Modeling behavior data in security and defense applications.
Abstract not provided.
Abbott, Robert G.↗
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Results for “secure data”
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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RouteE: A Vehicle Energy Consumption Prediction Engine
The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.
47 OTHER INSTRUMENTATION↗
Real-World Driving Features for Identifying Intelligent Driver Model Parameters: Preprint
Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver. It stands to reason that determining the parameter values from real-world driving data would serve the purpose of representing a real-world driver. While the published literature is replete with techniques and consequences of using real-world driving data to determine parameter values for parametric driver models, none have explored them in the context of using shorter driving features where the parameter values may change over the course of a single trip for the same driver. In this study we consider the “Intelligent Driver Model” (IDM) as the driver behavior model to explore and real-world driving data from the Transportation Secure Data Center (TSDC) maintained at the National Renewable Energy Laboratory (NREL). The TSDC includes real-world travel data from across the United States, from which NREL has created a wide range of driving routes consisting of road features such as speed limits, stop locations and turn locations. The real-world driving data are categorized into different driving regimes and extracted into driving features. The driving features are then used to calibrate the parameter values for the IDM. The distribution of the parameters and the relationships among them are reported. The insights obtained from this study enable judicious usage of IDM or similar parametric driver models to represent baseline human driver behavior in simulations.
27 ARPA - Advanced Research Projects Agency-Energy↗
Fine-Grained Containerization for Data Authentication, Security, and Portable Workflows.
Abstract not provided.
Lofstead, Gerald↗
Deliverable D11 – Data Sharing, Storage, Security Protocols, and a Specification of a Potential Data Sharing Portal
Pacific Northwest National Laboratory (PNNL) and Technical University of Denmark (DTU) completed this deliverable as part of Work Package 2: Data Information Catalog for Distributed Wind Research for the International Energy Agency (IEA) Wind Technology Collaboration Programme Task 41: Enabling Wind to Contribute to a Distributed Energy Future. As part of the work plan, Deliverable D11 requires the development of data sharing, storage, and security protocols for metadata to be stored on the platform, if needed. The specification of a potential data sharing portal that expands on the catalog is also required.
17 WIND ENERGY↗
Analysis Reveals Impact of Road Grade on Vehicle Energy Use (Fact Sheet)
With funding from the U.S. Department of Energy’s Vehicle Technologies Office, engineers from the National Renewable Energy Laboratory (NREL) performed an analysis quantifying the incremental efficiency impacts of road grade by appending high-fidelity elevation profiles to GPS speed traces. This in-depth study employed nationwide elevation data from a U.S. Geological Survey digital elevation model as well as detailed GPS data from NREL’s Transportation Secure Data Center with drive cycles from more than 250,000 unique trips in various metropolitan areas across the country.
33 ADVANCED PROPULSION SYSTEMS↗
Archiving Data from New Survey Technologies: Lessons Learned on Enabling Research with High-Precision Data While Preserving Participant Privacy: Preprint
During the past 15 years, increasing numbers of organizations and planning agencies have begun collecting high-resolution Global Positioning System (GPS) travel data. Despite the significant effort and expense to collect it, privacy concerns often lead to underutilization of the data. To address this dilemma of providing data access while preserving privacy, the National Renewable Energy Laboratory, with support from the U.S. Department of Transportation and U.S. Department of Energy, established the Transportation Secure Data Center (TSDC). Lessons drawn from best-practice examples from other data centers have helped shape the structure and operating procedures for the TSDC, which functions under the philosophy of first and foremost preserving privacy, but doing so in a way that balances security with accessibility and usability of the data for legitimate research. This paper provides details about the TSDC approach toward achieving these goals, which has included creating a secure enclave with no external access for backing up and processing raw data, a publicly accessible website for downloading cleansed data, and a secure portal environment through which approved users can work with detailed spatial data using a variety of tools and reference information. This paper also describes lessons learned from operating the TSDC with respect to improvements in GPS data handling, processing, and user support, along with plans for continual enhancements to better support the needs of both data providers and users and to thus advance the research value derived from such valuable data.
