261 research outputs found
Mapping Participatory Sensing and Community-led Environmental Monitoring Initiatives: Making Sense H2020 CAPS Project
This report presents a summary of the state of the art in urban participatory sensing and community-led environmental monitoring, the types of engagement approaches typically followed, contextual examples of current developments in this field, and current challenges and opportunities for successful interventions. The goal is to better understand the field and possible options for reflection and action around it, in order to better inform future conceptual and practical developments inside and outside the Making Sense project.JRC.I.2-Foresight, Behavioural Insights and Design for Polic
Fog Architectures and Sensor Location Certification in Distributed Event-Based Systems
Since smart cities aim at becoming self-monitoring and self-response systems,
their deployment relies on close resource monitoring through large-scale urban
sensing. The subsequent gathering of massive amounts of data makes essential
the development of event-filtering mechanisms that enable the selection of what
is relevant and trustworthy. Due to the rise of mobile event producers,
location information has become a valuable filtering criterion, as it not only
offers extra information on the described event, but also enhances trust in the
producer. Implementing mechanisms that validate the quality of location
information becomes then imperative. The lack of such strategies in cloud
architectures compels the adoption of new communication schemes for Internet of
Things (IoT)-based urban services. To serve the demand for location
verification in urban event-based systems (DEBS), we have designed three
different fog architectures that combine proximity and cloud communication. We
have used network simulations with realistic urban traces to prove that the
three of them can correctly identify between 73% and 100% of false location
claims
Bayesian modelling of community-based multidimensional trust in participatory sensing under data sparsity
We propose a new Bayesian model for reliable aggregation of crowdsourced estimates of real-valued quantities in participatory sensing applications. Existing approaches focus on probabilistic modelling of user’s reliability as the key to accurate aggregation. However, these are either limited to estimating discrete quantities, or require a significant number of reports from each user to accurately model their reliability. To mitigate these issues, we adopt a community-based approach, which reduces the data required to reliably aggregate real-valued estimates, by leveraging correlations between the re- porting behaviour of users belonging to different communities. As a result, our method is up to 16.6% more accurate than existing state-of-the-art methods and is up to 49% more effective under data sparsity when used to estimate Wi-Fi hotspot locations in a real-world crowdsourcing application
Smartphone sensing meets transport data: A collaborative framework for transportation service analytics
National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ
Spatial modelling of air pollution for open smart cities
A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsHalf of the world’s population already lives in cities, and by 2050 two-thirds of the
world’s population are expected to further move into urban areas. This urban growth
leads to various environmental, social and economic challenges in cities, hampering
the Quality of Life (QoL). Although recent trends in technologies equip us with
various tools and techniques that can help in improving quality of life, air pollution
remains the ‘biggest environmental health risk’ for decades, impacting individuals’
quality of life and well-being according to World Health Organisation (WHO). Many
efforts have been made to measure air quality, but the sparse arrangement of
monitoring stations and the lack of data currently make it challenging to develop
systems that can capture within-city air pollution variations. To solve this, flexible
methods that allow air quality monitoring using easily accessible data sources at the
city level are desirable. The present thesis seeks to widen the current knowledge
concerning detailed air quality monitoring by developing approaches that can help
in tackling existing gaps in the literature. The thesis presents five contributions
which address the issues mentioned above. The first contribution is the choice of
a statistical method which can help in utilising existing open data and overcoming
challenges imposed by the bigness of data for detailed air pollution monitoring.
The second contribution concerns the development of optimisation method which
helps in identifying optimal locations for robust air pollution modelling in cities.
