Powered by OpenAIRE graph
Found an issue? Give us feedback
ZENODOarrow_drop_down
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

Energy-Efficient Management Paradigms for Smart Buildings and Infrastructure

Authors: Dr. Nikolai I. Kobasko National Academy of Sciences of Ukraine; Kyiv.;

Energy-Efficient Management Paradigms for Smart Buildings and Infrastructure

Abstract

The incremented popularity of Internet of Things (IoT), thanks to improvements both in hardware and software of sensors over the last years, enables the possibility to monitor and gather any kind of data. Additionally, the arrangement of heterogeneous sensors, capable of perceiving information about their surroundings, into a rich Wireless Sensor Network (WSN), allows the appearance of complex systems in which resources are managed more efficiently. Smart cities, buildings, parkings, emergency services are appearing, where control over energy consumption and better sustainability are coupled with an improvement of the comfort of occupants. In this paper, we address the problem of energy optimization in smart buildings, considering both the planning and operational aspects. Specifically, the first aim is to propose an optimal deployment of the WSN inside a building. For this, we present a model able to identify the optimal locations for different types of sensors and gateways, by optimizing energy consumption while fulfilling connectivity, resource, protection, and clustering coverage constraints. Once the IoT system is deployed, we address the problem of how the building actually functions, according to the behaviour of the occupants. In particular, we propose a Building Management System (BMS) capable of efficiently and automatically manage the building elements using human behavioural models, thus lowering the overall building energy consumption whilst maintaining acceptable levels of comfort.

Keywords

ILP, Smart buildings, Internet of Things, comfort, simulation, optimization, Wireless Sensor Network, energy

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!