PEIR, the personal environmental impact report, as a platform for participatory sensing systems research

By Min Y. Mun, Sasank Reddy, Katie Shilton, Nathan Yau, Jeff Burke, Deborah Estrin, Mark H. Hansen, E. Howard, Ruth West, Péter Pál Boda. 2009

755 citations recorded by the source corpus.

Subfields: Computer Science Applications, Transportation

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In Computer Science Applications

In Transportation

Citations in the corpus

A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy (2015, background): “Data privacy and security issues arise frequently and increasingly in such surveillance systems [1], [35], [42], [51].”
Mobile Phone Sensing Systems: A Survey (2013, background): “Mobile phone sensors are also used for environmental monitoring like pollution, weather and noise monitoring etc. Examples of such systems include [19] [20] [21].”
Secure multiparty aggregation with differential privacy: a comparative study (2013, background): “Data privacy and security issues arise frequently and increasingly in such data surveillance systems [1, 27, 33, 41].”
The Role of Advanced Sensing in Smart Cities (2012, methodology): “This data can be collaboratively used to obtain information regarding the weather with location information [90].”
A survey of mobile phone sensing (2010, neutral): “, GPS [3]), while others require real-time inference (e.”
Participatory Sensing: Crowdsourcing Data from Mobile Smartphones in Urban Spaces (2011, background): “use their mobile phone to determine their exposure to environmental pollutants [4].”
A survey on privacy in mobile participatory sensing applications (2011, background): “Sensitive locations can be selected by the participants and protected using location selective hiding [20], which was introduced in the PEIR campaign presented in Section 2.”
Energy-efficient rate-adaptive GPS-based positioning for smartphones (2010, background): “Similarly, PEIR [18] collects GPS readings approximately every 30 seconds.”
Wireless Sensor Networks for Healthcare (2010, background): “Their complementary characteristics make smartphones and motes complementary platforms suitable for different categories of healthcare applications, which we discuss in the section that follows.”
PRISM: platform for remote sensing using smartphones (2010, background): “The goals have been varie d: monitoring air pollution [25], evaluating soot emission le vels [37], assessing environmental impact of individuals [32], noise mapping [7], matching passengers to cars for dynamic ride sharing [36], u rban gaming [12], sensing enhanced social networking [30], and r ich ”
Named Data Networking (NDN) Project (2010, background): “Data collected for such applications includes GPS trajectories to monitor traffic patterns [31], pollution traces to assess environmental impact [32], vehicular fuel-efficiency measurements to find green routes [30], and health data.”
Privacy and accountability for location-based aggregate statistics (2011, background): “The emergence of location-based mobile services and the interest in using them in road transportation, participatory sensing [30, 22], and various social mobile crowdsourcing applications has led to a fertile area of research and commercial activity.”
Mobile Crowd Sensing and Computing (2015, methodology): “PEIR [Mun et al. 2009] Hybrid, community Continuous Ethical & service Infrastructure Centralized Visualization and sharing”
TRAC: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing (2014, background): “Although a number of mobile crowdsourcing applications [5] [6] [7] [8] have been proposed, most of them have assumed that smartphones voluntarily contribute to the mobile crowdsourcing system, which is not true in the real world.”