The third contribution of the thesis is also an optimisation method which helps
in initiating systematic volunteered geographic information (VGI) campaigns for
detailed air pollution monitoring by addressing sparsity and scarcity challenges
of air pollution data in cities. The fourth contribution is a study proposing the
involvement of housing companies as a stakeholder in the participatory framework
for air pollution data collection, which helps in overcoming certain gaps existing in
VGI-based approaches. Finally, the fifth contribution is an open-hardware system that
aids in collecting vehicular traffic data using WiFi signal strength. The developed
hardware can help in overcoming traffic data scarcity in cities, which limits detailed
air pollution monitoring. All the contributions are illustrated through case studies
in Muenster and Stuttgart. Overall, the thesis demonstrates the applicability of the developed approaches for enabling air pollution monitoring at the city-scale under
the broader framework of the open smart city and for urban health research
Citizen participation: crowd-sensed sustainable indoor location services
In the present era of sustainable innovation, the circular economy paradigm
dictates the optimal use and exploitation of existing finite resources. At the
same time, the transition to smart infrastructures requires considerable
investment in capital, resources and people. In this work, we present a general
machine learning approach for offering indoor location awareness without the
need to invest in additional and specialised hardware. We explore use cases
where visitors equipped with their smart phone would interact with the
available WiFi infrastructure to estimate their location, since the indoor
requirement poses a limitation to standard GPS solutions. Results have shown
that the proposed approach achieves a less than 2m accuracy and the model is
resilient even in the case where a substantial number of BSSIDs are dropped.Comment: Preprint submitted to Elsevie
Smartphone GPS tracking—Inexpensive and efficient data collection on recreational movement
This research note describes the methodological and practical applications of using smartphone GPS tracking (SGT) to explore the spatial distribution and density of recreational movement in multiple-use urban forests. We present findings from the pilot phase of an on-going case study in Keskuspuisto (Central park), Helsinki, Finland. The study employs an inventive and inexpensive approach for participatory data collection i.e. gathering GPS data from recreational users who have already recorded their routes for purposes other than research, using any kind of sports tracking application on their personal mobile phones. We used the SGT data to examine visitor spatial patterns on formal trails and informal paths, and present examples with runners and mountain bikers. Hotspot mapping of mountain bikers’ off-trail movement was conducted identifying several locations with clustering of off-trail use. Small-scale field mapping of three hotspot areas confirmed that the method accurately located areas of high use intensity where visible effects of path widening and high level of wear on the forest floor vegetation could be observed. We conclude that the SGT methodology offers great opportunities for gathering useful and up-to-date spatial information for adaptive planning and management as it highlights areas where conservation and visitor management measures may need to be adjusted. We suggest that this method warrants testing also for other user-centred research and planning purposes.Peer reviewe
Trust-based algorithms for fusing crowdsourced estimates of continuous quantities
Crowdsourcing has provided a viable way of gathering information at unprecedented volumes and speed by engaging individuals to perform simple micro–tasks. In particular, the crowdsourcing paradigm has been successfully applied to participatory sensing, in which the users perform sensing tasks and provide data using their mobile devices. In this way, people can help solve complex environmental sensing tasks, such as weather monitoring, nuclear radiation monitoring and cell tower mapping, in a highly decentralised and parallelised fashion. Traditionally, crowdsourcing technologies were primarily used for gathering data for classifications and image labelling tasks. In contrast, such crowd–based participatory sensing poses new challenges that relate to (i) dealing with human–reported sensor data that are available in the form of continuous estimates of an observed quantity such as a location, a temperature or a sound reading, (ii) dealing with possible spatial and temporal correlations within the data and (ii) issues of data trustworthiness due to the unknown capabilities and incentives of the participants and their devices. Solutions to these challenges need to be able to combine the data provided by multiple users to ensure the accuracy and the validity of the aggregated results. With this in mind, our goal is to provide methods to better aid the aggregation process of crowd–reported sensor estimates of continuous quantities when data are provided by individuals of varying trustworthiness. To achieve this, we develop a trust–based in- formation fusion framework that incorporates latent trustworthiness traits of the users within the data fusion process. Through this framework, we develop a set of four novel algorithms (MaxTrust, BACE, TrustGP and TrustLGCP) to compute reliable aggregations of the users’ reports in both the settings of observing a stationary quantity (Max- Trust and BACE) and a spatially distributed phenomenon (TrustGP and TrustLGCP). The key feature of all these algorithm is the ability of (i) learning the trustworthiness of each individual who provide the data and (ii) exploit this latent user’s trustworthiness information to compute a more accurate fused estimate. In particular, this is achieved by using a probabilistic framework that allows our methods to simultaneously learn the fused estimate and the users’ trustworthiness from the crowd reports. We validate our algorithms in four key application areas (cell tower mapping, WiFi networks mapping, nuclear radiation monitoring and disaster response) that demonstrate the practical impact of our framework to achieve substantially more accurate and informative predictions compared to the existing fusion methods. We expect that results of this thesis will allow to build more reliable data fusion algorithms for the broad class of human–centred information systems (e.g., recommendation systems, peer reviewing systems, student grading tools) that are based on making decisions upon subjective opinions provided by their users
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