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The ever-progressing advancements in urban growth and technological development in recent decades have caused a noticeable increase of the phenomenon of socialenvironmental deterioration, leading to a decline in quality of life, reduction of social welfare and difficult urban mobility for people living in cities. The concept of Smart City can be used to mitigate several of the challenges arising from the aforementioned issues, relying on multiple tools and techniques (such as crowdsensing) to gather essential context data about how actual citizens consume resources and commute throughout their everyday lives. In this paper, we show how an urban mobility data analytics tool may help to determine the most visited regions and interconnections in an urban area. This information has been obtained using data gathered from a pool of users participating in a crowdsensing campaign, using the ParticipAct Brazil platform. The obtained results confirm the reliability of the information produced, highlighting the regions with the highest concentration of people during the geolocation monitoring process and their connections; therefore, this data may be used to plan possible future changes to how the city allocates its resources, to better suit the mobility needs of its citizens.

Today's spread of chronic diseases and the need to control infectious diseases outbreaks have raised the demand for integrated information systems that can support patients while moving anywhere and anytime. This has been promoted by recent evolution in telecommunication technologies, together with an exponential increase in using sensor-enabled mobile devices on a daily basis. The construction of Mobile Health Communities (MHC) supported by Mobile CrowdSensing (MCS) is essential for mobile healthcare emergency scenarios. In a previous work, we have introduced the COLLEGA middleware, which integrates modules for supporting mobile health scenarios and the formation of MHCs through MCS. In this paper, we extend the COLLEGA middleware to address the need in real time scenarios to handle data arriving continuously in streams from MHC's members. In particular, this paper describes the novel COLLEGA support for managing the real-time formation of MHCs. Experimental results are also provided that show the effectiveness of our identification solution.

The exploitation of mobile social networking technologies merging crowdsensing systems enable mobile users to opportunistically create participatory mobile social networks based on not only common attributes, interests or contacts, but also mobility-related context, such as physical location and co-presence. Disaster management and mobile healthcare applications can benefit from the possibility of creating participatory communities based on physical closeness. Co-located people can dynamically form ad-hoc mobile community response networks (CRNs) to provide anywhere and anytime care assistance to users with critical physical/behavioral conditions after a disaster occurrence or even during their normal day-life while on the move. The effectiveness of mobile CRNs depends, however, on the possibility to select among co-located users the ones with the most appropriate competence to understand and execute required assistance actions. The paper introduces the concept of competence-based mobile CRNs and describes how competence-based mobile CRNs can be created within the specific framework of a crowdsensing-based middleware called COLLEGA that provides comprehensive management functionalities for supporting prompt assistance in emergency situations. In particular, the paper discusses our proposed competence model and its implementation within COLLEGA enabling to extract the competence of mobile CRN's members from data available on social networks, such as LinkedIn.

Neste artigo sao apresentados um modelo e uma arquitetura para construcao e estruturacao de ambientes que necessitam realizar, eficientemente, armazenamento, busca e analise de grandes volumes de dados. Para a implementacao do modelo foi utilizado o projeto ParticipACT Brasil. Este projeto visa implantar na cidade de Florianopolis o processo de ciencia cidada para obter informacoes relevantes que auxiliem os gestores a resolverem problemas urbanos. Pode-se observar, entao que a implementacao do modelo facilitou a estruturacao do ambiente, e ajudou na escolha de ferramentas especializadas na manipulacao de big data. In this paper we presented a model and an architecture for building and structuring environments that need to store, search and analyze, efficiently, of large amount of data. For the implementation of the model we used ParticipACT Brazil project. This project aims to deploy in Florianopolis city the citizen science process to obtain relevant information to help managers to solve urban problems. We can be observed that the implementation of the structure has facilitated the environment and helped in choice of specialized tools in manipulating big data.

Recent advancements in wireless technologies and the widespread availability of smartphones equipped with several physical and virtual sensors are leading to the emergence of novel mobile healthcare scenarios where patients with critical physical/behavioral conditions can be provided with anywhere and anytime care assistance even while on the move. Crowdsensing, through the massive use of smartphone sensors further enhances the potential of supporting participatory management of emergency scenarios. This paper presents a macro process modelling of crowdsourced-based participatory emergency scenarios and, accordingly, proposes a crowdsensing-based middleware called COLLEGA that provides comprehensive management functionalities for supporting prompt assistance in case of a medical emergency. In particular, through participatory and opportunistic sensing, the COLLEGA framework allows dynamic formation of ad-hoc assistance groups formed by passing-by users capable of assisting mobile patients in need of help while waiting for professional caregivers and provides support for understanding the emergency situation and effectively planning and executing assistance actions.

New sensors embedded into modern smartphones has led into a new data collection prospective in which people directly collect all the sensitive data. This feature has found different applications, in particular in the Smart Cities area, in order to establish dynamic communications between the citizens and the city government. This category of application is nestled into the Mobile Crowd Sensing (MCS) application group, due to their final purpose of sharing sensing data to an open platform that includes a huge number of people. This paper presents an extension of the general-purpose ParticipAct platform, a MCS application developed by the University of Bologna, focused on the needs of people with impaired mobility. The goal is specializing ParticipAct to enable a crowdsourcing platform that guarantees a solid support for their lifetime allowing reviewing and sharing opinions regarding public and private places and architectonic barriers of a city area. Showed results confirm the effectiveness of the developed application in terms of both its viability via integration with existing and widely diffused Geographical Information Systems (GIS), and its feasibility in terms of system and user-perceived performances.

Novel sensor-equipped smartphones have enabled the possibility of harvesting large quantities of data in urban areas by opportunistically involving citizens and their portable devices, as mobile sensors widely available and distributed over Smart Cities areas, typically defined as Mobile Crowd Sensing (MCS). Although some existing efforts have already tackled some of the several MCS issues, to the best of our knowledge, active experiments addressing the challenging issue of the assignment of MCS data collection campaigns to users, namely, MCS scheduling, in a large-scale crowdsensing real-world experiment are still missing. This paper presents the ParticipAct platform and living lab, an ongoing crowdsensing experiment at University of Bologna that involves 300 students for one year. In particular, this article focus on ParticipAct intelligent MCS scheduling of future crowdsensing campaigns based on user mobility history and powered by NoSQL technologies for fast processing of the large amount of mobility traces in the ParticipAct backend. Showed results confirm the feasibility of the proposed approach and quantify its cost.

Recent advances in sensor-equipped smartphones are opening brand new opportunities, such as automatically extracting Points Of Interest (POIs) and mobility habits of citizens in Smart Cities from the large amount of harvested data hotspots. At the same time, the high dynamicity and unpredictability of Smart Cities crowds, opportunistically collaborating toward these common crowdsensing tasks, introduces challenging issues due to the need for fast and continuous processing of these Big Data Streams in the backend of next generation crowdsensing platforms. This paper presents our practical experiences and lessons learnt in deploying the ParticipAct platform and living lab, an ongoing experiment at University of Bologna that involves 300 students for one year. Among all management issues addressed in ParticipAct, this article shows the integration of MongoDB in the ParticipAct backend, as a powerful NoSQL storage and processing engine to fasten the identification of POIs; the reported performance results confirm the feasibility of the approach by quantifying its advantages for city managers.