33 ADVANCED PROPULSION SYSTEMS↗
Universal Utility Data Exchange (UUDEX) Functional Design Requirements: Cybersecurity of Energy Delivery Systems Research and Development
This document provides a set of high-level functional design requirements for Universal Utility Data Exchange (UUDEX). These requirements include a set of use cases, data exchanges supported by UUDEX, both for grid operations and for cyber security data, functional requirements for data exchange, data models used by UUDEX, data exchange architectures, and security considerations. These functional design requirements are intended to be used in more detailed design documents.
97 MATHEMATICS AND COMPUTING↗
Universal Utility Data Exchange (UUDEX) Functional Design Requirements - Rev 1
This document provides a set of high-level functional design requirements for Universal Utility Data Exchange (UUDEX). These requirements include a set of use cases, data exchanges supported by UUDEX, both for grid operations and for cyber security data, functional requirements for data exchange, data models used by UUDEX, data exchange architectures, and security considerations. These functional design requirements are intended to be used in more detailed design documents.
97 MATHEMATICS AND COMPUTING↗
A Survey on Next-generation Power Grid Data Architecture
The operation and control of power grids will increasingly rely on data. A high-speed, reliable, flexible and secure data architecture is the prerequisite of the next-generation power grid. This paper summarizes the challenges in collecting and utilizing power grid data, and then provides reference data architecture for future power grids. Based on the data architecture deployment, related research on data architecture is reviewed and summarized in several categories including data measurement/actuation, data transmission, data service layer, data utilization, as well as two cross-cutting issues, interoperability and cyber security. Research gaps and future work are also presented.
You, Shutang↗
Getting Data Out of the Ground: Modern Data Challenges Facing EGS Collab, the DOE Geothermal Data Repository, and the Geothermal Industry
The amount of data being generated by the geothermal industry is increasing exponentially. Advanced tools, higher resolution sensors, and digitally integrated equipment are providing valuable insight into geothermal exploration and operational efficiencies, but the volumes of data they generate and the unique properties of Enhanced Geothermal System (EGS) projects are creating challenges of their own. This paper explores those challenges and the steps the U.S. Department of Energy (DOE) is taking to address them through comprehensive data management strategies, improvements to DOE's Geothermal Data Repository (GDR), and the development of a secure data collaboration tool for DOE's Frontier Observatory for Research in Geothermal Energy (FORGE) and EGS Collab projects.
15 GEOTHERMAL ENERGY↗
Privacy-Preserving Knowledge Transfer with Bootstrap Aggregation of Teacher Ensembles
There is a need to transfer knowledge among institutions and organizations to save effort in annotation and labeling or in enhancing task performance. However, knowledge transfer is difficult because of restrictions that are in place to ensure data security and privacy. Institutions are not allowed to exchange data or perform any activity that may expose personal information. With the leverage of a differential privacy algorithm in a high-performance computing environment, we propose a new training protocol, Bootstrap Aggregation of Teacher Ensembles (BATE), which is applicable to various types of machine learning models. The BATE algorithm is based on and provides enhancements to the PATE algorithm, maintaining competitive task performance scores on complex datasets with underrepresented class labels.We conducted a proof-of-the-concept study of the information extraction from cancer pathology report data from four cancer registries and performed comparisons between four scenarios: no collaboration, no privacy-preserving collaboration, the PATE algorithm, and the proposed BATE algorithm. The results showed that the BATE algorithm maintained competitive macro-averaged F1 scores, demonstrating that the suggested algorithm is an effective yet privacy-preserving method for machine learning and deep learning solutions.
