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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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Privacy-aware contextual localization using network traffic analysis

The rise of location-based services has enabled many opportunities for content service providers to optimize the content delivery to user's wireless devices based on her location. Since the sharing precise location remains a major privacy concern among the users, certain location-based services rely on contextual location (e.g. residence, work, etc.) as opposed to acquiring user's exact physical location. In this paper, we present PACL (Privacy-Aware Contextual Localizer) model, which can learn user's contextual location just by passively monitoring user's network traffic. PACL can discern a set of vital attributes (statistical and application-based) from user's network traffic, and predict user's contextual location with a very high accuracy. We design and evaluate PACL using real-world network traces of over 1700 users with over 100GB of total data. Our results show that PACL, when built using the Bayesian Network machine learning algorithm, can predict user's contextual location with the accuracy of around 89%.

97 MATHEMATICS AND COMPUTING↗

Optimal Control of Connected and Automated Vehicles at Roundabouts: An Investigation in a Mixed-Traffic Environment

Connectivity and automation in vehicles provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better operating decisions to improve safety and reduce pollution, energy consumption, and travel delays. This paper investigates the implications of optimally coordinating vehicles that are wirelessly connected to each other in roundabouts to achieve a smooth traffic flow. We apply an optimization framework and an analytical solution that allows optimal coordination of vehicles for merging in such traffic scenario. The effectiveness of the proposed approach is validated through simulation and it is shown that fully coordinated vehicles reduce total travel time by 51% and fuel consumption by 35%. Furthermore, we study the influence of vehicle coordination in a mixed-traffic environment and compare the network performance under different market penetration rates of connected and automated vehicles (CAVs). For this particular study with near-capacity demand, high penetration of CAVs is necessary to ensure an efficient and effective control, due to extremely unstable traffic.

Zhao, Liuhui↗

Optimal Control of Connected and Automated Vehicles at Roundabouts

Connectivity and automation in vehicles provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better operating decisions to improve safety and reduce pollution, energy consumption, and travel delays. This study investigates the implications of optimally coordinating vehicles that are wirelessly connected to each other and to an infrastructure in roundabouts to achieve a smooth traffic flow without stop-and-go driving. We apply an optimization framework and an analytical solution that allows optimal coordination of vehicles for merging in such traffic scenario. The effectiveness of the efficiency of the proposed approach is validated through simulation and it is shown that coordination of vehicles can reduce total travel time by 3~49% and fuel consumption by 2~27% with respect to different traffic levels. In addition, network throughput is improved by up to 25% due to elimination of stop-and-go driving behavior.

Zhao, Liuhui↗

Deployment of Rapid Assay Systems for Real-time Sorting of Excavation Spoil on Contaminated Land - 19258

To optimise segregation at source, and hence minimise waste production, NUVIA have developed and utilised the Gamma Excavation Monitor (GEM) system. The GEM was designed for land remediation projects, to provide a rapid screening tool for excavator buckets of material. Following successful deployment in the UK, including the Olympic Park site in London, and two major Magnox land remediation projects, the first GEM unit was deployed to Canadian Nuclear Laboratories' (CNL) Chalk River Laboratory (CRL) in 2016. The GEM unit was fitted with a plastic scintillator detector; recent developments have included the utilisation of a sodium iodide. The system is initiated by the presence of an excavator bucket above the detector which triggers proximity switches that automatically start the counting routine. At the end of a short count time, typically 10 seconds, the materials category is indicated by means of a traffic light system, based on either the gross gamma activity or a photo-peak. Using this instrument, material determined not to be radioactive can be re-used on-site. In addition to the traffic light system, results are also transmitted wirelessly to a tablet device. These combined features allow operation of the system without the need to have operatives near the mechanical excavator bucket; therefore, eliminating a significant industrial hazard. Deployments of the GEM on the CRL site have included Storm Water Management project, within the CRL controlled area, and the Foundation Road Landfill Project. The use of the GEM in these projects enabled in-situ decisions to be made regarding the segregation and sentencing of soil rather than having to wait for the analysis of samples; saving time and cost to the project. The Building 492 project involved the excavation of soil within a supervised area to prepare for the installation of modular trailer units. For this specific job, the GEM was calibrated to ensure soil met clearance criteria as stipulated in the Nuclear Substances and Radiation Devices Regulations (Canadian Nuclear Safety Commission; SOR/2000-207). A total of 326 loads of soil were monitored during this project and re-assurance sampling conducted by CNL corroborated the results produced by the GEM system. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

TDD LoRa and Delta Encoding in Low-Power Networks of Environmental Sensor Arrays for Temperature and Deformation Monitoring

Abstract Densely distributed sensor networks can revolutionize environmental observations by providing real-time data with an unprecedented spatiotemporal resolution. However, field deployments often pose unique challenges in terms of power provisions and wireless connectivity. We present a framework for wirelessly connected distributed sensor arrays for near-surface temperature and/or deformation monitoring. Our research focuses on a novel time division duplex implementation of the LoRa protocol, enabling battery powered base stations and avoiding collisions within the network. In order to minimize transmissions and improve battery life throughout the network, we propose a dedicated delta encoding algorithm that utilizes the spatial and temporal similarity in the acquired data sets. We implemented the developed technologies in a AA battery powered hardware platform that can be used as a wireless data logger or base station, and we conducted an assessment of the power consumption. Without data compression, the projected battery life for a data logger is 4.74 years, and a wireless base stations can last several weeks or months depending on the amount of network traffic. The delta encoding algorithm can further improve this battery life with a factor of up to 3.50. Our results demonstrate the viability of the proposed methods for low-power environmental wireless sensor networks.

54 ENVIRONMENTAL SCIENCES↗