Integration of IoT and cloud services in a home automation assistant

Abstract

The world becomes over time smarter, technology evolves rapidly in any scientific area and according to home electrical installations then interest is concentrating mainly in internet of things and smart systems section. Various technologies are involved in a smart facility with main purpose to reduce energy wasting costs, increase comfort and provide remote control and system information about the smart facility status from anywhere through a visualization platform that is designed for the user interaction. In an internet of things system many appliances are possible to be adapted such as lights control, motion detection, climate control, shutter and blinds control, security cameras. The amount of the connected devices to the internet is rapidly increasing, the next step is the support of the associated technologies of the web of things. (WoT). (Guinard, 2011) The main characteristic of the smart system components is the ability of the smart devices of communicating with each other in digital way. The communication between the devices applied by using smart device communication protocols. The smart devices are translating the natural world different signal to a digitized information that can be transferred through the smart device interconnected network and also through a gateway to third party system for extending the flexibility of information usage. (Gubbi, 2013, p. 1645 1660) (Pelesic, 2021) The smart devices are having a great impact according to the market, stakeholders are producing billions of devices in yearly bases for fulfil the customer needs for smart control, cost reduce and facility management. According to Statista predictions for 2025 the amount of the installed smart devices will reach the 30.9 billion devices. The increased amount of the installed smart devices will increase the facility management and control exponentially. (Vailshery, 2021) The user is involving to control more complex smart systems when increasing the smart installation. A possible solution would be the adaptation of different smart technologies that are providing the ability to make things easier to control without any technical knowledge experience. The voice assistants are having a great impact in to the market as a secondary control of a smart system. The main control of a smart system from the user is applied through a dashboard that is in most of cases compatible with mobile devices, tablets and computers. The voice assistant understands the user simple commands through its speech recognition model which is the most important part of a virtual assistant. The speech recognition system consists complex neural networks for the speech recognition creation models procedure. Convolutional Neural Networks (CNN) are better v choice because are providing more accuracy and less validation accuracy considering the comparison with Basic Neural Networks. (Patil, 2021) The user is interacting with the system with main criteria the simple control, reliability and operability. Many techniques have been applied in the past and are under development for making things easier to control for the user and the installers because these methods will increase the market benefits and the user amount because no dedicated knowledge is required for the usage of the system. The voice assistants are mainly use cognitive services and artificial intelligence for understand voice and natural language through a device or service. The voice assistants are beginning to be more popular through smart phones but also having a great impact to smart homes according to the people trend life at the beginning. People adapted and became familiar with kind of technologies for simple tasks control and information for their facility. (Uma, 2019) Automation is one of the most complex area that users are providing investments for earn time and money by reducing energy without losing their comforts. Automation are dedicated function routines that are providing features that are considering procedures that are triggered without supervision in the most of cases. Google assistant and Alexa are opening the path by making the intelligent platforms more friendly for the user. The microcontrollers, open coding sources and electronic equipment are providing support to many intelligent solutions that discovered from many scientists, engineers, student and people that is involved with the innovation. (Singh, 2019) The automation tasks are complicated, and many times are created from the KNX partners or the system administrators. The user doesn’t have the ability anytime to know which automation must apply for reaching the desired result that automation is pointing. Many automations, scripts, events and scenes can be part of the system. By taking in to account the user demands for simple control in parallel with the market increment according to statistics considering the smart devices this dissertation introduces a method for simplify internet of things control by pointing mainly in the automation area by applying cognitive, artificial intelligence techniques and natural language processing for adapting the user demands through conversational way. (Patil, 2021) The user is difficult to remember the name or the ability of any automation, script, event or scene of the smart system. The conversational ability of the user is taking in advance to create a voice assistant that can understand and point to the most significant system spot according to the user conversational description. The user conversational description will trigger a query of many complex functions that the user had to do nothing about it, without technical knowledge describes the action, the spot of interest or the point of process and the voice assistant understands and reacts by approaching one or more-point targets by taking in advance the user description classification scores and phrases similarities. The control of a complex congested ecosystem applied according to this dissertation in real time for providing the significance of the applied result by adapting cognitive services. The artificial intelligence, cognitive behavior and natural language processing was applied through google cloud services and Dialogflow Essential mechanisms for succeed the conversational patterns and system interaction with the KNX and MQTT servers. (Hager, 2021) (Raspberry, n.d.) (HomeAssistant, n.d.) The google cloud and Dialogflow essentials services became interconnected with the appliance of fulfilment webhook with the ecosystem. The google Dialogflow provides according to our knowledge until now small talk, Machine learning algorithms and is understanding the user expressions with the appliance of logical agents. (Patil, 2021) The user provides a conversational request, and the cognitive engine is classifying the request and produce an action to the system for applied in the real world through KNX and MQTT components mainly. The creation complex and congested ecosystem for experimentation purposes it is critical for receive the installation issues, the validity, the operability, the interoperability and the control things simplification by adapting a dedicated conversational voice assistant for point significant system elements. The combination of many communication protocols that are collaborating through the cloud for room control by pointing for example an HVAC system and analyze the received data is an innovation progress for the smart facilities installations. (Vanus, 2018) The experiment was placed in real time with the user to take the control of the ecosystem for providing their opinion about reducing complexity according to their technical skills and knowledge. The experimental procedure was applied by providing simple information to the user through the voice assistant and a simple advice guide for task completion. The users interacted with the system instantly and their experience is reflected to their responses according to the statistics pie charts that was collected through google questionnaire. Considering the results which arose of the conversational dedicated voice assistant proposed method, succeed to simplify things for the users. Although people according to the experiment results most of the individuals are afraid to provide trust to artificial intelligence engines for having the full control of more personal features. Considering to the system appliance improvements are needed for the future to be applied in industry with more dedicated and flexible natural language processing models and training for adapting more complex demands that are pointing to more simple descriptive commands. Considering the natural language processing modeling in parallel with the experiment, revealed that as simple the user phrase is, is more difficult for the cognitive system to classification and recognize the user demand when applied in complex headers and procedure descriptive requests. More dedicated natural language processing models is proposed for the future for adapting the user different areas of different interests according to industrial application, smart home, hospital and cities for applying cognitive system interactions.