Please use this identifier to cite or link to this item: https://hdl.handle.net/10955/5486
Title: Technologies and IoT Protocols applied to Energy Management in Smart Home Environment
Authors: Serianni, Abdon
De Ranco, Floriano
Keywords: IoT
Cognitive IoT
M2M
Machine learning
Smart home
Issue Date: 13-Sep-2021
Publisher: Università della Calabria
Abstract: This thesis presents the studies during this period of my PhD course. In the first research period, I focused the activities principally on the study and analysis of the protocols and technologies used for the IoT solutions in Smart Home environment. It was analyzed the MQTT protocol and its possible applications. The MQTT protocol uses the event-driven publish/subscribe pattern. In our tests, MQTT usage was compared with a classic HTTP request/response paradigm, used in REST and CoAP approaches. A layered IoT communication architecture will be proposed and described. The usage of proposed IoT communication architecture was analyzed in Smart Home context and in other application contexts such as e-Health and Internet of Vehicles (IoV). After an analysis of Data Mining and Machine learning concepts, the focus of the activities was on Neural Networks. The use of LSTM networks was analyzed for time-series forecasting and prediction of consumption in two different environments (home and office). In the smart home environment, smart objects are characterized by limited resources. Our proposal to increase the computational capabilities of these smart devices is a hidden cognitive object that uses pre-trained NN and continuous learning for anomaly detection and suggested action prediction tasks. The Cognitive Smart Object is the joining of a smart device and a hidden cognitive object. The Cognitive Smart Object was used in thermal comfort control application and manage better energy consumption. The concepts introduced have been used for an assisted comfort solution and the neural network results were used to suggest to the user conventional management of the climatic comfort levels. A Continuous Learning mechanism was been implemented with the usage of user feedback to shape the neural network and obtain a neural network that follows user behaviours that diverge from behaviour compliant with the ASHRAE standard. From the analysis of the results obtained, it was possible to highlight how NN has given results closer to the user’s habits and at the same time the user has been educated to use the right levels of thermal comfort.
Description: Dottorato di ricerca in Information and Communication technologies. Ciclo XXXIII
URI: https://hdl.handle.net/10955/5486
Appears in Collections:Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica - Tesi di Dottorato

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