Collaboration of Smart IoT Devices Exemplified With Smart Cupboards

[EN] The variety of smart things connected to Internet hampers the possibility of having a standalone solution for service-centric provisioning in the Internet of Things (IoT). The different features of smart objects in processing capabilities, memory, and size make it difficult for final users to learn the installation and usage of all these devices in collaboration with other IoT objects, hindering the user experience. In this context, we propose a collaboration mechanism for IoT devices based on the multi-agent systems with mobile agents. This paper illustrates the current approach with smart cupboards for potentially tracking memory losses. The user study revealed that users found working products of this approach usable, easy-to-learn and useful, and they agreed that the current approach could provide a high quality of experience not only in the specific case of service-centric IoT devices for tracking memory losses but also in other domains. The learning capability by means of this approach was showed with significant reductions of reaction times and number of errors over the first and second tests with the current approach. System response timesThis work was supported in part by the Dpto. de Innovacion, Investigacion y Universidad del Gobierno de Aragon through the program FEDER Aragon 2014-2020 Construyendo Europa desde Aragon under Grant T49_17R, in part by the University of Zaragoza and the Foundation Ibercaja through the Research Project Construccion de un framework para agilizar el desarrollo de aplicaciones moviles en el ambito de la salud under Grant JIUZ-2017-TEC-03, in part by the Estancias de movilidad en el extranjero Jose Castillejo para jovenes doctores Program, Spanish Ministry of Education, Culture and Sport, under Grant CAS17/00005, in part by the Universidad de Zaragoza, Fundacion Bancaria Ibercaja and Fundacion CAI, Programa Ibercaja-CAI de Estancias de Investigacion, under Grant IT24/16 and Grant IT1/18, in part by the Research Project Desarrollo Colaborativo de Soluciones AAL, Spanish Ministry of Economy and Competitiveness, under Grant TIN2014-57028-R, in part by the Organismo Autonomo Programas Educativos Europeos under Grant 2013-1-CZ1-GRU06-14277, and in part by the Ministerio de Economia y Competitividad through the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento, under Grant TIN2017-84802-C2-1-P.García-Magariño, I.; González-Landero, F.; Amariglio, R.; Lloret, J. (2019). Collaboration of Smart IoT Devices Exemplified With Smart Cupboards. IEEE Access. 7:9881-9892. https://doi.org/10.1109/ACCESS.2018.2890393S98819892

Defenses Against Perception-Layer Attacks on IoT Smart Furniture for Impaired People

[EN] Internet of Things (IoT) is becoming highly supportive in innovative technological solutions for assisting impaired people. Some of these IoT solutions are still in a prototyping phase ignoring possible attacks and the corresponding security defenses. This article proposes a learning-based approach for defending against perception-layer attacks performed on specific sensor types in smart furniture for impaired people. This approach is based on the analysis of time series by means of dynamic time warping algorithm for calculating similarity and a novel detector for identifying anomalies. This approach has been illustrated by defending against simulated perception-layer magnetic attacks on a smart cupboard with door magnetic sensors. The results show the performance of the proposed approach for properly identifying these attacks. In particular, these results advocate an accuracy about 95.5% per day.This work was supported in part by the research project Utilisation of IoT and Sensors in Smart Cities for Improving Quality of Life of Impaired People under Grant 52-2020, in part by the Ciudades Inteligentes Totalmente Integrales, Eficientes Y Sotenibles (CITIES) funded by the Programa Iberoamericano de Ciencia y Tecnologia para el Desarrollo (CYTED) under Grant 518RT0558, in part by the Diseno Colaborativo Para La Promocion Del Bienestar En Ciudades Inteligentes Inclusivas under Grant TIN2017-88327-R funded by the Spanish Council of Science, Innovation and Universities from the Spanish Government, and in part by the Ministerio de Economia y Competitividad in the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento under Grant TIN2017-84802-C2-1-P.Nasralla, MM.; García-Magariño, I.; Lloret, J. (2020). Defenses Against Perception-Layer Attacks on IoT Smart Furniture for Impaired People. IEEE Access. 8:119795-119805. https://doi.org/10.1109/ACCESS.2020.3004814S119795119805

Human-Centric AI for Trustworthy IoT Systems With Explainable Multilayer Perceptrons

[EN] Internet of Things (IoT) widely use analysis of data with artificial intelligence (AI) techniques in order to learn from user actions, support decisions, track relevant aspects of the user, and notify certain events when appropriate. However, most AI techniques are based on mathematical models that are difficult to understand by the general public, so most people use AI-based technology as a black box that they eventually start to trust based on their personal experience. This article proposes to go a step forward in the use of AI in IoT, and proposes a novel approach within the Human-centric AI field for generating explanations about the knowledge learned by a neural network (in particular a multilayer perceptron) from IoT environments. More concretely, this work proposes two techniques based on the analysis of artificial neuron weights, and another technique aimed at explaining each estimation based on the analysis of training cases. This approach has been illustrated in the context of a smart IoT kitchen that detects the user depression based on the food used for each meal, using a simulator for this purpose. The results revealed that most auto-generated explanations made sense in this context (i.e. 97.0%), and the execution times were low (i.e. 1.5 ms or lower) even considering the common configurations varying independently the number of neurons per hidden layer (up to 20), the number of hidden layers (up to 20) and the number of training cases (up to 4,000).This work was supported in part by the U.K. Engineering and Physical Sciences Research under Grant EP/N028155/1, in part by the Programa Iberoamericano de Ciencia y Tecnologia para el Desarrollo (CYTED) through the CITIES: Ciudades inteligentes totalmente integrales, eficientes y sotenibles under Grant 518RT0558, and in part by the Spanish council of Science, Innovation and Universities from the Spanish Government through the Diseno colaborativo para la promocion del bienestar en ciudades inteligentes inclusivas under Grant TIN2017-88327-R.García-Magariño, I.; Muttukrishnan, R.; Lloret, J. (2019). Human-Centric AI for Trustworthy IoT Systems With Explainable Multilayer Perceptrons. IEEE Access. 7:125562-125574. https://doi.org/10.1109/ACCESS.2019.2937521S125562125574

