A Rule-based Service Customization Strategy for Smart Home Context-aware Automation

The continuous technical progress of the smartphone built-in modules and embedded sensing techniques has created chances for context-aware automation and decision support in home environments. Studies in this area mainly focus on feasibility demonstrations of the emerging techniques and system architecture design that are applicable to the different use cases. It lacks service customization strategies tailoring the computing service to proactively satisfy users’ expectations. This investigation aims to chart the challenges to take advantage of the dynamic varying context information, and provide solutions to customize the computing service to the contextual situations. This work presents a rule-based service customization strategy which employs a semantic distance-based rule matching method for context-aware service decision making and a Rough Set Theory-based rule generation method to supervise the service customization. The simulation study reveals the trend of the algorithms in time complexity with the number of rules and context items. A prototype smart home system is implemented based on smartphones and commercially available low-cost sensors and embedded electronics. Results demonstrate the feasibility of the proposed strategy in handling the heterogeneous context for decision making and dealing with history context to discover the underlying rules. It shows great potential in employing the proposed strategy for context-aware automation and decision support in smart home applications

Analysis and study of powerefficient sar adc for active rfid sensor

This thesis introduced an energy-efficient successive-approximation-register (SAR) analog-to-digital converter (ADC) specialized to the active sensors for low-power radio frequency identification (RFID) tag system. As part of the Internet of Things (IoT) transformation, RFID is widely used. In the application where the power supply is limited, power consumption is always a notable criterion as analog circuits such as the ADC circuit, regulator circuit, rectifier circuit and radio frequency (RF) are the common power demanding parts in the system. Normally, the requirement for a longer battery performance is closely related to low-power consumption. For the application active RFID sensor in which requires low to moderate resolution and speed as well as low-power consumption, SAR is usually used as its part of the ADC circuit. Therefore, the power-efficient SAR ADC is presented in this work. The block of SAR ADCs such as the comparator block, digital-to-analog converter (DAC) block, and sampler block is designed to meet the requirement of a low-power consumption performance measurement. This thesis at first will explores the differences between multiple ADC techniques in the previous works. The proposed SAR ADC is presented to enhance the power consumption of SAR ADC in the active RFID sensor application through the implementation of a single-input comparator with the switched-capacitor DAC. In this form of architecture, there is only one input to the comparator, and only one set and a split sampling capacitor in the switched capacitor DAC to generate the required reference levels. The difference in input and output voltage of the proposed SAR ADC is the indication for the low-power design. The influence of parasitic capacitance is reduced to the extent of becoming a non-factor. The parameters of the SAR ADC are the resolution of 8-bit, the sampling frequency of 500 kHz, the supply voltage of 1 V, and the 0.18 μm complementary metal-oxide-semiconductor (CMOS) technology. The power consumption of the proposed SAR ADC is 2.3 μW which is estimated at around 25.8% improvement from the previous work. The demands for low-power consumption of RFID active sensor is well examined. The validity of the proposed design has been proven by the simulation results

Improved multi-user interaction in a smart environment through a preference-based conflict resolution virtual assistant

In this work we will examine and develop a system that can assist people in Activities of Daily Life (ADL). This study focuses on resolving conflicts for the requests from different users’ profiles, for instance - elderly, adult and young. The objective of the system is to present a dialogue manager which is able to detect multi-user semantic conflict and to resolve the conflict for improved dialogue informing about its decisions using a system interface Avatar. The system is also able to prioritize requests that occurred among the services of multiple home appliances, as well as to deal with conflicting entities involving a single device. We investigated whether the multi-user context awareness by a Virtual Assistant adds value to the Smart Home concept in recognizing multi-user conflicts dynamically. This work has proposed a preference based method for resolving conflict and evaluated the developed system in a smart home environmen

Improved multi-user interaction in a smart environment through a preference-based conflict resolution virtual assistant

In this work we will examine and develop a system that can assist people in Activities of Daily Life (ADL). This study focuses on resolving conflicts for the requests from different users’ profiles, for instance - elderly, adult and young. The objective of the system is to present a dialogue manager which is able to detect multi-user semantic conflict and to resolve the conflict for improved dialogue informing about its decisions using a system interface Avatar. The system is also able to prioritize requests that occurred among the services of multiple home appliances, as well as to deal with conflicting entities involving a single device. We investigated whether the multi-user context awareness by a Virtual Assistant adds value to the Smart Home concept in recognizing multi-user conflicts dynamically. This work has proposed a preference based method for resolving conflict and evaluated the developed system in a smart home environmen

Modelo basado en tecnologías semánticas y contenidos multimodales para sistemas sensibles al contexto

