Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

A Role-Based Approach for Orchestrating Emergent Configurations in the Internet of Things

The Internet of Things (IoT) is envisioned as a global network of connected things enabling ubiquitous machine-to-machine (M2M) communication. With estimations of billions of sensors and devices to be connected in the coming years, the IoT has been advocated as having a great potential to impact the way we live, but also how we work. However, the connectivity aspect in itself only accounts for the underlying M2M infrastructure. In order to properly support engineering IoT systems and applications, it is key to orchestrate heterogeneous 'things' in a seamless, adaptive and dynamic manner, such that the system can exhibit a goal-directed behaviour and take appropriate actions. Yet, this form of interaction between things needs to take a user-centric approach and by no means elude the users' requirements. To this end, contextualisation is an important feature of the system, allowing it to infer user activities and prompt the user with relevant information and interactions even in the absence of intentional commands. In this work we propose a role-based model for emergent configurations of connected systems as a means to model, manage, and reason about IoT systems including the user's interaction with them. We put a special focus on integrating the user perspective in order to guide the emergent configurations such that systems goals are aligned with the users' intentions. We discuss related scientific and technical challenges and provide several uses cases outlining the concept of emergent configurations.Comment: In Proceedings of the Second International Workshop on the Internet of Agents @AAMAS201

Designing a goal-oriented smart-home environment

The final publication is available at Springer via http://dx.doi.org/10.1007/s10796-016-9670-x[EN] Nowadays, systems are growing in power and in access to more resources and services. This situation makes it necessary to provide user-centered systems that act as intelligent assistants. These systems should be able to interact in a natural way with human users and the environment and also be able to take into account user goals and environment information and changes. In this paper, we present an architecture for the design and development of a goal-oriented, self-adaptive, smart-home environment. With this architecture, users are able to interact with the system by expressing their goals which are translated into a set of agent actions in a way that is transparent to the user. This is especially appropriate for environments where ambient intelligence and automatic control are integrated for the user’s welfare. In order to validate this proposal, we designed a prototype based on the proposed architecture for smart-home scenarios. We also performed a set of experiments that shows how the proposed architecture for human-agent interaction increases the number and quality of user goals achieved.This work is partially supported by the Spanish Government through the MINECO/FEDER project TIN2015-65515-C4-1-R.Palanca Cámara, J.; Del Val Noguera, E.; García-Fornes, A.; Billhard, H.; Corchado, JM.; Julian Inglada, VJ. (2016). Designing a goal-oriented smart-home environment. Information Systems Frontiers. 1-18. https://doi.org/10.1007/s10796-016-9670-xS118Alam, M. R., Reaz, M. B. I., & Ali, M. A. M. (2012). A review of smart homes: Past, present, and future. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42(6), 1190–1203.Andrushevich, A., Staub, M., Kistler, R., & Klapproth, A. (2010). Towards semantic buildings: Goal-driven approach for building automation service allocation and control. In 2010 IEEE conference on emerging technologies and factory automation (ETFA) (pp. 1–6) IEEE.Ayala, I., Amor, M., & Fuentes, L. (2013). Self-configuring agents for ambient assisted living applications. Personal and Ubiquitous Computing, 17(6), 1159–1169.Cetina, C., Giner, P., Fons, J., & Pelechano, V. (2009). Autonomic computing through reuse of variability models at runtime: The case of smart homes. Computer, 42(10), 37–43.Cook, D. J. (2009). Multi-agent smart environments. Journal of Ambient Intelligence and Smart Environments, 1(1), 51–55.Dalpiaz, F., Giorgini, P., & Mylopoulos, J. (2009). An architecture for requirements-driven self-reconfiguration. In Advanced information systems engineering (pp. pp 246–260). Springer.De Silva, L. C., Morikawa, C., & Petra, I. M. (2012). State of the art of smart homes. Engineering Applications of Artificial Intelligence, 25(7), 1313–1321.Huhns, M., & et al. (2005). Research directions for service-oriented multiagent systems. IEEE Internet Computing, 9, 69–70.Iftikhar, M. U., & Weyns, D. (2014). Activforms: active formal models for self-adaptation. In SEAMS, (pp 125–134).Kucher, K., & Weyns, D. (2013). A self-adaptive software system to support elderly care. Modern Information Technology, MIT.Lieberman, H., & Espinosa, J. (2006). A goal-oriented interface to consumer electronics using planning and commonsense reasoning. In Proceedings of the 11th international conference on Intelligent user interfaces (pp. 226–233).Liu, H., & Singh, P. (2004). ConceptNet—a practical commonsense reasoning tool-kit. BT Technology Journal, 22(4), 211–226.Loseto, G., Scioscia, F., Ruta, M., & Di Sciascio, E. (2012). Semantic-based smart homes: a multi-agent approach. In 13th Workshop on objects and Agents (WOA 2012) (Vol. 892, pp. 49–55).Martin, D., Burstein, M., Hobbs, J., Lassila, O., McDermott, D., McIlraith, S., Narayanan, S., Paolucci, M., Parsia, B., Payne, T., & et al (2004). OWL-S: Semantic markup for web services. W3C Member Submission, 22, 2007–2004.Matthews, R. B., Gilbert, N. G., Roach, A., Polhill, J. G, & Gotts, N. M. (2007). Agent-based land-use models: a review of applications. Landscape Ecology, 22(10), 1447–1459.Molina, J. M., Corchado, J. M., & Bajo, J. (2008). Ubiquitous computing for mobile environments. In Issues in multi-agent systems (pp 33–57). Birkhäuser, Basel.Palanca, J., Navarro, M., Julian, V., & García-Fornes, A. (2012). Distributed goal-oriented computing. Journal of Systems and Software, 85(7), 1540–1557. doi: 10.1016/j.jss.2012.01.045 .Rao, A., & Georgeff, M. (1995). BDI agents: From theory to practice. In Proceedings of the first international conference on multi-agent systems (ICMAS95) (pp. 312–319).Reddy, Y. (2006). Pervasive computing: implications, opportunities and challenges for the society. In 1st International symposium on pervasive computing and applications (p. 5).de Silva, L., & Padgham, L. (2005). Planning as needed in BDI systems. International Conference on Automated Planning and Scheduling.Singh, P. (2002). The public acquisition of commonsense knowledge. In Proceedings of AAAI Spring symposium acquiring (and using) linguistic (and world) knowledge for information access

