Abstract
The Internet of Things (IoT) is an emerging Internet-based architecture, enabling the exchange of data and services in a global network. With the advent of the Internet of Things, more and more devices are connecting to the Internet in order to help people get and share data or program actions. In this paper, we introduce an IoT Agent, a Web application for monitoring and controlling a smart home remotely. The IoT Agent integrates a chat bot that can understand text or voice commands using natural language processing (NLP). With the use of NLP, home devices are more user-friendly and controlling them is easier, since even when a command or question/command is different from the presets, the system understands the user’s wishes and responds accordingly. Our solution exploits several available Application Programming Interfaces (APIs), namely: the Dialogflow API for the efficient integration of NLP to our IoT system, the Web Speech API for enriching user experience with voice recognition and synthesis features, MQTT (Message Queuing Telemetry Transport) for the lightweight control of actuators and Firebase for dynamic data storage. This is the most significant innovation it brings: the integration of several third-party APIs and open source technologies into one mash-up, highlighting how a new IoT application can be built today using a multi-tier architecture. We believe that such a tiered architecture can be very useful for the rapid development of smart home applications.
Highlights
Mobile phones, home electrical appliances, cars, simple light bulbs, airplanes—almost every device we use in our daily lives—is connected to the internet, sending and receiving data in order to communicate with the outside world
The most significant innovation it brings is that it is totally based on third party Application Programming Interfaces (APIs) and open source technologies, so it highlights how a new Internet of Things (IoT)
The most significant innovation that the current implementation presents in comparison to the existing research is that it is totally based on third party APIs and open source technologies, so it is simple, modern and functional
Summary
Home electrical appliances, cars, simple light bulbs, airplanes—almost every device we use in our daily lives—is connected to the internet, sending and receiving data in order to communicate with the outside world. An application used in conjunction with NLP could handle voice commands to turn on or off every device in a home, monitor the home’s environmental conditions, or even control a smart car. This could be generalized to every interconnected device. For training our NLP system, we used the Dialogflow Application Programming Interface (API), an Application Programming Interface (API) from Google for conversational interfaces that uses machine-learning techniques in order to train itself This way, even if the questions/commands sent by the user differ to the ones preset during training, the system is able to “understand” and respond .
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