Tracking Context-Aware Well-Being through Intelligent Environments

The growth of personal sensors and the ability to sensorize attributes connected with the physical beings and environments are increasing. Initiatives such as Internet of Things (IoT)) aim to connect devices and people through communication channels in order to automate and fuel interaction. Targeted approaches can be found on the Smart Cities projects which use the IoT to gather data from people and attributes related to city management. Though good for management of new cities, well-being should as well be of principal importance. It regards users higher than infrastructure and managerial data. Taking lessons from ergonomic studies, health studies and user habits it is possible to track and monitor user daily living. Moreover, the link between user living conditions and sparse events such as illness, indispositions can be tracked to well-being data through autonomous services. Such application is detailed in the approach categorized by this article and the research presente

Traffic expression through ubiquitous and pervasive sensorization - smart cities and assessment of driving behaviour

The number of portable and wearable devices has been increasing in the population of most developed countries. Meanwhile, the capacity to monitor and register not only data about people’s habits and locations but also more complex data such as intensity and strength of movements has created an opportunity to their contribution to the general wealth and sustainability of environments. Ambient Intelligence and Intelligent Decision Making processes can benefit from the knowledge gathered by these devices to improve decisions on everyday tasks such as planning navigation routes by car, bicycle or other means of transportation and avoiding route perils. Current applications in this area demonstrate the usefulness of real time system that inform the user of conditions in the surrounding area. Nevertheless, the approach in this work aims to describe models and approaches to automatically identify current states of traffic inside cities and relate such information with knowledge obtained from historical data recovered by ubiquitous and pervasive devices. Such objective is delivered by analysing real time contributions from those devices and identifying hazardous situations and problematic sites under defined criteria that has significant influence towards user well-being, economic and environmental aspects, as defined is the sustainability definition

Assessing interpersonal trust in an ambient intelligence negotiation system

This paper describes an approach to assess and measure trust based on a specific Ambient Intelligence environment. The primary aim of this work is to address and expand on this line of research by investigating the possibility of measuring trust based on quantifiable behavior. To do so, we present a brief review of the existing definitions of trust and define trust in the context of an Ambient Intelligence (AmI) scenario. Further, we propose a formal definition so that the analysis of trust in this kind of scenarios can be developed. Thus, it is suggested the use of Ambient Intelligence techniques that use a trust data model to collect and evaluate relevant information based on the assumption that observable trust between two entities (parties) results in certain typical behaviors. This will establish the foundation for the prediction of such aspects based on the analysis of people’s interaction with technological environments, providing new potentially interesting trust assessment tools.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within the Project Scope UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Explainable artificial intelligence on smart human mobility: a comparative study approach

Explainable artificial intelligence has been used in several scientific fields to understand how and why a machine learning model makes its predictions. Its characteristics have allowed for greater transparency and outcomes in AI-powered decision-making. This building trust and confidence can be useful in human mobility research. This work provides a comparative study in terms of the explainability of artificial intelligence on smart human mobility in the context of a regression problem. Decision Tree, LIME, SHAP, and Seldon Alibi are explainable approaches to describe human mobility using a dataset generated from New York Services. Based on our results, all of these approaches present relevant indicators for our problem.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It has also been supported by national funds through FCT – Fundação para a Ciência e Tecnologia through project UIDB/04728/2020

Urban human mobility modelling and prediction: impact of comfort and well-being indicators

There are increasingly more discussions on and guidelines about different levels of indicators surrounding smart cities (e.g., comfort, well-being and weather conditions). They are an important opportunity to illustrate how smart urban development strategies and digital tools can be stretched or reinvented to address localised social issues. Thus, multi-source heterogeneous data provides a new driving force for exploring urban human mobility patterns. In this work, we forecast human mobility data using LinkNYC kiosks and Metropolitan Transportation Authority (MTA) Wi-Fi in New York City to study how comfort and well-being indicators influence people's movements. By comparing the forecasting performance of statistical and deep learning methods on the aggregated mobile data we show that each class of methods has its advantages and disadvantages depending on the forecasting scenario. However, for our time-series forecasting problem, deep learning methods are preferable when it comes to simplicity and immediacy of use, since they do not require a time-consuming model selection for each different cell. Deep learning approaches are also appropriate when aiming to reduce the maximum forecasting error. Statistical methods instead have shown their superiority in providing more precise forecasting results, but they require data domain knowledge and computationally expensive techniques in order to select the best parameters.This work has been supported by FCT -Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It has also been supported by national funds through FCT -Fundacao para a Ciencia e Tecnologia through project UIDB/04728/2020

Analyzing metrics to understand human mobility phenomena: challenges and solutions

