Common Sense Community: Scaffolding Mobile Sensing and Analysis for Novice Users

Willett, Wesley; Aoki, Paul M.; Kumar, Neil; Subramanian, Sushmita; Woodruff, Allison

doi:10.1007/978-3-642-12654-3_18

Cited by 92 publications

(53 citation statements)

References 12 publications

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“…New, often low-cost and DIY (do it yourself) technologies enable people (typically 'non-experts') to actively measure a range of environmental factors through participatory sensing [40]. Examples include handheld air quality monitors [14,30], or mobile phones for tracking water quality [22], among many other others.…”

Section: Active Participatory Sensingmentioning

confidence: 99%

Nurturing natural sensors

Kuznetsov

Odom

Pierce

et al. 2011

Proceedings of the 13th International Conference on Ubiquitous Computing

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Sensing has played a significant role in the evolution of ubiquitous computing systems, enabling many of today's compelling interactive and ubiquitous experiences. In this paper, we argue for expanding the current landscape of sensing to include living organisms such as plants and animals, along with traditional tools and digital devices. We present a field study of ten individuals who routinely work with living organisms such as plants, fish, reptiles and bees, and rely on these organisms as well as analog instruments and digital sensors to infer environmental conditions and inform future actions. Our findings offer a new perspective on everyday biomarkers, and we use the lens of organic and non-digital sensing to reflect on current sensing paradigms in ubiquitous computing. We conclude with three opportunity areas to help frame future work in ubiquitous sensing: (1) incorporating traditional technologies and living systems into ubiquitous sensing applications, (2) developing information technologies that teach new ways of 'seeing', and (3) supporting richer forms of metadata to unite stakeholders through their actions, interests and concerns.

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Section: Active Participatory Sensingmentioning

confidence: 99%

Nurturing natural sensors

Kuznetsov

Odom

Pierce

et al. 2011

Proceedings of the 13th International Conference on Ubiquitous Computing

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“…In many recent mobile sensing network designs, researchers have built sensing devices equipped with multiple types of sensors to detect various pollutants co-existing in the environment (Jiang et al, 2011;Willett et al, 2010). For such applications, it is possible to exploit the correlation of readings and recover noisy readings using Bayesian belief networks (Janakiram et al, 2006).…”

Section: Y Xiang Et Al: Mobile Sensor Network Noise Reductionmentioning

confidence: 99%

“…Unfortunately, official monitoring networks are usually very sparse due to their high costs. Therefore, mobile and distributed atmospheric air quality sensor networks are becoming increasingly popular and mainstream (Jiang et al, 2011;Willett et al, 2010;Piedrahita et al, 2014;Xiang, 2014). Those sensor networks are carried by users and are capable of measuring the immediate surrounding atmosphere.…”

Section: Introductionmentioning

confidence: 99%

Mobile sensor network noise reduction and recalibration using a Bayesian network

2016

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Abstract. People are becoming increasingly interested in mobile air quality sensor network applications. By eliminating the inaccuracies caused by spatial and temporal heterogeneity of pollutant distributions, this method shows great potential for atmospheric research. However, systems based on low-cost air quality sensors often suffer from sensor noise and drift. For the sensing systems to operate stably and reliably in real-world applications, those problems must be addressed. In this work, we exploit the correlation of different types of sensors caused by cross sensitivity to help identify and correct the outlier readings. By employing a Bayesian network based system, we are able to recover the erroneous readings and recalibrate the drifted sensors simultaneously. Our method improves upon the state-of-art Bayesian belief network techniques by incorporating the virtual evidence and adjusting the sensor calibration functions recursively.Specifically, we have (1) designed a system based on the Bayesian belief network to detect and recover the abnormal readings, (2) developed methods to update the sensor calibration functions infield without requirement of ground truth, and (3) extended the Bayesian network with virtual evidence for infield sensor recalibration. To validate our technique, we have tested our technique with metal oxide sensors measuring NO 2 , CO, and O 3 in a real-world deployment. Compared with the existing Bayesian belief network techniques, results based on our experiment setup demonstrate that our system can reduce error by 34.1 % and recover 4 times more data on average.

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“…This may take the form of social media [25] or even build upon the use of narratives in pervasive sensing systems (e.g. [22]).…”

Section: Recognition Of Emotional Engagementmentioning

confidence: 99%

Listening to the forest and its curators

Moran

Pantidi

Rodden

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk Listening to the Forest and its Curators: Lessons Learnt from a Bioacoustic Smartphone Application Deployment ABSTRACTOur natural environment is complex and sensitive, and is home to a number of species on the verge of extinction. Surveying is one approach to their preservation, and can be supported by technology. This paper presents the deployment of a smartphone-based citizen science biodiversity application. Our findings from interviews with members of the biodiversity community revealed a tension between the technology and their established working practices. From our experience, we present a series of general guidelines for those designing citizen science apps.

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Common Sense Community: Scaffolding Mobile Sensing and Analysis for Novice Users

Cited by 92 publications

References 12 publications

Nurturing natural sensors

Nurturing natural sensors

Mobile sensor network noise reduction and recalibration using a Bayesian network

Listening to the forest and its curators

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