An IoT-aware AAL System to Capture Behavioral Changes of Elderly People

The ageing of population is a phenomenon that is affecting the majority of developed countries around the world and will soon affect developing economies too. In recent years, both industry and academia are focused on the development of several solutions aimed to guarantee a healthy and safe lifestyle to the elderly. In this context, the behavioral analysis of elderly people can help to prevent the occurrence of Mild Cognitive Impairment (MCI) and frailty problems. The innovative technologies enabling the Internet of Things (IoT) can be used in order to capture personal data for automatically recognizing changes in elderly people behavior in an unobtrusive, low-cost and low-power modality. This work aims to describe the ongoing activities within the City4Age project, funded by the Horizon 2020 Programme of the European Commission, mainly focused on the use of IoT technologies to develop an innovative AAL system able to capture personal data of elderly people in their home and city environments. The proposed architecture has been validated through a proof-of-concept focused mainly on localization issues, collection of ambient parameters, and user-environment interaction aspects

Ambient intelligence in buildings : design and development of an interoperable Internet of Things platform

During many years, people and governments have been warned about the increasing levels of pollution and greenhouse gases (GHG) emissions that are endangering our lives on this planet. The Information and Communication Technology sector, usually known as the ICT sector, responsible for the computerization of the society, has been pinpointed as one of the most important sectors contributing to such a problem. Many efforts, however, have been put to shift the trend towards the utilization of renewable resources, such as wind or solar power. Even though governments have agreed to follow this path and avoid the usage of non-renewable energies, it is not enough. Although the ICT sector might seem an added problem due to the number of connected devices, technology improvements and hardware optimization enable new ways of fighting against global warming and GHG emissions. The aforementioned computerization has forced companies to evolve their work into a computer-assisted one. Due to this, companies are now forced to establish their main headquarters inside buildings for work coordination, connection and management. Due to this, buildings are becoming one of the most important issues regarding energy consumption. In order to cope with such problem, the Internet of Things (IoT) offers new paradigms and alternatives for leading the change. IoT is commonly defined as the network of physical and virtual objects that are capable of collecting surrounding data and exchanging it between them or through the Internet. Thanks to these networks, it is possible to monitor any thinkable metric inside buildings, and, then, utilize this information to build efficient automated systems, commonly known as Building Energy Management Systems (BEMS), capable of extracting conclusions on how to optimally and efficiently manage the resources of the building. ICT companies have foreseen this market opportunity that, paired with the appearance of smaller, efficient and more durable sensors, allows the development of efficient IoT systems. However, the lack of agreement and standardization creates chaos inside IoT, and the horizontal connectivity between such systems is still a challenge. Moreover, the vast amount of data to process requires the utilization of Big Data techniques to guarantee close to real-time responses. This thesis initially presents a standard Cloud-based IoT architecture that tries to cope with the aforementioned problems by employing a Cloud middleware that obfuscates the underlying hardware architecture and permits the aggregation of data from multiple heterogeneous sources. Also, sensor information is exposed to any third-party client after authentication. The utilization of automated IoT systems for managing building resources requires high reliability, resilience, and availability. The loss of sensor data is not permitted due to the negative consequences it might have, such as disruptive resource management. For this, it is mandatory to grant backup options to sensor networks in order to guarantee correct functioning in case of partial network disconnections. Additionally, the placement of the sensors inside the building must guarantee minimal energy consumption while fulfilling sensing requirements. Finally, a building resource management use case is presented by means of a simulation tool. The tool draws on occupants' probabilistic models and environmental condition models for actuating upon building elements to ensure optimal and efficient functioning. Occupants' comfort is also taken into consideration and the trade-off between the two metrics is studied. All the presented work is meant to deliver insights and tools for current and future IoT system implementations by setting the basis for