SmartSpaces: aware of users, preferences, behavioursandhabits, in a non-invasive approach

The aim of this work is to take advantage of emerging technologies available in the market that support the so called wearable devices, and the non-invasive particularity of these to, in an autonomous way, adapt the environment to the comfort parameters of each user (e.g. thermal, acoustic, air quality, light, sun exposure). Provide comfort according to the preferences of each individual, is a challenge and an opportunity to create innovative solutions and new paradigms in the context of Intelligent Environments. Currently this challenge has as main difficulties, the people’s mobility, the disparity of habits, schedules and the individual comfort preferences. The same is aggravated when depending on physiological conditions, derived from a large number of factors (tiredness, mood, etc.), user preferences often suffer significant changes, that current systems can not measure.info:eu-repo/semantics/publishedVersio

Manage users and spaces security constraints on a multi-agent system in a adaptive environment system

Nowadays managing user preferences and local actuators specifications is an actual problem on IoT adaptive systems. This paper proposes a multi-agent system to achieve a Smart Environment System, that manages the interaction between persons and physical spaces, where spaces smartly adapt to user preferences in a transparent way. With this work, we also propose a set of security customization to secure actuators and users present in managed spaces, that has been developed using a multi-agent system architecture with different features to achieve a solution to support all proposed objectives.info:eu-repo/semantics/publishedVersio

Prediction tools for student learning assessment in professional schools

Professional Schools are in need to access technologies and tools that allow the monitoring of a student evolution course, in acquiring a given skill. Furthermore, they need to be able to predict the presentation of the students on a course before they actually sign up, to either provide them with the extra skills required to succeed, or to adapt the course to the students’ level of knowledge. Based on a knowledge base of student features, the Student Model, a Student Prediction System must be able to produce estimates on whether a student will succeed on a particular course. This tool must rely on a formal methodology for problem solving to estimate a measure of the quality-ofinformation that branches out from students’ profiles, before trying to guess their likelihood of success. Indeed, this paper presents an approach to design a Student Prediction System, which is, in fact, a reasoner, in the sense that, presented with a new problem description (a student outline) it produces a solved problem, i.e., a diagnostic of the student potential of success

Expert systems: Special issue on “New trends and Innovations in Intelligent Distributed Computing”

[Excerpt] Distributed Systems current face new challenges of adapting and reusing research results in the area of Intelligent Systems. Intelligent Systems use methods and technology derived from Knowledge‐based and Computational Intelligence. Distributed Computing develops methods and technology to build systems composed of collaborating components. The fast growth of both Big Data and Data Mining have created interesting challenges for classical methods, algorithms, and frameworks from Distributed Computing, which makes especially interesting analysis and research into new trends and innovations that have recently appeared in this area. This special issue welcomed submissions of original papers introducing research results on all the aspects covering the roles of Knowledge and Intelligence in Distributed Systems, ranging from concepts and theoretical developments to advanced technologies and innovative applications. This issue presents a expanded versions of these papers from the best of those presented at the 10th International Symposium on Intelligent Distributed Computing (IDC 2016), which was held in Paris (France). [...]As the special issue editors, we would like to take this opportunity to thank the various authors for their papers and the reviewers fortheir work. We are also grateful to Jon Hall, Editor-in-Chief of the Wiley journal Expert Systems. We would like to particularly thankthe IDC'16 programme committee members for their hard work and dedication. To conclude, we would like to acknowledge the financialsupport received from Spanish Ministry of Economy and Competitiveness (MINECO) projects: EphemeCH (TIN2014-56494-C4-{1… 4}-P)and DeepBio (TIN2017-85727-C4-{1… 4}-P), both under the European Regional Development Fund FEDER and the support by COMPETE:POCI-01-0145-FEDER-007043 and FCT Fundao para a Cincia e Tecnologia within the Project Scope: UID/CEC/00319/2013

Behaviour analysis in smart spaces

We are on a new era of interaction between persons and physical spaces. Users want that those spaces smartly adapt to their preferences in a transparent way. This paper describes the process of planning, reasoning and modeling of a Smart Environment with domestic and industrial application, taking advantage of emerging wearable devices on the market (smart watches, fitness trackers, etc.) and newer wireless communication technologies (NFC, BLE, Wi-Fi Direct). Enabling in a noninvasive way for the user, optimize the efficiency, comfort, and safety at the environments. This approach can be applied in home automation, public spaces and also incorporated at industrial level, to help build smart and autonomous factories.info:eu-repo/semantics/publishedVersio

