Akıllı ve güvenli ev için şeylerin interneti tabanlı zigbee snıffer

This thesis 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. Most of these methodologies use the absolute values in 100 kWh, focusing on identifying the users with higher energy savings potential. The energy consumption segmentation of the electricity consumers provides knowledge and a better understanding of the consumer. In this particular case, it allows to personalize energy savings recommendations according to the ZigBee protocol-specific characteristics, improving the consumer experience by providing adequate advice at the appropriate time, facts that increase the effectiveness of the energy efficiency advice' service future ZigBee protocol. Keywords: Internet of Things (IoT), K-Means Clustering, Energy Consumption, Smart, Zigbee protocol