scholarly journals Probabilistic registration for large-scale mobile participatory sensing

2013 ◽  
Author(s):  
S. Hachem ◽  
A. Pathak ◽  
V. Issarny
Keyword(s):  
Large Scale ◽  
Participatory Sensing

A study of the accuracy of mobile technology for measuring urban noise pollution in large scale participatory sensing campaigns

2017 ◽  
Vol 117 ◽  
pp. 219-226 ◽  
Author(s):  
Pierre Aumond ◽  
Catherine Lavandier ◽  
Carlos Ribeiro ◽  
Elisa Gonzalez Boix ◽  
Kennedy Kambona ◽  
...  
Keyword(s):  
Mobile Technology ◽  
Large Scale ◽  
Noise Pollution ◽  
Participatory Sensing ◽  
Urban Noise

scholarly journals Participatory Sensing: Building Empowering Surveillance

2010 ◽  
Vol 8 (2) ◽  
pp. 131-150 ◽  
Author(s):  
Katie Shilton
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
New Technologies ◽  
Participatory Sensing ◽  
Ethical Challenges ◽  
Surveillance Technology ◽  
The World ◽  
Wide Scale ◽  
The Many ◽  
Definition Of

Mobile phones could become the largest surveillance system on the planet. These ubiquitous, networked devices can currently sense and upload data such as images, sound, location, and motion using on-board cameras, microphones, GPS, and accelerometers. And they can be triggered and controlled by billions of individuals around the world. But the emergent, wide-scale sensing systems that phones support pose a number of questions. Who will control the necessary infrastructure for data storage, analysis, sharing, and retention? And to what purposes will such systems be deployed? This paper explores whether these questions can be answered in ways that promote empowering surveillance: large-scale data collection used by individuals and communities to improve their quality of life and increase their power relative to corporations and governments. Researchers in academic and industry laboratories around the world are currently coordinating mobile phone networks for purposes that expand the definition of surveillance. Technology movements, variously called personal sensing, urban sensing or participatory sensing, have emerged within the areas of social computing and urban computing. These research programs endeavor to make ubiquitous devices such as phones a platform for coordinated investigation of human activity. Researchers are exploring ways to introduce these technologies into the public realm, a move that anticipates sensing by people across the world. This paper uses ethnographic data collected in a sensing development laboratory to illuminate possibilities that participatory sensing holds for equitable use, meaningful community participation, and empowerment. Analyzing the motivations and values embedded within the design process and resulting technologies reveals ways in which participatory sensing builds tools for empowering surveillance and responds to the many ethical challenges these new technologies raise.

scholarly journals Service-oriented middleware for large-scale mobile participatory sensing

2014 ◽  
Vol 10 ◽  
pp. 66-82 ◽  
Author(s):  
Sara Hachem ◽  
Animesh Pathak ◽  
Valerie Issarny
Keyword(s):  
Large Scale ◽  
Participatory Sensing ◽  
Service Oriented

scholarly journals AN ANALYSIS OF SECONDARY TEACHERS' REASONING WITH PARTICIPATORY SENSING DATA

2017 ◽  
Vol 16 (2) ◽  
pp. 305-334
Author(s):  
ROBERT GOULD ◽  
ANNA BARGAGLIOTTI ◽  
TERRI JOHNSON
Keyword(s):  
Large Scale ◽  
Statistics Education ◽  
Secondary Mathematics ◽  
Statistical Investigation ◽  
Participatory Sensing ◽  
Strategic Decisions ◽  
Sensing Data ◽  
Research Journal ◽  
Share Data ◽  
Numeric Data

Participatory sensing is a data collection method in which communities of people collect and share data to investigate large-scale processes. These data have many features often associated with the big data paradigm: they are rich and multivariate, include non-numeric data, and are collected as determined by an algorithm rather than by traditional experimental designs. While not often found in classrooms, arguably they should be since data with these features are commonly encountered in daily life. Because of this, it is of interest to examine how teachers reason with and about such data. We propose methods for describing progress through a statistical investigation. These methods are demonstrated on two groups of secondary mathematics teachers engaged in a model-eliciting activity centered around participatory sensing data. We employ graphical depictions of discrete Markov chains to describe the strategic decisions the teachers follow while analyzing data, and find that this descriptive technique reveals some suggestive patterns, particularly emphasizing the importance of frequent questioning and crafting productive statistical questions. First published November 2017 at Statistics Education Research Journal Archives

scholarly journals Coordinating Measurements in Uncertain Participatory Sensing Settings

