Bootstrap Based Uncertainty Propagation for Data Quality Estimation in Crowdsensing Systems

The diffusion of mobile devices equipped with sensing, computation, and communication capabilities is opening unprecedented possibilities for high-resolution, spatio-temporal mapping of several phenomena. This novel data generation, collection, and processing paradigm, termed crowdsensing, lays upon complex, distributed cyberphysical systems. Collective data gathering from heterogeneous, spatially distributed devices inherently raises the question of how to manage different quality levels of contributed data. In order to extract meaningful information, it is, therefore, desirable to the introduction of effective methods for evaluating the quality of data. In this paper, we propose an approach aimed at systematic accuracy estimation of quantities provided by end-user devices of a crowd-based sensing system. This is obtained thanks to the combination of statistical bootstrap with uncertainty propagation techniques, leading to a consistent and technically sound methodology. Uncertainty propagation provides a formal framework for combining uncertainties, resulting from different quantities influencing a given measurement activity. Statistical bootstrap enables the characterization of the sampling distribution of a given statistics without any prior assumption on the type of statistical distributions behind the data generation process. The proposed approach is evaluated on synthetic benchmarks and on a real world case study. Cross-validation experiments show that confidence intervals computed by means of the presented technique show a maximum 1.5% variation with respect to interval widths computed by means of controlled standard Monte Carlo methods, under a wide range of operating conditions. In general, experimental results confirm the suitability and validity of the introduced methodology

Massive Remote School Trips: A Case Study

none5noDuring the height of the CoViD-19 pandemic in 2020 and early 2021, schools throughout Europe have been closed for several months, leaving teachers with the responsibility of providing distance learning through video conferencing and remote-presence systems, while parents were scrambling for appropriate tools and support. School outings have also suffered from the limited mobility of students given by stay-at-home orders and other restrictions. In this paper we present a set of technologies developed to reproduce the school trip experience, allowing students to stay at home or in school and requiring only a Web browser and Internet access, while integrating communication tools that allow participants to actively be engaged in interactive lessons and educational experiences. In 2020 the tool has been used during the “CodyTrip” event, a two-day visit to the town of Urbino, attended by more than 15.000 students, followed up with a series of events in 2021 with over 115.000 participants. Results from the pilot events show very high engagement and demonstrate the feasibility of organizing online visiting experiences with massive participation without compromising the perceived interactivity of the proposed activities, which can be equally engaging for different audience demographics. Findings also suggest that this solution may be adopted not only as a contingent substitute for traveling during the pandemic, but as an effective tool to widen the scope and appeal of cultural tourism.openBogliolo, Alessandro; Delpriori, Saverio; Di Francesco, Gian Marco; Klopfenstein, Cuno Lorenz; Paolini, Brendan DominicBogliolo, Alessandro; Delpriori, Saverio; Di Francesco, Gian Marco; Klopfenstein, Cuno Lorenz; Paolini, Brendan Domini

Mobile crowdsensing for road sustainability: exploitability of publicly-sourced data

ABSTRACTThis paper examines the opportunities and the economic benefits of exploiting publicly-sourced datasets of road surface quality. Crowdsourcing and crowdsensing initiatives channel the parti..

Balance trucks:Using crowd-sourced data to procedurally-generate gameplay within mobile games

Within the field of procedural content generation (PCG) research, the use of crowd-sensing data has, until now, primarily been used as a means of collecting information and generating feedback relating to player experience within games, and game aesthetics. However, crowd-sensing data can offer much more, supplying a seemingly untapped font of information which may be used within the creation of unique PCG game spaces or content, whilst providing a visible outlet for the dissemination of crowd-sensed material to users. This paper examines one such use of crowd-sensed data, the creation of a game which will reside within the CROWD4ROADS (C4RS) application, SmartRoadSense (SRS). The authors will open with a brief discussion of PCG. Following this, an explanation of the features and aims of the SRS application will be provided. Finally, the paper will introduce ‘Balance Trucks’, the SRS game, discussing the concepts behind using crowd-sensed data within its design, its development and use of PCG

Standing Balance Assessment by Measurement of Body Center of Gravity Using Smartphones

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Le reti di sensori come prodotti di massa: un nuovo paradigma di programmazione e gestione orientato all'usabilita

Lo produzione di applicazioni per le reti si sensori wireless è un compito riservato a programmatori esperti con competenze nell'ambito dello sviluppo su piattaforme embedded. Il sistema realizzato in questo lavoro permette, anche a chi non ha capacità specifiche, di sviluppare applicazioni per i nodi di una WSN e di eseguire in autonomia le operazioni di installazione e controllo dei nodi

Crowdsensing Scenarios for the Common Good

This whitepaper provides an overview of mobile crowdsensing, as an extension to “participatory sensing” that tasks average citizens and volunteers to perform local knowledge gathering and sharing, in particular thanks to the use of mobile smart devices. The article describes existing examples from literature and related works, detailing how crowdsensing has already been adopted successfully in several different fields, which share the focus on the common good. Examples include existing systems aimed at the development of smart cities, life quality improvements for urban citizens, critical event management, social recommender systems, or road quality monitoring platforms, such as SmartRoadSense

Incentives for Crowdsourcing

This whitepaper provides an overview perspective on the foundation of crowdsourcing and crowdsensing, detailing related notions such as collective intelligence and citizen science. As all crowd-based initiatives are usually built upon a core of volunteers participating to an “open call” to perform tasks, crowdsensing in particular relies on the willingness of participants to invest time and interest in a cause, sacrifice limited resources of their mobile devices, and provide implicit or explicit efforts, including ignoring risks that impact the privacy of their data. This document describes how incentives and rewarding schemes are adopted in this context to attract a community of users and to keep it engaged. Two incentive schemes in particular are described, both of which have been developed within the CROWD4ROADS project: a game mode that transforms collected data into generated terrain for a 2D platform game and a voucher exchange system that acts as a platform for any initiative for the common good. Both incentive schemes are fully anonymous and integrate within a fully anonymous data collection scheme