Defining basic metrics that analyze human motion is important for urban planning and population mobility forecasting. These metrics are applied to understand extensive human mobility data generated from multiple sources. This means that our understanding of the basic metrics governing human motion is conditioned by integrating different data sources available. To the best of our knowledge, this article is a comprehensive study of the characteristics and metrics of human mobility patterns. Initially, it focuses on understanding common metrics in human mobility research. Then, it compares metrics such as resilience, displacement, interval and duration in different data types such as Wi-Fi, Call Detail Records (CDRs), Global Positioning System (GPS) and Social Media collected from two individuals. Comparing the results, a variation in movement patterns in both individuals is found in our study. Finally, we uncover a few interesting phenomena that lay a solid foundation for future research.This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It has also been supported by national funds through FCT – Fundação para a Ciência e Tecnologia through project UIDB/04728/2020

Mobile networks and internet of things: contributions to smart human mobility

Nowadays, our society can be considered as a “connected society” thanks to a heterogeneous network and the growth of mobile technologies. This growth has meant new devices are now supporting Internet of Things (IoT) architecture. Consequently, a new look at the current design of wireless communication systems is needed. Smart mobility concerns the massive movement of people and requires a complex infra-structure that produces a lot of data, creating new interesting challenges in terms of network management and data processing. In this paper, we address classic generations of mobile technology until the latest 5G implementation and its alternatives. This analysis is contextualized for the problem of smart mobility services and people-centric services for the internet of things that have a wide range of application scenarios within smart cities.This work has been supported by FCT – Funda¸c˜ao para a Ciˆencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It has also been sup ported by national funds through FCT – Funda¸c˜ao para a Ciˆencia e Tecnologia through project UIDB/04728/2020

WalkingStreet: understanding human mobility phenomena through a mobile application

Understanding human mobility patterns requires access to timely and reliable data for an adequate policy response. This data can come from several sources, such as mobile devices. Additionally, the wide availability of communications networks enables applications (mobile apps) to generate data anytime and anywhere thanks to their general adoption by individuals. Although data is generated from personal devices, if a relevant set of metrics is applied to it, it can become useful for the authorities and the community as a whole. This paper explores new methods for gathering and analyzing location-based data using a mobile application called WalkingStreet. The article also illustrates the great potential of human mobility metrics for moving spatial measures beyond census units, key measures of individual, collective mobility and a mix of the two, investigating a range of important social phenomena, the heterogeneity of activity spaces and the dynamic nature of spatial segregation.This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It has also been supported by national funds through FCT – Funda¸c˜ao para a Ciˆencia e Tecnologia through project UIDB/04728/2020

Distress detection in road pavements using neural networks

Combining Computer Vision (CV) and Anomaly Detection (AD), there is a convergence of methodologies using convolutional layers in AD architectures, which we consider an innovation in the field. The main goal of this work is to present different Artificial Neural Networks (ANN) architectures, applying them to distress detection in road pavements and comparing the results obtained in each approach. The experimented methods for AD in images include a binary classifier as a baseline, an Autoencoder (AE) and a Variational Autoencoder (VAE). Supervised and unsupervised practises are also compared, proving their utility in scenarios where there is no labelled data available. Using the VAE model in a supervised setting, it presents an excellent distinction between good and bad pavement. When labelled data is not available, using the AE model and the distribution of similarities of good pavement reconstructions to calculate the threshold is the best option with accuracy and precision above 94%. The development of these models shows that it is possible to develop an alternative solution to reduce operating costs compared to expensive commercial systems and to improve the usability compared to conventional methods of classifying road surfaces.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

Forecasting energy consumption of wastewater treatment plants with a transfer learning approach for sustainable cities

A major challenge of today’s society is to make large urban centres more sustainable. Improving the energy efficiency of the various infrastructures that make up cities is one aspect being considered when improving their sustainability, with Wastewater Treatment Plants (WWTPs) being one of them. Consequently, this study aims to conceive, tune, and evaluate a set of candidate deep learning models with the goal being to forecast the energy consumption of a WWTP, following a recursive multi-step approach. Three distinct types of models were experimented, in particular, Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), and uni-dimensional Convolutional Neural Networks (CNNs). Uni- and multi-variate settings were evaluated, as well as different methods for handling outliers. Promising forecasting results were obtained by CNN-based models, being this difference statistically significant when compared to LSTMs and GRUs, with the best model presenting an approximate overall error of 630 kWh when on a multi-variate setting. Finally, to overcome the problem of data scarcity in WWTPs, transfer learning processes were implemented, with promising results being achieved when using a pre-trained uni-variate CNN model, with the overall error reducing to 325 kWh.The work of Paulo Novais and Cesar Analide has been supported by FCT-Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. The work of Pedro Oliveria and Bruno Fernandes is also supported by National Funds through the Portuguese funding agency, FCT-Fundação para a Ciência e a Tecnologia within project DSAIPA/AI/0099/2019