2018 ◽  
Vol 61 ◽  
pp. 433-474 ◽  
Author(s):  
Alexandros Zenonos ◽  
Sebastian Stein ◽  
Nicholas R. Jennings
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Low Cost ◽  
Cost Effective ◽  
Environmental Data ◽  
Participatory Sensing ◽  
Sufficient Information ◽  
Mobility Patterns ◽  
Computationally Efficient ◽  
The Impact

Environmental monitoring allows authorities to understand the impact of potentially harmful phenomena, such as air pollution, excessive noise, and radiation. Recently, there has been considerable interest in participatory sensing as a paradigm for such large-scale data collection because it is cost-effective and able to capture more fine-grained data than traditional approaches that use stationary sensors scattered in cities. In this approach, ordinary citizens (non-expert contributors) collect environmental data using low-cost mobile devices. However, these participants are generally self-interested actors that have their own goals and make local decisions about when and where to take measurements. This can lead to highly inefficient outcomes, where observations are either taken redundantly or do not provide sufficient information about key areas of interest. To address these challenges, it is necessary to guide and to coordinate participants, so they take measurements when it is most informative. To this end, we develop a computationally-efficient coordination algorithm (adaptive Best-Match) that suggests to users when and where to take measurements. Our algorithm exploits probabilistic knowledge of human mobility patterns, but explicitly considers the uncertainty of these patterns and the potential unwillingness of people to take measurements when requested to do so. In particular, our algorithm uses a local search technique, clustering and random simulations to map participants to measurements that need to be taken in space and time. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the current state of the art by up to 24% in terms of utility gained.

scholarly journals Analyzing Spatial and Temporal User Behavior in Participatory Sensing

2018 ◽  
Vol 7 (9) ◽  
pp. 344 ◽  
Author(s):  
Ngo Khoi ◽  
Sven Casteleyn
Keyword(s):  
Large Scale ◽  
User Behavior ◽  
High Accuracy ◽  
Participatory Sensing ◽  
Design Data ◽  
User Behaviors ◽  
Sensing Applications ◽  
Active Data ◽  
Geographical Context ◽  
Do So

The large number of mobile devices and their increasingly powerful computing and sensing capabilities have enabled the participatory sensing concept. Participatory sensing applications are now able to effectively collect a variety of information types with high accuracy. Success, nevertheless, depends largely on the active participation of the users. In this article, we seek to understand spatial and temporal user behaviors in participatory sensing. To do so, we conduct a large-scale deployment of Citizense, a multi-purpose participatory sensing framework, in which 359 participants of demographically different backgrounds were simultaneously exposed to 44 participatory sensing campaigns of various types and contents. This deployment has successfully gathered various types of urban information and at the same time portrayed the participants’ different spatial, temporal and behavioral patterns. From this deployment, we can conclude that (i) the Citizense framework can effectively help participants to design data collecting processes and collect the required data, (ii) data collectors primarily contribute in their free time during the working week; much fewer submissions are done during the weekend, (iii) the decision to respond and complete a particular participatory sensing campaign seems to be correlated to the campaign’s geographical context and/or the recency of the data collectors’ activities, and (iv) data collectors can be divided into two groups according to their behaviors: a smaller group of active data collectors who frequently perform participatory sensing activities and a larger group of regular data collectors who exhibit more intermittent behaviors. These identified user behaviors open avenues to improve the design and operation of future participatory sensing applications.

scholarly journals Large-scale study of city dynamics and urban social behavior using participatory sensing

2014 ◽  
Vol 21 (1) ◽  
pp. 42-51 ◽  
Author(s):  
Thiago Silva ◽  
Pedro S. Vaz De Melo ◽  
Jussara Almeida ◽  
Antonio F. Loureiro
Keyword(s):  
Social Behavior ◽  
Large Scale ◽  
Participatory Sensing ◽  
Large Scale Study ◽  
City Dynamics
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