Toward a Fog-Based Traffic Flow Framework for Tactile Internet

Fanibhare, Vaibhav; Sarkar, Nurul I.; Al-Anbuky, Adnan

doi:10.1109/jiot.2021.3126758

Cited by 3 publications

(1 citation statement)

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“…Beyond the scope of this study, infrastructure is designed with respect to the methodology chapter, where energy-efficient infrastructure design is based on a hybrid model of an optical access network with fog computing nodes. It is based on the application of gradient learning cleverly distributed among the computational nodes available in a given residential unit, as well as other unused parts of the network, using the efficient TI traffic flow algorithm [61]. This results in intelligent leverage of the fog computing nodes offloading with a real smart city application.…”

Section: Positive Energy District Local Government Infrastructurementioning

confidence: 99%

Living Lab Long-Term Sustainability in Hybrid Access Positive Energy Districts—A Prosumager Smart Fog Computing Perspective

Vohnout

Bukovský

Chou

et al. 2023

IEEE Internet Things J.

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Living Lab, one of the recent emerging smart city concepts, faces long-term sustainability challenges associated with its complexity and breadth of use. To be efficient, it must rely on comprehensive set of information distributed appropriately among all stakeholders to unleash its full innovation potential. This is especially true in the case of positive energy districts, where timely data dissemination is essential for prosumager decisions and their greedy behaviour. This paper interconnects intelligent information exchange, supported by ultra-low latency hybrid access network infrastructure, with the clever use of available fog computing resources to properly disseminate complex energy details to all participating entities. As the optimal distribution of information using proper task offloading is the convergence problem, we recalled higher-order neural units that helped maintain computational and energy efficiency in conjunction with the preservation of the overall system stability. We have achieved a reliable hourly energy consumption prediction with a computationally very lightweight alternative to commonly used deep neural network approaches that can be deployed on available smart appliances with ease. The application and simulation were performed on the dataset provided by one of Europe's smart city pioneers, where the prosumager positive energy district transition has already started.

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Section: Positive Energy District Local Government Infrastructurementioning

confidence: 99%