Community and citizen science on climate change-influenced topics offers a way for participants to actively engage in understanding the changes and documenting the impacts. As in broader climate change education, a focus on the negative impacts can often leave participants feeling a sense of powerlessness. In large scale projects where participation is primarily limited to data collection, it is often difficult for volunteers to see how the data can inform decision making that can help create a positive future. In this paper, we propose and test a method of linking community and citizen science engagement to thinking about and planningmore »
Insider-Resistant Context-Based Pairing for Multimodality Sleep Apnea Test
The increasingly sophisticated at-home screening
systems for obstructive sleep apnea (OSA), integrated with
both contactless and contact-based sensing modalities, bring
convenience and reliability to remote chronic disease management.
However, the device pairing processes between system
components are vulnerable to wireless exploitation from a noncompliant
user wishing to manipulate the test results. This work
presents SIENNA, an insider-resistant context-based pairing
protocol. SIENNA leverages JADE-ICA to uniquely identify a
user’s respiration pattern within a multi-person environment
and fuzzy commitment for automatic device pairing, while using
friendly jamming technique to prevent an insider with knowledge
of respiration patterns from acquiring the pairing key. Our
analysis and test results show that SIENNA can achieve reliable
(> 90% success rate) device pairing under a noisy environment
and is robust against the attacker with full knowledge of the
context information.
- Award ID(s):
- 1662487
- Publication Date:
- NSF-PAR ID:
- 10312063
- Journal Name:
- IEEE Global Communications Conference: Communication & Information Systems Security - Communication & Information System Security
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
To create safer and less congested traffic operating environments researchers at the University of Tennessee at Chattanooga (UTC) and the Georgia Tech Research Institute (GTRI) have fostered a vision of cooperative sensing and cooperative mobility. This vision is realized in a mobile application that combines visual data extracted from cameras on roadway infrastructure with a user’s coordinates via a GPS-enabled device to create a visual representation of the driving or walking environment surrounding the application user. By merging the concepts of computer vision, object detection, and mono-vision image depth calculation, this application is able to gather absolute Global Positioning Systemmore »
-
Context-based pairing solutions increase the usability of IoT device pairing by eliminating any human involvement in the pairing process. This is possible by utilizing on-board sensors (with same sensing modalities) to capture a common physical context (e.g., ambient sound via each device’s microphone). However, in a smart home scenario, it is impractical to assume that all devices will share a common sensing modality. For example, a motion detector is only equipped with an infrared sensor while Amazon Echo only has microphones. In this paper, we develop a new context-based pairing mechanism called Perceptio that uses time as the common factormore »
-
Arctic Treeline is the transition from the boreal forest to the treeless tundra and may be determined by growing season temperatures. The physiological mechanisms involved in determining the relationship between the physical and biological environment and the location of treeline are not fully understood. In Northern Alaska, we studied the relationship between temperature and leaf respiration in 36 white spruce ( Picea glauca ) trees, sampling both the upper and lower canopy, to test two research hypotheses. The first hypothesis is that upper canopy leaves, which are more directly coupled to the atmosphere, will experience more challenging environmental conditions andmore »
-
Cyberbullying is a prevalent concern within social computing research that has led to the development of several supervised machine learning (ML) algorithms for automated risk detection. A critical aspect of ML algorithm development is how to establish ground truth that is representative of the phenomenon of interest in the real world. Often, ground truth is determined by third-party annotators (i.e., “outsiders”) who are removed from the situational context of the interaction; therefore, they cannot fully understand the perspective of the individuals involved (i.e., “insiders”). To understand the extent of this problem, we compare “outsider” versus “insider” perspectives when annotating 2,000more »