The design and realization of age-friendly living and working environments is a huge challenge that we have just only started to address as the number of older citizens who are and want to continue being active members of society and live independently is constantly increasing. SmartWork builds a worker-centric AI system for work ability sustainability, integrating unobtrusive sensing and modelling of the worker state with a suite of novel services for context and worker-aware adaptive work support. The unobtrusive and pervasive monitoring of health, behaviour, cognitive and emotional status of the worker enables the functional and cognitive decline risk assessment. The holistic approach for work ability modelling captures the attitudes and abilities of the ageing worker and enables decision support for personalized interventions for maintenance/improvement of the work ability. The evolving work requirements are translated into required abilities and capabilities, and the adaptive work environment supports the older office worker with optimized services for on-the-fly work flexibility coordination, seamless transfer of the work environment between different devices and different environments (home, office, on the move), and on-demand personalized training. The SmartWork services and modules also empower the employer with AI decision support tools for efficient task completion and work team optimization through flexible work practices. Optimization of team formation, driven by the semantic modelling of the work tasks, along with training needs prioritization at team level to identify unmet needs, allow employers to optimize tasks (e.g. needed resources), shifting focus on increased job satisfaction for increased productivity. Formal and informal carers are able to continuously monitor the overall health status and risks of the people they care for, thus providing full support to the older office worker for sustainable, active and healthy ageing.

Driving style is seen not only to become a significant cause of greenhouse gas (GHG) and other air pollutant emissions but also a critical parameter regarding road safety, with huge social & financial adverse effects. GamECAR aims to develop a highly innovative and interactive Serious Games platform that will empower and guide users to adopt an eco-friendly driving style. This will be achieved, without distracting users from safe driving, through a multidisciplinary approach aiming at the development of a user friendly, unobtrusive multi-player gaming environment, where the users will not only play collaboratively/competitively using their mobile device but also using the car itself and their own bodies, thus turning eco-driving into an immersive and highly motivating experience. The sensing infrastructure of GamECAR will not only acquire data related to driving from an OBD sensor that will capture a complex set of parameters related to eco-driving, but will also sense environmental and physiological parameters of the driver, so as to better position the state of the system (car) in context (environment, user). The use of virtual user models and cognitive modeling of the users, will further boost personalization and adaptation of the game itself with respect to the needs of the individual driver. The GamECAR system will be quantified and evaluated in test campaigns with drivers in three different sites. Quantification campaigns serve system development and evaluation campaigns demonstrate usefulness and exploitation potential. Finally, the project has a clear exploitation plan through a balanced and highly complementary composition of SMEs that have specific roles in the development of the integrated GamECAR system. The impact of such a holistic and innovative approach is huge and the foundations laid here are expected to result in a widespread adoption of sensor-based gamification platforms in areas going far beyond eco-driving.

The GAIA project focuses on the educational community; faculty, staff, students and parents at all levels of education: primary/secondary/high schools and universities. Targeting Energy Efficiency in the context of the educational community is clearly very important due to a number of reasons since raising awareness among young people and changing their behaviour and habits concerning energy usage is key to achieving sustained energy reductions and it will also indirectly affect their immediate family environment, while achieving energy reduction in the school buildings. GAIA will create an innovative ICT ecosystem (including web-based, mobile, social and sensing elements) tailored specifically for school environments, taking into account both the users (faculty, staff, students, parents) and buildings (schools, universities, homes) that will motivate and support citizens' behavioural change to achieve greater energy efficiency. GAIA will include also a set of pilots in different countries. GAIA will directly educate over 6.900 users, influence and attempt to transform their behaviour through a series of trials conducted in the educational environment and in homes. We expect a larger number of people to be informed about the activities of GAIA and be positively affected towards an energy-efficient behaviour transformation.

ELEGANT aims to solve the ever-increasing problem of software fragmentation in the IoT/Big Data interoperability domain. Software fragmentation prohibits the unification of these two ecosystems severely limiting the ability to regard them as a single system and tune the whole infrastructure towards defining its a) Performance, b) Energy Efficiency, c) Security, d) Reliability, and d) Dependability (PESRD) requirements. ELEGANT proposes a novel software programming paradigm, along with an associated set of methodologies and toolchains, to program IoT and Big Data frameworks using a unified programming framework. Its key proposed innovations in the areas of: a) Light-weight application virtualization, b) Automatic code extraction compatible with both IoT and Big Data frameworks, c) AI-assisted Intelligent Orchestration, d) dynamic code motion, and e) advanced code verification and cybersecurity mechanisms, will enable the seamless operation of end-to-end IoT/Big Data complex systems. This way, users employing the ELEGANT software stack and methodologies will be able to seamlessly define the pareto-optimal point in the PESRD optimization space while the entire system will be able to dynamically adjust itself during execution. To achieve its ambitious goals, ELEGANT assembles a consortium of experts across all domains ranging from low-level system software, IoT, Big Data, AI-assisted scheduling, and DevOps. Finally, the proposed solutions will be evaluated against pre-defined KPIs across a wide range of operational use cases from four distinct domains: health, automotive, smart metering, and video surveillance.

Imagine a Big Data application with the following characteristics: (i) it has to process large amounts of complex streaming data, (ii) the application logic that processes the incoming data must execute and complete within a strict time limit, and (iii) there is a limited budget for infrastructure resources. In today’s world, the data would be streamed from the local network or edge devices to a cloud provider which is rented by a customer to perform the data execution. The Big Data software stack, in an application and hardware agnostic manner, will split the execution stream into multiple tasks and send them for processing on the nodes the customer has paid for. If the outcome does not match the strict three second business requirement, then the customer has two options: 1) scale-up (by upgrading processors at node level), 2) scale-out (by adding nodes to their clusters), or 3) manually implement code optimizations specific to the underlying hardware. E2Data proposes an end-to-end solution for Big Data deployments that will fully exploit and advance the state-of-the-art in infrastructure services by delivering a performance increase of up to 10x while utilizing up to 50% less cloud resources. E2Data will provide a new Big Data paradigm, by combining state-of-the-art software components, in order to achieve maximum resource utilization for heterogeneous cloud deployments without affecting current programming norms (i.e. no code changes in the original source). The E2Data innovations will be driven by the requirements of four resource demanding applications from the finance, health, green buildings, and security domains. Finally, the evaluation will be conducted on both high-performing x86 and low-power ARM cluster architectures representing realistic execution scenarios of real-world deployments.