Yoon, Hong-Jun↗
Chance-Constrained Optimization for Critical Infrastructure Protection
Stochastic optimization deals with making highly reliable decisions under uncertainty. Chance constraints are a crucial tool of stochastic optimization to develop mathematical optimization models; they form the backbone of many important national security data science applications. These include critical infrastructure resiliency, cyber security, power system operations, and disaster relief management. However, existing algorithms to solve chance-constrained optimization models are severely limited by problem size and structure. In this investigative study, we (i) develop new algorithms to approximate chance-constrained optimization models, (ii) demonstrate the application of chance-constraints to a national security problem, and (iii) investigate related stochastic optimization problems. We believe our work will pave way for new research is stochastic optimization as well as secure national infrastructures against unforeseen attacks.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
A robust wireless communication system for electromagnetically harsh environments of nuclear facilities - 155
Wireless sensors could play a significant role in safety, efficiency, and reliability of instrumentation and control process in current and next generation nuclear power reactors. While conventional narrowband wireless sensors have successfully been used in numerous industrial applications, the RF propagation challenges posed by the heavy metallic and cluttered environment of Nuclear Power Plants (NPPs) has prevented their use in such operations. These challenges include: RF propagation in harsh (reflective, absorptive, cluttered) environments, data security issues, and RF interference to and from other devices in a nuclear reactor. In this introductory paper, first we address how Ultra-wideband (UWB) technology can provide a viable solution for the signal propagation issues in such electromagnetically harsh environments. Second, we present a novel UWB remote powering scheme that allows for battery-free operation of sensors to increase their lifetime. We finalize the paper by presenting the experimental results of using UWB signaling in a representative harsh environment conducted at MIT research reactor. We plan to address the security issues through an innovative UWB communications scheme for sensor data communications in a subsequent paper. (authors)
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Data Confidentiality Challenges in Big Data Applications
In this paper, we address the problem of data confidentiality in big data analytics. In many fields, much useful patterns can be extracted by applying machine learning techniques to big data. However, data confidentiality must be protected. In many scenarios, data confidentiality could well be a prerequisite for data to be shared. We present a scheme to provide provable secure data confidentiality and discuss various techniques to optimize performance of such a system.
data confidentiality↗
Secure and Privacy Aware Data Sharing Approach for Smart Electric Vehicles
The integration of smart electric vehicles (SEVs) into smart cities marks a significant step toward creating efficient, sustainable, and connected urban spaces. However, secure and private data sharing is a major challenge as SEVs connect with smart city systems. The interaction between SEVs and consumer electronic devices (CEDs) raises serious concerns about data security and privacy. Here, to address these challenges, this article presents how blockchain technology and federated learning (FL) can address these issues. The proposed approach provides a secure and privacy-aware framework for data exchange between SEVs and CEDs in smart cities. The experiment results demonstrate the effectiveness of the proposed framework for secure data sharing and maintaining system reliability in smart city environments. It also enables trust and promotes the widespread adoption of interconnected urban technologies.
Das, Debashis [Meharry Medical College, Nashville,↗
Livewire Data Platform - A Solution for EEMS Data Sharing
To bring about fundamental changes in our transportation system, data sharing must be endemic. The Livewire Data Platform is a user-friendly, searchable platform allowing easy and secure data sharing and discovery for DOE's Energy Efficient Mobility Systems (EEMS) researchers. The platform was funded by the DOE Vehicle Technologies Office (VTO) to enable a research community that builds partnerships and collaboration around data in order to find answers to today's biggest mobility challenges.
33 ADVANCED PROPULSION SYSTEMS↗
Securing Environmental IoT Data Using Masked Authentication Messaging Protocol in a DAG-Based Blockchain: IOTA Tangle
The demand for the digital monitoring of environmental ecosystems is high and growing rapidly as a means of protecting the public and managing the environment. However, before data, algorithms, and models can be mobilized at scale, there are considerable concerns associated with privacy and security that can negatively affect the adoption of technology within this domain. In this paper, we propose the advancement of electronic environmental monitoring through the capability provided by the blockchain. The blockchain’s use of a distributed ledger as its underlying infrastructure is an attractive approach to counter these privacy and security issues, although its performance and ability to manage sensor data must be assessed. We focus on a new distributed ledger technology for the IoT, called IOTA, that is based on a directed acyclic graph. IOTA overcomes the current limitations of the blockchain and offers a data communication protocol called masked authenticated messaging for secure data sharing among Internet of Things (IoT) devices. We show how the application layer employing the data communication protocol, MAM, can support the secure transmission, storage, and retrieval of encrypted environmental sensor data by using an immutable distributed ledger such as that shown in IOTA. Finally, we evaluate, compare, and analyze the performance of the MAM protocol against a non-protocol approach.
Gangwani, Pranav (ORCID:0000000159226002)↗