Carlos Roberto De Rolt

COLLEGA middleware for the management of participatory Mobile Health Communities

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.

LTE proximity discovery for supporting participatory mobile health communities

Advancements in mobile communication technologies and the continuously increasingly diffusion of smartphones equipped with several physical and virtual sensors and with different network support are promoting 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. In particular, this recent technology evolution simplifies the formation of mobile health communities (MHC) for prompt assistance in the case of emergency situations, where a MHC can be defined as a dynamic team of care givers formed by passing by mobile users physically co-located with the patient in need of help while on the move. Crowdsensing, through the massive use of smartphone sensors, further enhances the potential of supporting participatory management of MHCs for emergency scenarios. This paper presents a crowdsensing-based middleware called COLLEGA that provides several management functionalities for supporting prompt assistance to mobile patients in the case of a medical emergency. In particular, the paper claims to exploit the novel emerging LTE Direct technology to facilitate dynamic formation of MHCs and data dissemination. Our LTE-based support for participatory MHCs is described and experimental results showing the feasibility and effectiveness of the approach are also provided.

Privacy information in a positive credit system

A positive credit history and rating help consumers with good payment history to get lower interest rates, greater flexibility for credit, as well as loans with longer payment terms. In order to establish credit profiles, there is a need to get information (e.g. spending patterns) from the consumers and from the companies that are selling related goods. The problem is how to share private information about customers and companies without compromising the secrecy and confidentiality of such information. In this work, we propose a cryptographic protocol to share sensitive information while preserving the privacy of the customer as well as the information of the commercial institution. Furthermore, we analyse the proposed protocol by using Petri nets to verify the absence of livelocks, deadlocks, and other anomalies in the protocol.

Towards an Infrastructure to Support Big Data for a Smart City Project

The spread of projects focused on smart cities have grown in recent years. With this, the massive amount of data generated in these initiatives, creates a degree of complexity in how to manage all this information. In this paper we propose an infrastructure model for big data for a smart city projet. The goal of this model is to present the stages for the processing of data in the step of extraction, storage, processing and visualization, as well as the types of tools needed for each phase. To implement our proposed model, we used the Particip ACT Brazil a project based in smart cities. This project uses different databases to compose its big data and uses this data to seek solutions to urban problems. We observe that our model provides a structured vision of the software to be used in big data server of ParticipACT Brazil. In addition, we can also note that our model can be used in other big data servers.

Crowdsensing and proximity services for impaired mobility

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.

Smartphones as smart cities sensors: MCS scheduling in the ParticipAct project

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.

Automatic extraction of POIs in smart cities: Big data processing in ParticipAct

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.

Competence-based mobile Community Response Networks

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 CRNs members from data available on social networks, such as LinkedIn.

Dynamic Identification of Participatory Mobile Health Communities

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.

Sharing Privacy Information in Credit Analysis Environment

Credit analysis is used by financial companies to grant credit to customers. The analysis is expensive, bureaucratic, and requires the collection of customer personal information. However, shopping malls are selling goods in several installments and need a simple, fast, and cheap process to grant credit to their customers. One way to improve the efficiency of this process is to facilitate the sharing of private information from customers and stores. The challenge here is how to share private information without disclosing the identity of its owner. Here we propose a protocol to share information while preserving the privacy of the customer as well as the information of the commercial institution.