An intelligent controller for smart home

Babu, V. Suresh; Kumar, Uddeshaya; Priyadharshini, R.; Premkumar, Krithika; Nithin, S.

doi:10.1109/icacci.2016.7732459

Cited by 23 publications

(7 citation statements)

References 14 publications

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“…The AC was developed from scratch using the Python language, one of the most common languages in ML implementations. The operation of this module strongly depends on the use of the Advanced Python Scheduler (APScheduler) 9 and the Eclipse Paho MQTT Python Client 10 libraries.…”

Section: B Implementationmentioning

confidence: 99%

Voice-Activated Smart Home Controller Using Machine Learning

2021

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The emergence of the Internet of Things concept has provided a great vision for the technological future, intending to enable the extraction and comprehension of information from the environment around us, making use of the interaction and cooperation between several technological devices. The example of Smart Homes, in particular, aims to integrate these devices into households, enabling the automation of tasks previously performed by humans, to simplify their daily lives and create a more comfortable environment. However, many of these devices fail to keep their promise, since they were not developed taking into account the frequent change of habits and tastes of the user, being necessary reprogramming of the device to follow the new behaviors. Taking this problem into account, this article presents the design and end-to-end implementation of a voice-activated smart home controller for intelligent devices, deployed in a real environment and validated in an experimental setup of motorized blinds. The architecture of the proposed solution integrates evolvable intelligence with the use of an Online Learning framework, enabling it to automatically adapt to the user's habits and behavioral patterns. The results obtained from the various evaluation tests provide a validation of the operation and usefulness of the developed system. The main contributions of this work are: I) design of a smart home controller's architecture; II) end-to-end implementation of a smart home controller and respective guidelines; III) opensource dataset of user behavior from the smart blinds scenario; IV) comparison between Online and Offline Learning approaches.

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Section: B Implementationmentioning

confidence: 99%

Voice-Activated Smart Home Controller Using Machine Learning

2021

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“…An effortless and user-friendly method of using decision-based controlling for Smart Home is proposed in [15]. A combination of an artificial neural network and the fuzzy logic controlling system can be devised for a switched reluctance generator, but the controlling systems work independently, without any connection [16].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Controller Design by the Artificial Intelligence Methods

Nowaková

Pokorný

2020

Sensors

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With the rapid growth of sensor networks and the enormous, fast-growing volumes of data collected from these sensors, there is a question relating to the way it will be used, and not only collected and analyzed. The data from these sensors are traditionally used for controlling and influencing the states and processes. Standard controllers are available and successfully implemented. However, with the data-driven era we are facing nowadays, there is an opportunity to use controllers, which can include much information, elusive for common controllers. Our goal is to propose a design of an intelligent controller–a conventional controller, but with a non-conventional method of designing its parameters using approaches of artificial intelligence combining fuzzy and genetics methods. Intelligent adaptation of parameters of the control system is performed using data from the sensors measured in the controlled process. All parts designed are based on non-conventional methods and are verified by simulations. The identification of the system’s parameters is based on parameter optimization by means of its difference equation using genetic algorithms. The continuous monitoring of the quality control process and the design of the controller parameters are conducted using a fuzzy expert system of the Mamdani type, or the Takagi–Sugeno type. The concept of the intelligent control system is open and easily expandable.

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“…The system uses open-source software and guarantees that 10%–15% power savings are achieved with proper setting and scheduling. Similar systems based on ZigBee are described in [42,43]. Moreover, an integrated solution is proposed in [44], where the system enables small residential consumers to provide DR services for grid support considering both local energy resources and end-user’s convenience in a real-household environment.…”

Section: Related Workmentioning

confidence: 99%

An Electricity Price-Aware Open-Source Smart Socket for the Internet of Energy

Blanco-Novoa

Fernández‐Caramés

Fraga‐Lamas

et al. 2017

Sensors

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The Internet of Energy (IoE) represents a novel paradigm where electrical power systems work cooperatively with smart devices to increase the visibility of energy consumption and create safer, cleaner and sustainable energy systems. The implementation of IoE services involves the use of multiple components, like embedded systems, power electronics or sensors, which are an essential part of the infrastructure dedicated to the generation and distribution energy and the one required by the final consumer. This article focuses on the latter and presents a smart socket system that collects the information about energy price and makes use of sensors and actuators to optimize home energy consumption according to the user preferences. Specifically, this article provides three main novel contributions. First, what to our knowledge is the first hardware prototype that manages in a practical real-world scenario the price values obtained from a public electricity operator is presented. The second contribution is related to the definition of a novel wireless sensor network communications protocol based on Wi-Fi that allows for creating an easy-to-deploy smart plug system that self-organizes and auto-configures to collect the sensed data, minimizing user intervention. Third, it is provided a thorough description of the design of one of the few open-source smart plug systems, including its communications architecture, the protocols implemented, the main sensing and actuation components and the most relevant pieces of the software. Moreover, with the aim of illustrating the capabilities of the smart plug system, the results of different experiments performed are shown. Such experiments evaluate in real-world scenarios the system’s ease of use, its communications range and its performance when using HTTPS. Finally, the economic savings are estimated for different appliances, concluding that, in the practical situation proposed, the smart plug system allows certain energy-demanding appliances to save almost €70 per year.

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An intelligent controller for smart home

Cited by 23 publications

References 14 publications

Voice-Activated Smart Home Controller Using Machine Learning

Voice-Activated Smart Home Controller Using Machine Learning

Intelligent Controller Design by the Artificial Intelligence Methods

An Electricity Price-Aware Open-Source Smart Socket for the Internet of Energy

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