standardization agreements yet to happen.Durant molts anys, s'ha alertat a la població i als governs sobre l'increment en els nivells de pol·lució i d'emissió de gasos d'efecte hivernacle, que estan posant en perill la nostra vida a la Terra. El sector de les Tecnologies de la Informació i Comunicació, normalment conegut com les TIC, responsable de la informatització de la societat, ha estat senyalat com un dels sectors més importants encarregat d'agreujar tal problema. Però, molt esforç s'està posant per revertir aquesta situació mitjançant l'ús de recursos renovables, com l'energia eòlica o solar. Tot i que els governs han acordat seguir dit camí i evitar l'ús d'energia no renovable tant com sigui possible, no és suficient per erradicar el problema. Encara que el sector de les TIC pugui semblar un problema afegit donada la gran quantitat i l'increment de dispositius connectats, les millores en tecnologia i en hardware estan habilitant noves maneres de lluitar contra l'escalfament global i l'emissió de gasos d'efecte hivernacle. La informatització, anteriorment mencionada, ha forçat a les empreses a evolucionar el seu model de negoci cap a un més enfocat a la utilització de xarxes d'ordinadors per gestionar els seus recursos. Per això, dites companyies s'estan veient forçades a establir les seves seus centrals dintre d'edificis, per tenir un major control sobre la coordinació, connexió i maneig dels seus recursos. Això està provocant un augment en el consum energètic dels edificis, que s'estan convertint en un dels principals problemes. Per poder fer front al problema, la Internet de les Coses o Internet of Things (IoT) ofereix nous paradigmes i alternatives per liderar el canvi. IoT es defineix com la xarxa d'objectes físics i virtuals, capaços de recol·lectar la informació per construir sistemes automatitzats, coneguts com a Sistemes de Gestió Energètica per Edificis, capaços d'extreure conclusions sobre com utilitzar de manera eficient i òptima els recursos de l'edifici. Companyies pertanyents a les TIC han previst aquesta oportunitat de mercat que, en sincronia amb l'aparició de sensors més petits, eficients i duradors, permeten el desenvolupament de sistemes IoT eficients. Però, la falta d'acord en quant a l'estandardització de dits sistemes està creant un escenari caòtic, ja que s'està fent impossible la connectivitat horitzontal entre dits sistemes. A més, la gran quantitat de dades a processar requereix la utilització de tècniques de Big Data per poder garantir respostes en temps acceptables. Aquesta tesi presenta, inicialment, una arquitectura IoT estàndard basada en la Neu, que tracta de fer front als problemes anteriorment presentats mitjançant l'ús d'un middleware allotjat a la Neu que ofusca l'arquitectura hardware subjacent i permet l'agregació de la informació originada des de múltiples fonts heterogènies. A més, la informació dels sensors s'exposa perquè qualsevol client de tercers pugui consultar-la, després d'haver-se autenticat. La utilització de sistemes IoT automatitzats per gestionar els recursos dels edificis requereix un alt nivell de fiabilitat, resistència i disponibilitat. La perduda d'informació no està permesa degut a les conseqüències negatives que podría suposar, com una mala presa de decisions. Per això, és obligatori atorgar opcions de backup a les xarxes de sensors per garantir un correcte funcionament inclús quan es produeixen desconnexions parcials de la xarxa. Addicionalment, la col·locació dels sensors dintre de l'edifici ha de garantir un consum energètic mínim dintre de les restriccions de desplegament imposades. Finalment, presentem un cas d'ús d'un Sistema de Gestió Energètica per Edificis mitjançant una eina de simulació. Dita eina utilitza com informació d'entrada models probabilístics sobre les accions dels ocupants i models sobre la condició ambiental per actuar sobre els elements de l'edifici i garantir un funcionament òptim i eficient. A més, el confort dels ocupants també es considera com mètrica a optimitzar. Donada la impossibilitat d’optimitzar les dues mètriques de manera conjunta, aquesta tesi també presenta un estudi sobre el trade-off que existeix entre elles. Tot el treball presentat està pensat per atorgar idees i eines pels sistemes IoT actuals i futurs, i assentar les bases per l’estandardització que encara està per arribar.Durante muchos años, se ha alertado a la población y a los gobiernos acerca del incremento en los niveles de polución y de emisión de gases de efecto invernadero, que están poniendo en peligro nuestra vida en la Tierra. El sector de las Tecnologías de la Información y Comunicación, normalmente conocido como las TIC, responsable de la informatización de la sociedad, ha sido señalada como uno de los sectores más importantes encargado de agravar tal problema. Sin embargo, mucho esfuerzo se está poniendo para revertir esta situación mediante el uso de recursos renovables, como la energía eólica o solar. A pesar de que los gobiernos han acordado seguir dicho camino y evitar el uso de energía no renovable tanto como sea posible, no es suficiente para erradicar el