MetroSense: opportunistic crowd-sourced sensing (2007-15)

This project is no longer active; this page is no longer updated.

Related projects: [Mobility-models]

Related keywords: [privacy], [security], [sensors], [wearable]


The MetroSense project was a broad project that explored the potential for "opportunistic crowd-sensing": crowd-sourced collection of sensor data from mobile users carrying smartphones or other sensing devices. One of our papers summarized the many challenges [kapadia:metrosec-challenges].

In our group, we developed the AnonySense system, which includes novel mechanisms for the anonymous collection of sensor data from people who volunteer their cell phones as part of a distributed sensing platform, addressing a key challenge in the important area of participatory and opportunistic urban sensing, and developed a novel interface to allow people to specify how sensor data about them might be shared with others. To evaluate this work, we measured system performance in terms of bandwidth and power consumption, conducted a user study, and used large wireless-network traces from the Dartmouth campus. [cornelius:anonysense, kapadia:anonysense, shin:anonysense].

In related work we developed a spatiotemporal blurring mechanism based on tessellation and clustering of space according to the location of access points and the relative density of users in each region [shin:anonytiles].

In related work, a student looked at linkability in activity inference data sets [fielding:thesis].

In a subproject called PLACE (Privacy in Location-Aware Computing Environments) [anthony:pervasive], we also developed a method for access control called virtual walls. By allowing users to deploy 'virtual walls', they can control the privacy of their digital footprints much in the same way they control their privacy in the physical world. We presented a policy framework and model for virtual walls with three levels of transparency that correspond to intuitive levels of privacy. We also described the results of a user study (N=23) that indicated that our model is easy to understand and use. [kapadia:walls].

We also developed DEAMON, an energy-efficient distributed algorithm for long-term sensor monitoring. Our approach assumes only that mobile nodes are tasked to report sensor data under conditions specified by a Boolean expression, and that a network of nearby sensor nodes contribute to monitoring subsets of the task's sensors. Our algorithm to select sensor nodes and to monitor the sensing condition conserves energy of all nodes by limiting sensing and communication operations. We evaluated DEAMON with a stochastic analysis and with simulation results, and show that it should significantly reduce energy consumption [shin:deamon].


Denise Anthony, Cory Cornelius, Jeffrey Fielding, Tristan Henderson, Peter Johnson, Apu Kapadia, David Kotz, Dan Peebles, Minho Shin, Nikos Triandopoulos, Patrick Tsang.

Funding and acknowledgements

This research was funded by the Institute for Security Technology Studies (ISTS), supported by Grants 2005-DD-BX-1091 awarded by the Bureau of Justice Assistance, 60NANB6D6130 awarded by the U.S. Department of Commerce, and 2006-CS-001-000001 awarded by the U.S. Department of Homeland Security.

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Papers (tagged 'metrosense')

[Also available in BibTeX]

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[Kotz research]