technology and Urban Development, 15, 4
DOI	http://dx.doi.org/10.22712/susb.20240035
Abstract	Efficiency of energy consumption is a key element in respect of the sustainable development of cities and smart buildings. Mobile Edge Computing (MEC) is already becoming one of the promising technologies to support application of smart cities that has higher data throughput, low latency and energy efficiency without depending on the centralized data center. Nevertheless, the concern of optimizing resource management in these environments continues to be complex. In this paper, a new machine learning scheme for energy-aware load distribution in MEC systems is proposed to support green infrastructure in smart cities, particularly for smart building applications. Our framework uses real-time monitoring data of resource usage, energy consumption, and service level agreement (SLA) breaches for dynamic load balancing for energy-efficient operations. The future work involves the application of the proposed model for optimizing response time, energy consumption, and SLA by 13.4%, 6.74% and 32.7% respectively than traditional methods, while enhancing model’s accuracy by 1.06%. This framework can be easily implemented into smart building management systems as well as into the infrastructure of smart cities to increase energy efficiency, decrease operating costs, and foster sustainable usage of urban services. © International Journal of Sustainable Building Technology and Urban Development.
Author Keywords	energy efficiency; mobile edge computing (MEC); reinforcement learning; resource utilization; smart buildings; smart city technologies; sustainable building management; sustainable urban infrastructure

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