problema. Aunque el sector de las TIC pueda parecer un problema añadido dada la gran cantidad y el incremento de dispositivos conectados, las mejoras en tecnología y en hardware están habilitando nuevas maneras de luchar contra el calentamiento global y la emisión de gases de efecto invernadero. Durante las últimas décadas, compañías del sector público y privado conscientes del problema han centrado sus esfuerzos en la creación de soluciones orientadas a la eficiencia energética tanto a nivel de hardware como de software. Las nuevas redes troncales están siendo creadas con dispositivos eficientes y los proveedores de servicios de Internet tienden a crear sistemas conscientes de la energía para su optimización dentro de su dominio. Siguiendo esta tendencia, cualquier nuevo sistema creado y añadido a la red debe garantizar un cierto nivel de conciencia y un manejo óptimo de los recursos que utiliza. La informatización, anteriormente mencionada, ha forzado a las empresas a evolucionar su modelo de negocio hacia uno más enfocado en la utilización de redes de ordenadores para gestionar sus recursos. Por eso, dichas compañías se están viendo forzadas a establecer sus sedes centrales dentro de edificios, para tener un mayor control sobre la coordinación, conexión y manejo de sus recursos. Esto está provocando un aumento en el consumo energético de los edificios, que se están convirtiendo en uno de los principales problemas. Para poder hacer frente al problema, el Internet de las Cosas o Internet of Things (IoT) ofrece nuevos paradigmas y alternativas para liderar el cambio. IoT se define como la red de objetos físicos y virtuales, capaces de recolectar la información del entorno e intercambiarla entre los propios objetos o a través de Internet. Gracias a estas redes, es posible monitorizar cualquier métrica que podamos imaginar dentro de un edificio, y, después, utilizar dicha información para construir sistemas automatizados, conocidos como Sistemas de Gestión Energética para Edificios, capaces de extraer conclusiones sobre cómo utilizar de manera eficiente y óptima los recursos del edificio. Compañías pertenecientes a las TIC han previsto esta oportunidad de mercado que, en sincronía con la aparición de sensores más pequeños, eficientes y duraderos, permite el desarrollo de sistemas IoT eficientes. Sin embargo, la falta de acuerdo en cuanto a la estandarización de dichos sistemas está creando un escenario caótico, ya que se hace imposible la conectividad horizontal entre dichos sistemas. Además, la gran cantidad de datos a procesar requiere la utilización de técnicas de Big Data para poder garantizar respuestas en tiempos aceptables. Esta tesis presenta, inicialmente, una arquitectura IoT estándar basada en la Nube que trata de hacer frente a los problemas anteriormente presentados mediante el uso de un middleware alojado en la Nube que ofusca la arquitectura hardware subyacente y permite la agregación de la información originada des de múltiples fuentes heterogéneas. Además, la información de los sensores se expone para que cualquier cliente de terceros pueda consultarla, después de haberse autenticado. La utilización de sistemas IoT automatizados para manejar los recursos de los edificios requiere un alto nivel de fiabilidad, resistencia y disponibilidad. La pérdida de información no está permitida debido a las consecuencias negativas que podría suponer, como una mala toma de decisiones. Por eso, es obligatorio otorgar opciones de backup a las redes de sensores para garantizar su correcto funcionamiento incluso cuando se producen desconexiones parciales de la red. Adicionalmente, la colocación de los sensores dentro del edificio debe garantizar un consumo energético mínimo dentro de las restricciones de despliegue impuestas. En esta tesis, mejoramos el problema de colocación de los sensores para redes heterogéneas de sensores inalámbricos añadiendo restricciones de clustering o agrupamiento, para asegurar que cada tipo de sensor es capaz de obtener su métrica correspondiente, y restricciones de protección mediante la habilitación de rutas de transmisión secundarias. En cuanto a grandes redes homogéneas de sensores inalámbricos, esta tesis estudia aumentar su resiliencia mediante la identificación de los sensores más críticos. Finalmente, presentamos un caso de uso de un Sistema de Gestión Energética para Edificios mediante una herramienta de simulación. Dicha herramienta utiliza como información de entrada modelos probabilísticos sobre las acciones de los ocupantes y modelos sobre la condición ambiental para actuar sobre los elementos del edificio y garantizar un funcionamiento óptimo y eficiente. Además, el comfort de los ocupantes también se considera como métrica a optimizar. Dada la imposibilidad de optimizar las dos métricas de manera conjunta, esta tesis también presenta un estudio sobre el trade-off que existe entre ellas. Todo el trabajo presentado está pensado para otorgar ideas y herramientas para los sistemas IoT actuales y futuros, y asentar las bases para la estandarización que todavía está por llegar.Postprint (published version

