The widespread use of mobile devices has allowed the development of participatory sensing systems that capture various types of data using the existing or external sensors attached to mobile devices. Gathering data from such anonymous sources requires a mechanism to establish the integrity of sensor readings. In many cases, sensor data need to be preprocessed on the device itself before being uploaded to the target server while ensuring the chain of trust from capture to the delivery of the data. This can be achieved by a framework that provides a means to implement arbitrary operations to be performed on trusted sensor data, while guaranteeing the security and integrity of the data. This paper presents the design and implementation of a framework that allows the capture of trusted sensor data from both external and internal sensors on a mobile phone along with the development of trusted operations on sensor data while providing a mechanism for performing predefined operations on the data such that the chain of trust is maintained. The evaluation shows that the proposed system ensures the security and integrity of sensor data with minimal performance overhead.
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http://dx.doi.org/10.3390/s18051364 | DOI Listing |
J Agric Saf Health
October 2024
Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, Indiana, USA.
Highlights: The purpose of this research was to validate a test procedure for using commercially available smart technologies in detecting an agricultural-related incident. A convenient selection of commercially available wearable devices was used to measure the inertial qualities of simulated incidents. Simulated ejections, falls, and upsets were performed to recreate leading causes of agricultural injuries and fatalities using an anthropomorphic test device.
View Article and Find Full Text PDFJ Exp Med
June 2025
Department of Molecular Microbiology and Immunology, Oregon Health and Science University, Portland, OR, USA.
To distinguish pathogens from commensals, the intestinal epithelium employs cytosolic innate immune sensors. Activation of the NAIP-NLRC4 inflammasome initiates extrusion of infected intestinal epithelial cells (IEC) upon cytosolic bacterial sensing. We previously reported that activation of the inflammasome in tuft cells, which are primarily known for their role in parasitic infections, leads to the release of prostaglandin D2 (PGD2).
View Article and Find Full Text PDFNicotine Tob Res
March 2025
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena CA 91125 USA.
A minimally invasive "continuous nicotine monitor" (CNM) would resolve the dynamic nicotine concentration, [nicotine]t, faced by high-sensitivity nicotinic acetylcholine receptors (nAChRs) during and after nicotine intake by individual subjects. Motivations: "Know the potential enemy at an individual level". Smoking or vaping produces an initial "bolus" of nicotine in the blood and brain, lasting ~5 min with a peak concentration of ~ 100-200 nM.
View Article and Find Full Text PDFRSC Adv
March 2025
School of Humanities and Management, Heilongjiang University of Chinese Medicine Harbin PR China.
Wearable sensors have emerged as a transformative technology, enabling real-time monitoring and advanced functionality in various fields, including healthcare, human-machine interaction, and environmental sensing. This review provides a comprehensive overview of the latest advancements in wearable sensor technologies, focusing on innovations in sensor design, material flexibility, and integration with machine learning. We explore the feasibility of wearable electronics in achieving high-performance, flexible devices and discuss their potential to enhance human-machine interactions through intelligent data processing and decision-making.
View Article and Find Full Text PDFFront Plant Sci
February 2025
Automatic Control and System Dynamics, Chemnitz University of Technology, Chemnitz, Germany.
This is the first study who presents an approach to predict secondary metabolites content in tomatoes using multivariate time series classification of greenhouse sensor data, which includes climatic conditions as well as photosynthesis and transpiration rates. The aim was to find the necessary conditions in a greenhouse to determine the maximum content of secondary metabolites, as higher levels in fruits can promote human health. For this, we defined multiple classification tasks and derived suitable classification function.
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