Semantic Data Layers in Air Quality Monitoring for Smarter Cities

Air pollution is one of the key indicators for quality of life in urban environments, and is also the subject of global health concern, given the number of mortal diseases associated to exposure to pollutants. Assessing and monitoring air quality is an important step in order to better understand the impact of pollution on the health of the population. Nevertheless, in order to scale to the city level, traditional high-quality stationary sensing stations are not enough. Limitations include lack of coverage, the cost of deployment and maintenance, as well as the resolution of the observed phenomena. The OpenSense2 project aims at providing a city-level sensing deployment that combines different levels of air quality sensing: reference stations, mobile sensing on public transportation, and participatory crowdsensing. In this paper we highlight some of the key challenges of managing the data captured by such infrastructure, taking the city of Lausanne as a driving use-case. Furthermore, we present a semantics-based approach for characterizing and exposing the air quality data, so that it can be made available to citizens and application developers in a way that it can be usable and understood effectively

Toward Self-monitoring Smart Cities: the OpenSense2 Approach

The sustained growth of urban settlements in the last years has had an inherent impact on the environment and the quality of life of their inhabitants. In order to support sustainability and improve quality of life in this context, we advocate the fostering of ICT-empowered initiatives that allow citizens to self-monitor their environment and assess the quality of the resources in their surroundings. More concretely, we present the case of such a self-monitoring Smart City platform for estimating the air quality in urban environments at high resolution and large scale. Our approach is a combination of mobile and human sensing that exploits both dedicated and participatory monitoring. We identify the main challenges in such a crowdsensing scenario for Smart Cities, and in particular we analyze issues related to scalability, accuracy, accessibility, privacy, and discoverability, among others. Moreover, we show that our approach has the potential to empower citizens to diagnose their environment using mobile and portable sensing devices, combining their personal data with a public higher accuracy air quality network

Realizacija servisa pametnog zdravstva i njihova integracija u koncept pametnih gradova

The development of information technologies has contributed to the emergence of the concept of a smart city, and one of the components of this concept is smart health. This component is important because smart cities directly or indirectly affect the health of residents. The main contributions of this doctoral dissertation are a proposal of a software platform that integrates existing public health services as well as new e-health services, a proposal for a uniform way of integrating heterogeneous smart health services into a smart city, and a proposal and implementation of methods for labelling terms in medical texts, based on artificial intelligence, without which part of the smart health service could not be realized in an efficient way. The above-mentioned software platform combines the smart health services that will be proposed (services for information and prevention of diseases, epidemic control, search of medical documents, automatic questionnaire processing, air pollution monitoring, labelling of medical texts, organization of screening programs, etc.). These services are created based on input data of different types (sensor data on locations, air pollution, text from documents and medical information systems and data collected by crowdsourcing, data from relational and non-relational databases), so it is necessary to integrate these heterogeneous services in uniform way. Part of the proposed e-health services is based on data processing in medical information systems as well as medical text documents in the Serbian language. Methods for normalization, labelling and classification of terms in the Serbian language are a prerequisite for the successful implementation of these services, and within this dissertation methods are proposed and implemented whose F1-score is 0.9082, which is an excellent result compared to methods for this purpose in other languages. For their implementation, it is necessary to use artificial intelligence methods, such as natural language processing, data and text mining, machine learning, etc. Some of the proposed e-health services are practically implemented and integrated into the smart city concept

Robust and Efficient Data Clustering with Signal Processing on Graphs

Data is pervasive in today's world and has actually been for quite some time. With the increasing volume of data to process, there is a need for faster and at least as accurate techniques than what we already have. In particular, the last decade recorded the effervescence of social networks and ubiquitous sensing (through smartphones and the Internet of Things). These phenomena, including also the progresses in bioinformatics and traffic monitoring, pushed forward the research on graph analysis and called for more efficient techniques. Clustering is an important field of machine learning because it belongs to the unsupervised techniques (i.e., one does not need to possess a ground truth about the data to start learning). With it, one can extract meaningful patterns from large data sources without requiring an expert to annotate a portion of the data, which can be very costly. However, the techniques of clustering designed so far all tend to be computationally demanding and have trouble scaling with the size of today's problems. The emergence of Graph Signal Processing, attempting to apply traditional signal processing techniques on graphs instead of time, provided additional tools for efficient graph analysis. By considering the clustering assignment as a signal lying on the nodes of the graph, one may now apply the tools of GSP to the improvement of graph clustering and more generally data clustering at large. In this thesis, we present several techniques using some of the latest developments of GSP in order to improve the scalability of clustering, while aiming for an accuracy resembling that of Spectral Clustering, a famous graph clustering technique that possess a solid mathematical intuition. On the one hand, we explore the benefits of random signal filtering on a practical and theoretical aspect for the determination of the eigenvectors of the graph Laplacian. In practice, this attempt requires the design of polynomial approximations of the step function for which we provided an accelerated heuristic. We used this series of work in order to reduce the complexity of dynamic graphs clustering, the problem of defining a partition to a graph which is evolving in time at each snapshot. We also used them to propose a fast method for the determination of the subspace generated by the first eigenvectors of any symmetrical matrix. This element is useful for clustering as it serves in Spectral Clustering but it goes beyond that since it also serves in graph visualization (with Laplacian Eigenmaps) and data mining (with Principal Components Projection). On the other hand, we were inspired by the latest works on graph filter localization in order to propose an extremely fast clustering technique. We tried to perform clustering by only using graph filtering and combining the results in order to obtain a partition of the nodes. These different contributions are completed by experiments using both synthetic datasets and real-world problems. Since we think that research should be shared in order to progress, all the experiments made in this thesis are publicly available on my personal Github account