Adaptive system to manage everyday user comfort preferences

Urban mobility brings many challenges and opportunities, particularly regarding sustainability. It is natural that we want better living conditions, we are naturally given to consuming, even when there is no need, we increasingly want to travel, socialize, enjoy and it is not easy to accept that we will most likely have to change. It is no longer a distant future, but the present that we are living. Even in the face of successful solutions, receptivity is far from being massified and in most cases it imposes compromises in terms of comfort and quality of life, sometimes even imposing new habits and ways of being. In addition, not all of us have the same perception of the situation seriousness, or the same willingness to compromise. And this can happen for numerous reasons, namely physical or health limitations, financial limitations, different beliefs/motivations, or different ways of facing problems. It is even common that the staunchest defender of certain solutions, when faced with other equally plausible solutions, is completely insensitive or even opposed. In fact, the same individual may have different needs/preferences relatively to the place where he is or the activity he is performing, that is, preferences that vary with time and place. In a broader context of mobility, in which individuals in their daily lives move and visit different places, often with the presence of more people, the situation is even more complex, the variability of preferences increases, and it is necessary to combine preferences/needs of different individuals. Emerging technologies, within the Internet of Things (IoT) scope and smart spaces [1], allow us to aspire to capable solutions in line with the urban mobility and sustainability demands and, at the same time, to promote better conditions of comfort and well-being, without imposing sacrifices or changes in habits and considering the specificities of each individual, at different time and place. These solutions whose success depends in part on the autonomy of operation, not requiring any direct and conscious participation of people, for the ability to make the best decisions given the current context and future expectations, the context being defined by the characteristics of the environment. Including the dynamics, namely those resulting from the presence/involvement of people, but also for the transparency of action, not being evasive and, if possible, fulfilling its function without people realizing the existence of the technology/solution simply the most convenient happens. There are other factors that should not be neglected, such as those related to security and privacy. In this paper, the authors propose an architecture that considers these requirements so that, in a non-evasive way, it adapts the different spaces that the user frequents (house, work, leisure, others) to their personal preferences, such as temperature, humidity, sound, environment, etc. The architecture includes the different devices needed, to identify users, as well as the communication technologies to be used to transfer the preferences of each user to the system. The architecture includes a multi-agent system that allows managing conflicts of preferences through a user’s hierarchy and that considers safety values for each preference, to safeguard the different actuators (air conditioning, fan coils, multimedia, etc.) present in space. It was developed, focusing on the definition of each user's preferences in a smartphone application, which allows the user's preferences to be transferred to the space, without the need to perform any interaction, they can also be passed through smartwatches, fitness bracelets and similar devices, which currently have different communication technologies such as Bluetooth Low Energy (BLE), Near Field Communication (NFC) or Wifi-Direct. It also contains a local processing solution, currently supported by a Raspberry Pi, and will be present in each space where we want to adapt to different preferences. Each of these systems constantly receives each present user preferences. Based on the multi-agent system, it calculates the optimal preferences to be applied to each space at a given time. It is also responsible for sending these to the different actuators present in the space. The multi-agent system has different layers (simulation, data acquisition, user information, actuation). Briefly, there is an agent for each user present, containing their preferences, and there is an agent that represents the pace, containing eventual constraints, such as security values and others that may exist, namely in public spaces. Each of these agents aims to represent the interests of the involved parties. For example, the agent representing the space should be focused on an efficient use of equipment, minimizing energy costs, enhancing the durability of the equipment, minimizing maintenance costs. Taking advantage of the different hierarchies, an equation was devised that meets the different preferences to define the optimal solution, which will be sent to the different actuators.info:eu-repo/semantics/publishedVersio

Challenges in smart spaces: aware of users, preferences, behaviours and habits

There are new opportunities for research in the field of smart environments that should be explored. The concepts of smart homes and home automation, are currently in growing expansion in the scientific and research point of view, as the market demands for better solutions in this field. Users want that those spaces smartly adapt to their preferences in a transparent way. This paper describes the process of planning, reasoning and modeling of a Smart Environment, using emerging wearable devices on the market (smart watches, fitness trackers, etc.) and newer technologies like NFC, BLE and Wi-Fi Direct. Enabling the user to optimize the efficiency, comfort, and safety at the environments.info:eu-repo/semantics/publishedVersio

Gerir preferências de conforto e conflitos num ambiente inteligente

Managing comfort preferences conflicts of the different users and locals on an IoT adaptive system is an actual problem, this paper proposes a protocol and hierarchical rules to develop a multi-agent system to achieve a smart environment that supports interaction between persons and physical spaces, where spaces smartly adapt to their preferences in a transparent way. And a set of security customizations to secure the actuators and users on space, that has been developed using a multi-agent system architecture with different features to achieve a solution that support the proposed objectives. This work resulted in the specification of an architecture that supports the solution found, to solve the problem. The agent system model is fully developed. At this stage the agent layer was developed, implemented, and is now in a testing phase. Now it will be tested and validated using real case studies, to gather statistical information to assess its effectiveness and performance. With this work, the specification of constraints for all preferences specifications was achieved. In this way the safety of users and actuators present in space is achieved. Also, the total development of an architecture and respective cognitive model for a Smart Home was achieved, using a multi-agent system with BDI agents, developed using Jason and ARGO. The main objective of this work was to verify the potential that this type of architecture has for the development of ubiquitous multi-agent system using low-cost hardware, such as Raspberry’s.info:eu-repo/semantics/publishedVersio

SmartSpaces: aware of users, preferences, behavioursandhabits, in a non-invasive approach

The aim of this work is to take advantage of emerging technologies available in the market that support the so called wearable devices, and the non-invasive particularity of these to, in an autonomous way, adapt the environment to the comfort parameters of each user (e.g. thermal, acoustic, air quality, light, sun exposure). Provide comfort according to the preferences of each individual, is a challenge and an opportunity to create innovative solutions and new paradigms in the context of Intelligent Environments. Currently this challenge has as main difficulties, the people’s mobility, the disparity of habits, schedules and the individual comfort preferences. The same is aggravated when depending on physiological conditions, derived from a large number of factors (tiredness, mood, etc.), user preferences often suffer significant changes, that current systems can not measure. Figure 1, shows the development environment of this work. Explaining this figure, it can be seen the user who through its different devices (smartphone, wearable, and other compatible) communicates with the system, using technologies, like Near Field Communication (NFC), Bluetooth Low Energy (BLE) or Wi-Fi Direct. Next, the system performs communication with the Cloud, to validate the information. And the system will perform the management of the different components in the environment (climatization systems, security systems, other smart systems).info:eu-repo/semantics/publishedVersio