En la actualidad los seres humanos cuentan con dispositivos electrónicos que les permiten generar datos acerca de las actividades que realizan y el estado del entorno que los rodea. Por ejemplo, mediante un teléfono móvil, un usuario representa las situaciones que vive por medio de una fotografía capturada de forma instantánea y este almacena la ubicación en que se realizó la captura. Sin embargo, para las máquinas comprender el significado de las situaciones que vive un usuario es una tarea difícil, ya que requiere un amplio conocimiento de la realidad que este experimenta. La sensibilidad al contexto permite, por medio del uso de diferentes fuentes de información, caracterizar entidades de acuerdo a los datos de su entorno y generar nueva información con el objetivo de entregar servicios de valor de forma automática. La implementación clásica de un sistema de sensibilidad al contexto usa como fuente de información exclusiva los datos de sensor de los dispositivos que usan los usuarios. Este trabajo explora el uso de información semántica, obtenida por medio del análisis de contenidos digitales y datos de sensor, en los sistemas sensibles al contexto. Los contenidos digitales son relevantes porque su captura almacena información acerca de factores como los lugares en los que se encuentra el usuario, sus acompañantes, la actividad que realiza y las emociones que siente al realizarlas. El aporte principal de este trabajo es la propuesta de un modelo de contexto llamada MCAS. Este modelo usa la representación del contexto por medio de ontologías, la adquisición de contexto desde diferentes fuentes y el razonamiento de contexto por medio del uso de reglas implementadas en el lenguaje SPARQL. También se propone una ontología de alto nivel llamada mContext, la cual representa entidades relevantes para la definición del contexto como lo son persona, actividad, lugar, contenido multimedia, dispositivos y sensores. Se realiza una validación que busca verificar la viabilidad tecnológica y operacional de los componentes del modelo. Para ello, se implementa en el lenguaje Java, bases de datos de grafos y el lenguaje de consulta SPARQL; del modelo MCAS. Como datos de entrada se crean flujos de datos simulados, los cuales reflejan las actividades realizadas por cuatro usuarios con capacidades y necesidades variadas, y un grupo de reglas, que define las condiciones para la generación automática de actividades de alto nivel contextual. Los resultados permiten concluir que se puede generan un 100% de las actividades representadas por los expertos en las reglas. Sin embargo, se comprueba una de las limitaciones conocidas del razonamiento por medio de reglas en donde aquellas relaciones que no fueron creadas explícitamente por medio de reglas son ignoradas por el sistema haciendo necesario el desarrollo de estratégias de creación y eliminación de reglas en próximas etapas de la investigación.Abstract: Nowadays, human beings have electronic devices that allow them to generate data about their activities and environment. For example, using a cellphone, a user can know in real time the temperature of the place where he is located and also take a picture of a situación he is living. However, understanding the meaning of certain situación is still a difficult task for the machines that process the user's information. The Context Awareness allows, through the use of different sources of information, to characterize different entities from the environment's data. The classic context awareness systems approach use sensor data as the main data resource. This work explores the use of semantic information available in digital content and sensor data. Digital contents are relevant because they have information about user's locations, companions, activities, and emotions he feels in a specific situation. The main contribution of this work is the proposal of a context architecture called MCAS. This architecture takes into account the context modeling with ontologies, the context acquisition from different sources and the context reasoning through the use of rules implemented in the SPARQL language. Also, a high-level ontology called mContext is proposed, this ontology represents relevant entities for context definition such as person, activity, place, multimedia content, sensors, and devices. To validate the proposed architecture the MCAS architecture is implemented through the use of the Java language, the graph databases, and the SPARQL query language. The main objective of this process is to verify the technological and operational feasibility of the architecture components, through the automatic identification and generation of activities carried out by four users with varied capacities and needs. Based on this evaluation, it was concluded that the implementation of the proposed components allows the development of context-aware systems that generate 100 \% of the activities expected by the experts. It's important to notice that due to the rules reasoning limitations if an element is not considered on the rules generation, the expected high-level context activities will not be created. Additionally, the context data recording times on the graph database indicate that it is possible to develop near to real-time applications.Maestrí

AN INVESTIGATION INTO CONTEXT-AWARE AUTOMATED SERVICE IN SMART HOME FACILITIES: SEARCH ENGINE AND MACHINE LEARNING WITH SMARTPHONE

Technological advances, in general, coupled with the widespread use of smartphones, create ever more opportunities for mobile applications. This thesis considers the use of such devices within embedded systems to provide automated services in smart home automation. The overall approach links together context-aware data from the physical environment, sensors and actuators for domestic appliances and statistics-based decision-making. A prototype system named ‘Wireless Sensor/Actuator Mobile Computing in the Smart Home’ (WiSAMCinSH) is developed, which in turns aims to provide services that can benefit clients who are currently dependent on others in their daily activities. This research highlights and covers the following concepts. Firstly, it addresses the need to improve the prototypical decision-making model by enabling it to take into account context-aware information as conditions under which particular action decisions are appropriate. Secondly, an essential aspect of context-aware performance architecture is that its features must be of high accuracy, explicitly readable and fast. Thirdly, it is necessary to determine which probability-based rules are most effective in generating the dynamic environment to control the home facilities. Finally, it is important to analyse and classify in depth the accuracy of context acquisition and the corresponding context control using cross-validation methods. A case study uses integrated mobile detection technology to improve the efficiency of mobile applications, taking into account the resource limitations forced on the use of mobile devices. It also utilises other embedded sensing technologies to predict expectations, thereby enabling automatic control of facilities in the home. The main approach is to combine search engines and machine learning to create a system architecture for a context-aware computing service. Among the major challenges are finding the best statistics-based rules for decision-making and overcoming the heterogeneous character of the many devices which are used together. The results achieved show very promising potential for the use of mobile applications within a context-aware computing service, albeit one which still presents problems to be resolved through future research