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

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 p...

Full description

Saved in:
Bibliographic Details
Published in:IEEE internet of things journal 2023-11, Vol.10 (21), p.1-1
Main Authors: Vohnout, Rudolf, Bukovsky, Ivo, Chou, Shuo-Yan, Geyer, Jakub, Budik, Ondrej, Sharma, Rohit, Prokysek, Milos, Horvath, Tomas, Wyckmans, Annemie
Format: Article
Language:English
Subjects:
Access Network
Artificial neural networks
Complexity
Computation offloading
Computer architecture
Data exchange
Decision making
Edge computing
Energy consumption
Fog Computing
High Order Neural Unit
Living Lab
Optimization
Polynomial Neuron
Positive Energy District
Prosumager
Smart cities
Smart City
Stability
Sustainability
Tactile Internet
Task analysis
Technological innovation
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2023.3280594