Smart kitchen for Ambient Assisted Living

El envejecimiento de la población es una realidad en todos los países desarrollados. Las predicciones de crecimiento de esta población son alarmantes, planteando un reto para los servicios sociales y sanitarios. Las personas ancianas padecen diversas discapacidades que se van acentuando con la edad, siendo más propensas a sufrir accidentes domésticos, presentando problemas para realizar tareas cotidianas, etc. Esta situación conlleva a una pérdida paulatina de capacidades que en muchas ocasiones acaba con la vida autónoma de la persona. En este contexto, las Tecnologías de la Información y Comunicación (TIC) aplicadas al entorno doméstico pueden jugar un papel importante, permitiendo que las personas ancianas vivan más tiempo, de forma independiente en su propio hogar, presentando, por tanto, una alternativa a la hospitalización o institucionalización de las mismas. Este trabajo da un paso más en este sentido, presentando el diseño y desarrollo de un Ambiente Inteligente en la cocina, que ayuda a las personas ancianas y/o con discapacidad a desempeñar sus actividades de la vida diaria de una forma más fácil y sencilla. Esta tesis realiza sus principales aportaciones en dos campos: El metodológico y el tecnológico. Por un lado se presenta una metodología sistemática para extraer necesidades de colectivos específicos a fin de mejorar la información disponible por el equipo de diseño del producto, servicio o sistema. Esta metodología se basa en el estudio de la interacción Hombre-Máquina en base a los paradigmas y modelos existentes y el modelado y descripción de las capacidades del usuario en la misma utilizado el lenguaje estandarizado propuesto en la Clasificación Internacional del Funcionamiento, de la Discapacidad y de la Salud (CIF). Adicionalmente, se plantea el problema de la evaluación tecnológica, diseñando la metodología de evaluación de la tecnología con la finalidad de conocer su accesibilidad, funcionalidad y usabilidad del sistema desarrollado y aplicándola a 61 usuarios y 31 profesionales de la gerontología. Desde un punto de vista técnico, se afronta el diseño de un ambiente asistido inteligente (Ambient Assisted Living, AAL) en la cocina, planteando y definiendo la arquitectura del sistema. Esta arquitectura, basada en OSGi (Open Services Gateway initiative), oferta un sistema modular, con altas capacidades de interoperabilidad y escalabilidad. Además, se diseña e implementa una red de sensores distribuida en el entorno con el fin de obtener la mayor información posible del contexto, presentando distintos algoritmos para obtener información de alto nivel: detección de caídas o localización. Todos los dispositivos presentes en el entorno han sido modelados utilizando la taxonomía propuesta en OSGi4AmI, extendiendo la misma a los electrodomésticos más habituales de la cocina. Finalmente, se presenta el diseño e implementación de la inteligencia del sistema, que en función de la información procedente del contexto y de las capacidades del usuario da soporte a las principales actividades de la vida diaria (AVD) en la cocina

Federated Embedded Systems – a review of the literature in related fields

This report is concerned with the vision of smart interconnected objects, a vision that has attracted much attention lately. In this paper, embedded, interconnected, open, and heterogeneous control systems are in focus, formally referred to as Federated Embedded Systems. To place FES into a context, a review of some related research directions is presented. This review includes such concepts as systems of systems, cyber-physical systems, ubiquitous computing, internet of things, and multi-agent systems. Interestingly, the reviewed fields seem to overlap with each other in an increasing number of ways