A critical analysis of an IoT—aware AAL system for elderly monitoring

Abstract A growing number of elderly people (65+ years old) are affected by particular conditions, such as Mild Cognitive Impairment (MCI) and frailty, which are characterized by a gradual cognitive and physical decline. Early symptoms may spread across years and often they are noticed only at late stages, when the outcomes remain irrevocable and require costly intervention plans. Therefore, the clinical utility of early detecting these conditions is of substantial importance in order to avoid hospitalization and lessen the socio-economic costs of caring, while it may also significantly improve elderly people's quality of life. This work deals with a critical performance analysis of an Internet of Things aware Ambient Assisted Living (AAL) system for elderly monitoring. The analysis is focused on three main system components: (i) the City-wide data capturing layer, (ii) the Cloud-based centralized data management repository, and (iii) the risk analysis and prediction module. Each module can provide different operating modes, therefore the critical analysis aims at defining which are the best solutions according to context's needs. The proposed system architecture is used by the H2020 City4Age project to support geriatricians for the early detection of MCI and frailty conditions

An IoT-Aware Approach for Elderly-Friendly Cities

The ever-growing life expectancy of people requires the adoption of proper solutions for addressing the particular needs of elderly people in a sustainable way, both from service provision and economic point of view. Mild cognitive impairments and frailty are typical examples of elderly conditions which, if not timely addressed, can turn out into more complex diseases that are harder and costlier to treat. Information and communication technologies, and in particular Internet of Things technologies, can foster the creation of monitoring and intervention systems, both on an ambient-assisted living and smart city scope, for early detecting behavioral changes in elderly people. This allows to timely detect any potential risky situation and properly intervene, with benefits in terms of treatment's costs. In this context, as part of the H2020-funded City4Age project, this paper presents the data capturing and data management layers of the whole City4Age platform. In particular, this paper deals with an unobtrusive data gathering system implementation to collect data about daily activities of elderly people, and with the implementation of the related linked open data (LOD)-based data management system. The collected data are then used by other layers of the platform to perform risk detection algorithms and generate the proper customized interventions. Through the validation of some use-cases, it is demonstrated how this scalable approach, also characterized by unobtrusive and low-cost sensing technologies, can produce data with a high level of abstraction useful to define a risk profile of each elderly person

A systematic review of machine learning techniques related to local energy communities

In recent years, digitalisation has rendered machine learning a key tool for improving processes in several sectors, as in the case of electrical power systems. Machine learning algorithms are data-driven models based on statistical learning theory and employed as a tool to exploit the data generated by the power system and its users. Energy communities are emerging as novel organisations for consumers and prosumers in the distribution grid. These communities may operate differently depending on their objectives and the potential service the community wants to offer to the distribution system operator. This paper presents the conceptualisation of a local energy community on the basis of a review of 25 energy community projects. Furthermore, an extensive literature review of machine learning algorithms for local energy community applications was conducted, and these algorithms were categorised according to forecasting, storage optimisation, energy management systems, power stability and quality, security, and energy transactions. The main algorithms reported in the literature were analysed and classified as supervised, unsupervised, and reinforcement learning algorithms. The findings demonstrate the manner in which supervised learning can provide accurate models for forecasting tasks. Similarly, reinforcement learning presents interesting capabilities in terms of control-related applications.publishedVersio

Feed-Forward Neural Network (FFNN) Based Optimization Of Air Handling Units: A State-Of-The-Art Data-Driven Demand-Controlled Ventilation Strategy

Indiana University-Purdue University Indianapolis (IUPUI)Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous studies showed that CO2-based demand-controlled ventilation (DCV) methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO2 mass balanced models do not yield an optimal estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO2 concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of the air handling unit (AHU) associated with the zone. IAQ and thermal comfort standards were also taken into consideration as the active constraints of this optimization. As for the validation, the experiment was carried out in an auditorium located on a university campus. The results revealed that utilizing neural network occupancy estimation model can reduce the daily ventilation energy by 74.2% when compared to the current on/off control

