Internet of things based zigbee sniffer for smart and secure home
Yükleniyor...
Dosyalar
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
This paper aims to resolve the Internet of Things (IoT) based ZigBee sniffer for smart home and determine the
usage of energy or power with high spectrum allocation in future ZigBee Protocol with the help of clustering in
IoT with data mining. The research work starts presenting an overview of the broadband network energy sector
and the challenges that face it. It is observed a change in the energy policies promoting energy efficiency,
encouraging an active role of the consumer, instructing them about the importance of consumer behavior,
and protecting consumer rights. Electricity is gaining room as an energy source. Its share will keep constantly
increasing in the following decades. ZigBee Protocol and smart meters’ deployment will benefit both the utility
and the consumer in the near future. New services and new businesses appear in this environment, focusing
on the energy management field and tools. They require specialization in fields such as computer science,
software development, and data science. This research has segmented the ZigBee Protocol according to the
similarities of their electrical load profiles, using the proportion of energy usage per hour (%) as a common
framework. This energy consumption segmentation aims to provide personalized recommendations to each
group to reduce their energy consumption and the associated costs, fostering energy efficiency measures and
improving consumer engagement. The desired segmentation is obtained by an iterative process, based on
computational clusters calculation (using a Python programming language) and finalized by a post-clustering
analysis applying visualization and statistical data mining technique to detect the energy consumption and
reallocate them to a more appropriate group. The K-Means clustering technique was tested and compared,
giving the best prediction of accuracy 98.46% for all energy load profiles with a high spectrum of 100GHz. The
solution from the K-Means clustering is the one that better adapts to the segmentation sought, which is used
as the base of the post-clustering stage to obtain the final energy consumption segmentation.
Açıklama
Anahtar Kelimeler
Energy, Internet of Things (IoT), K-Means, ZigBee Protocol, Clustering, Consumption, Smart, Monitoring
Kaynak
AURUM Journal of Engineering Systems and Architecture
WoS Q Değeri
Scopus Q Değeri
Cilt
6
Sayı
1
Künye
Albayati, F. S.M., Cansever, G. (2022). Internet of things based zigbee sniffer for smart and secure home. AURUM Journal of Engineering Systems and Architecture, 6(1), 45-65.