A smart home environment to support safety and risk monitoring for the elderly living independently

The elderly prefer to live independently despite vulnerability to age-related challenges. Constant monitoring is required in cases where the elderly are living alone. The home environment can be a dangerous environment for the elderly living independently due to adverse events that can occur at any time. The potential risks for the elderly living independently can be categorised as injury in the home, home environmental risks and inactivity due to unconsciousness. The main research objective was to develop a Smart Home Environment (SHE) that can support risk and safety monitoring for the elderly living independently. An unobtrusive and low cost SHE solution that uses a Raspberry Pi 3 model B, a Microsoft Kinect Sensor and an Aeotec 4-in-1 Multisensor was implemented. The Aeotec Multisensor was used to measure temperature, motion, lighting, and humidity in the home. Data from the multisensor was collected using OpenHAB as the Smart Home Operating System. The information was processed using the Raspberry Pi 3 and push notifications were sent when risk situations were detected. An experimental evaluation was conducted to determine the accuracy with which the prototype SHE detected abnormal events. Evaluation scripts were each evaluated five times. The results show that the prototype has an average accuracy, sensitivity and specificity of 94%, 96.92% and 88.93% respectively. The sensitivity shows that the chance of the prototype missing a risk situation is 3.08%, and the specificity shows that the chance of incorrectly classifying a non-risk situation is 11.07%. The prototype does not require any interaction on the part of the elderly. Relatives and caregivers can remotely monitor the elderly person living independently via the mobile application or a web portal. The total cost of the equipment used was below R3000

Monitoring elderly behavior via indoor position-based stigmergy

In this paper we present a novel approach for monitoring elderly people living alone and independently in their own homes. The proposed system is able to detect behavioral deviations of the routine indoor activities on the basis of a generic indoor localization system and a swarm intelligence method. For this reason, an in-depth study on the error modeling of state-of-the-art indoor localization systems is presented in order to test the proposed system under different conditions in terms of localization error. More specifically, spatiotemporal tracks provided by the indoor localization system are augmented, via marker-based stigmergy, in order to enable their self-organization. This allows a marking structure appearing and staying spontaneously at runtime, when some local dynamism occurs. At a second level of processing, similarity evaluation is performed between stigmergic marks over different time periods in order to assess deviations. The purpose of this approach is to overcome an explicit modeling of user's activities and behaviors that is very inefficient to be managed, as it works only if the user does not stray too far from the conditions under which these explicit representations were formulated. The effectiveness of the proposed system has been experimented on real-world scenarios. The paper includes the problem statement and its characterization in the literature, as well as the proposed solving approach and experimental settings

Contributing to the Internet of Things

Part 1: IntroductionInternational audienceThe fast development of networked smart devices equipped with sensors and radio-frequency identification, connected to the Internet, is enabling the emergence of many new applications and the redesign of traditional systems towards more effective operation. Raising awareness among engineering PhD students for the potential of this new wave in their research work is a crucial element in their education. With this aim, the doctoral conference DoCEIS’13 focused on technological innovation for the Internet of Things, challenging the contributors to analyze in which ways their technical and scientific work could contribute to or benefit from this paradigm. The results of this initiative, which was reasonably successful, are briefly analyzed

HIDE: User centred Domotic evolution toward Ambient Intelligence

Pervasive Computing and Ambient Intelligence (AmI) visions are still far from being achieved, especially with regard to Domotics and home applications. According to the vision of Ambient Intelligence (AmI), the most advanced technologies are those that disappear: at maturity, computer technology should become invisible. All the objects surrounding us must possess sufficient computing capacity to interact with users, the surroundings and each other. The entire physical environment in which users are immersed should thus be a hidden computer system equipped with the appropriate software in order to exhibit intelligent behavior. Even though many implementations have started to appear in several contexts, few applications have been made available for the home environment and the general public. This is mainly due to the segmentation of standards and proprietary solutions, which are currently confusing the market with a sparse offer of uninteroperable devices and systems. Although modern houses are equipped with smart technological appliances, still very few of these appliances can be seamlessly connected to each other. The objective of this research work is to take steps in these directions by proposing, on the one hand, a software system designed to make today’s heterogeneous, mostly incompatible domotic systems fully interoperable and, on the other hand, a feasible software application able to learn the behavior and habits of home inhabitants in order to actively contribute to anticipating user needs, and preventing emergency situations for his health. By applying machine learning techniques, the system offers a complete, ready-to-use practical application that learns through interaction with the user in order to improve life quality in a technological living environment, such as a house, a smart city and so on. The proposed solution, besides making life more comfortable for users without particular needs, represents an opportunity to provide greater autonomy and safety to disabled and elderly occupants, especially the critically ill ones. The prototype has been developed and is currently running at the Pisa CNR laboratory, where a home environment has been faithfully recreated