Physical Sciences and Mathematics Commons^™

Location Privacy For Mobile Crowd Sensing Through Population Mapping, Minho Shin, Cory Cornelius, Apu Kapadia, Nikos Triandopoulos, David Kotz

Dartmouth Scholarship

Opportunistic sensing allows applications to “task” mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street or users' mobile phones to locate (Bluetooth-enabled) objects in their vicinity. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk: even if identifying information has been removed from a report, the accompanying time and location can reveal sufficient information to de-anonymize the user whose device sent the report. We propose and evaluate a novel spatiotemporal …

Trip: Tracking Rhythms In Plants, An Automated Leaf Movement Analysis Program For Circadian Period Estimation, Kathleen Greenham, Ping Lou, Sara E. Remsen, Hany Farid, C Robertson Mcclung

Dartmouth Scholarship

Background: A well characterized output of the circadian clock in plants is the daily rhythmic movement of leaves. This process has been used extensively in Arabidopsis to estimate circadian period in natural accessions as well as mutants with known defects in circadian clock function. Current methods for estimating circadian period by leaf movement involve manual steps throughout the analysis and are often limited to analyzing one leaf or cotyledon at a time.

Methods: In this study, we describe the development of TRiP (Tracking Rhythms in Plants), a new method for estimating circadian period using a motion estimation algorithm that can …

Data Citation Practices In The Crawdad Wireless Network Data Archive, Tristan Henderson, David Kotz

Dartmouth Scholarship

CRAWDAD (Community Resource for Archiving Wireless Data At Dartmouth) is a popular research data archive for wireless network data, archiving over 100 datasets used by over 6,500 users. In this paper we examine citation behaviour amongst 1,281 papers that use CRAWDAD datasets. We find that (in general) paper authors cite datasets in a manner that is sufficient for providing credit to dataset authors and also provides access to the datasets that were used. Only 11.5% of papers did not do so; common problems included (1) citing the canonical papers rather than the dataset, (2) describing the dataset using unclear identifiers, …