Energy-aware Occupancy Scheduling

Buildings are the largest consumers of energy worldwide. Within a building, heating, ventilation and air-conditioning (HVAC) systems consume the most energy, leading to trillion dollars of electrical expenditure worldwide each year. With rising energy costs and increasingly stringent regulatory environments, improving the energy efficiency of HVAC operations in buildings has become a global concern. From a short-term economic point-of-view, with over 100 billion dollars in annual electricity expenditures, even a small percentage improvement in the operation of HVAC systems can lead to significant savings. From a long-term point-of-view, the need of fostering a smart and sustainable built environment calls for the development of innovative HVAC control strategies in buildings. In this thesis, we look at the potential for integrating building operations with room booking and occupancy scheduling. More specifically, we explore novel approaches to reduce HVAC consumption in commercial buildings, by jointly optimising the occupancy scheduling decisions (e.g. the scheduling of meetings, lectures, exams) and the building’s occupancy-based HVAC control. Our vision is to integrate occupancy scheduling with HVAC control, in such a way that the energy consumption is reduced, while the occupancy thermal comfort and scheduling requirements are addressed. We identify four unique research challenges which we simultaneously tackle in order to achieve this vision, and which form the major contributions of this thesis. Our first contribution is an integrated model that achieves high efficiency in energy reduction by fully exploiting the capability to coordinate HVAC control and occupancy scheduling. The core component of our approach is a mixed-integer linear programming (MILP) model which optimally solves the joint occupancy scheduling and occupancy-based HVAC control problem. Existing approaches typically solve these subproblems in isolation: either scheduling occupancy given conventional control policies, or optimising HVAC control using a given occupancy schedule. From a computation standpoint, our joint problem is much more challenging than either, as HVAC models are traditionally non-linear and non-convex, and scheduling models additionally introduce discrete variables capturing the time slot and location at which each activity is scheduled. We find that substantial reduction in energy consumption can be achieved by solving the joint problem, compared to the state of the art approaches using heuristic scheduling solutions and to more naïve integrations of occupancy scheduling and occupancy-based HVAC control. Our second contribution is an approach that scales to large occupancy scheduling and HVAC control problems, featuring hundreds of activity requests across a large number of offices and rooms. This approach embeds the integrated MILP model into Large Neighbourhood Search (LNS). LNS is used to destroy part of the schedule and MILP is used to repair the schedule so as to minimise energy consumption. Given sets of occupancy schedules with different constrainedness and sets of buildings with varying thermal response, our model is sufficiently scalable to provide instantaneous and near-optimal solutions to problems of realistic size, such as those found in university timetabling. The third contribution is an online optimisation approach that models and solves the online joint HVAC control and occupancy scheduling problem, in which activity requests arrive dynamically. This online algorithm greedily commits to the best schedule for the latest activity requests, but revises the entire future HVAC control strategy each time it considers new requests and weather updates. We ensure that whilst occupants are instantly notified of the scheduled time and location for their requested activity, the HVAC control is constantly re-optimised and adjusted to the full schedule and weather updates. We demonstrate that, even without prior knowledge of future requests, our model is able to produce energy-efficient schedules which are close to the clairvoyant solution. Our final contribution is a robust optimisation approach that incorporates adaptive comfort temperature control into our integrated model. We devise a robust model that enables flexible comfort setpoints, encouraging energy saving behaviors by allowing the occupants to indicate their thermal comfort flexibility, and providing a probabilistic guarantee for the level of comfort tolerance indicated by the occupants. We find that dynamically adjusting temperature setpoints based on occupants’ thermal acceptance level can lead to significant energy reduction over the conventional fixed temperature setpoints approach. Together, these components deliver a complete optimisation solution that is efficient, scalable, responsive and robust for online HVAC-aware occupancy scheduling in commercial buildings

NETWORKED MICROGRID OPTIMIZATION AND ENERGY MANAGEMENT

Military vehicles possess attributes consistent with a microgrid, containing electrical energy generation, storage, government furnished equipment (GFE), and the ability to share these capabilities via interconnection. Many military vehicles have significant energy storage capacity to satisfy silent watch requirements, making them particularly well-suited to share their energy storage capabilities with stationary microgrids for more efficient energy management. Further, the energy generation capacity and the fuel consumption rate of the vehicles are comparable to standard diesel generators, for certain scenarios, the use of the vehicles could result in more efficient operation. Energy management of a microgrid is an open area of research especially in generation constrained scenarios where shedding of low-priority loads may be required. Typical metrics used to assess the effectiveness of an energy management strategy or policy include fuel consumption, electrical storage energy requirements, or the net exergy destruction. When considering a military outpost consisting of a stationary microgrid and a set of vehicles, the metrics used for managing the network become more complex. For example, the metrics used to manage a vehicle’s onboard equipment while on patrol may include fuel consumption, the acoustic signature, and the heat signature. Now consider that the vehicles are parked at an outpost and participating in vehicle-to-grid power-sharing and control. The metrics used to manage the grid assets may now include fuel consumption, the electrical storage’s state of charge, frequency regulation, load prioritization, and load dispatching. The focus of this work is to develop energy management and control strategies that allow a set of diverse assets to be controlled, yielding optimal operation. The provided policies result in both short-term and long-term optimal control of the electrical generation assets. The contributions of this work were: (1) development of a methodology to generate a time-varying electrical load based on (1) a U.S. Army-relevant event schedule and (2) a set of meteorological conditions, resulting in a scenario rich environment suitable for modeling and control of hybrid AC/DC tactical military microgrids, (2) the development of a multi-tiered hierarchical control architecture, suitable for development of both short and long term optimal energy management strategies for hybrid electric microgrids, and (3) the development of blending strategies capable of blending a diverse set of heterogeneous assets with multiple competing objective functions. This work could be extended to include a more diverse set of energy generation assets, found within future energy networks