Smart Cupboard for Assessing Memory in Home Environment

Sensor systems for the Internet of Things (IoT) make it possible to continuously monitor people, gathering information without any extra effort from them. Thus, the IoT can be very helpful in the context of early disease detection, which can improve peoples'' quality of life by applying the right treatment and measures at an early stage. This paper presents a new use of IoT sensor systemswe present a novel three-door smart cupboard that can measure the memory of a user, aiming at detecting potential memory losses. The smart cupboard has three sensors connected to a Raspberry Pi, whose aim is to detect which doors are opened. Inside of the Raspberry Pi, a Python script detects the openings of the doors, and classifies the events between attempts of finding something without success and the events of actually finding it, in order to measure the user''s memory concerning the objects'' locations (among the three compartments of the smart cupboard). The smart cupboard was assessed with 23 different users in a controlled environment. This smart cupboard was powered by an external battery. The memory assessments of the smart cupboard were compared with a validated test of memory assessment about face-name associations and a self-reported test about self-perceived memory. We found a significant correlation between the smart cupboard results and both memory measurement methods. Thus, we conclude that the proposed novel smart cupboard successfully measured memory

Diseño de un sistema ciberfísico aplicado al ámbito de la salud.

Los Sistemas Ciber-Físicos (CPS) tienen la capacidad de coordinar el tratamiento de datos y sistemas de comunicación con el seguimiento y control de las entidades que se encuentran en el entorno físico. Los ejemplos de CPS incluyen sistemas de conducción autónoma, sistemas de monitorización médica, modelos de consumo energético inteligente, sistemas de control de procesos, monitorización de procesos de fabricación, monitorización de infraestructuras y carreteras, sistemas de robótica, domótica y pilotos automáticos aeronáuticos. Estos sistemas están formados por un conjunto de dispositivos como: sensores, unidades de procesamiento, dispositivos de comunicación y en muchos casos servicios en la nube. Debido a la naturaleza de los distintos tipos de hardware un CPS puede estar formado por múltiples dispositivos con diferentes arquitecturas, protocolos e interfaces y en una gran parte de los casos los CPS son sistemas híbridos y distribuidos.Esta tesis presenta el diseño de un CPS centrado en el entorno de la salud. El CPS consta de un armario inteligente de cocina. Este armario posee internamente una placa de procesamiento Raspberry PI que se encarga de recopilar las aperturas y los cierres que se efectúen en este. La función principal del armario es estimar síntomas de aparición de enfermedades neurodegenerativas como el Alzheimer.La tesis también presenta otros elementos en la elaboración de un CPS como medidas de seguridad para evitar ataques de denegación de servicios (DoS) de tal manera que el normal funcionamiento no se vea comprometido. Otro elemento que se tiene en cuenta es la manera de interactuar con el usuario, el cual es objeto de estudio según el tipo de usuario que esté usando el CPS. Finalmente se propone un modelo de consumo energético, el cual se basa en la rutina del usuario para efectuar un consumo de energía en los momentos más álgidos del día en relación a la rutina diaria del usuario.El CPS fue evaluado en un entorno acondicionado y controlado con 23 participantes voluntarios. La fase de evaluación se realizó en 2 pasos en los cuales los voluntarios tuvieron que memorizar y extraer alimentos según el examinador iba indicando, en cada paso se usaron 15 tipos de alimentos distintos. Una vez que se realizaron todas las extracciones cada voluntario tuvo que realizar un test bien conocido de caras y nombres. En este test los participantes deben recordar una cara asociada a un determinado nombre. Un total de 30 caras y nombres son visionados por los voluntarios de manera que cada pareja debe ser memorizado. La etapa final del test de caras y nombres consistió en preguntar a qué cara le pertenece un nombre de 4 posibles. Una vez los resultados de ambos test fueron obtenidos, se contrastaron mediante varios test estadísticos. El resultado arrojó una correlación estadísticamente significativa, esto significa que al ser el test de caras y nombres un test bien conocido y comprobado para medir la memoria, el CPS presentado también posee la misma características.Finalmente, en la tesis se expone cómo los componentes interactivos, eléctricos y de seguridad son diseñados y acoplados para obtener el CPS propuesto.Esta Tesis Doctoral se presenta como compendio de publicaciones editadas, de acuerdo al extracto de 25/06/2020 del Consejo de Gobierno de la Universidad de Zaragoza por el que se aprueba el Reglamento Sobre Tesis Doctorales (Título IV, Capítulo III, artículo 20).<br /