Search NASA⌕ Search

SEARCH · Search NASA

Results for “COMMUNICATION NETWORKS”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Latency Minimizing Tasking for Information Processing Systems

Real-time cyber-physical systems and information processing clusters require system designers to consider the total latency involved in collecting and aggregating data. For example, applications such as wild-fire monitoring require data to be presented to users in a timely manner. However, most models and algorithms for sensor networks have focused on alternative metrics such as energy efficiency. In this paper, we present a new model of sensor network aggregation that focuses on total latency. Our model is flexible and enables users to configure varying transmission and computation time on a node-by-node basis, and thus enables the simulation of complex computational phenomena. In addition, we present results from three tasking algorithms that trade-off local communication for overall latency performance. These algorithms are evaluated in simulated networks of up to 200 nodes. We've presented an aggregation-focused model of sensor networks that can be used to study the trade-offs between computational coverage and total latency. Our model explicitly takes into account transmission and computation times, and enables users to define different values for the basestation. In addition, we've presented three different tasking algorithms that operate over model to produce aggregation schedules of varying quality. In the future, we expect to continue exploring distributed tasking algorithms for information processing systems. We've shown that the gap between highly optimized schedules that use global information is quite large relative to our distributed algorithms. This gives us encouragement that future distributed tasking algorithms can still make large gains.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

Network Randomization and Dynamic Defense for Critical Infrastructure Systems

Critical Infrastructure control systems continue to foster predictable communication paths, static configurations, and unpatched systems that allow easy access to our nation's most critical assets. This makes them attractive targets for cyber intrusion. We seek to address these attack vectors by automatically randomizing network settings, randomizing applications on the end devices themselves, and dynamically defending these systems against active attacks. Applying these protective measures will convert control systems into moving targets that proactively defend themselves against attack. Sandia National Laboratories has led this effort by gathering operational and technical requirements from Tennessee Valley Authority (TVA) and performing research and development to create a proof-of-concept solution. Our proof-of-concept has been tested in a laboratory environment with over 300 nodes. The vision of this project is to enhance control system security by converting existing control systems into moving targets and building these security measures into future systems while meeting the unique constraints that control systems face.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Performance Evaluation of an Advanced Distributed Energy Resource Management Algorithm

This paper presents performance evaluation of a new distributed energy resource management system (DERMS) algorithm via an advanced hardware-in-the-loop (HIL) platform. The HIL platform provides realistic testing in a laboratory environment, including the accurate modeling of sub-transmission and distribution networks, the DERMS software controller, and 84 power hardware solar photovoltaic (PV) inverters, standard communication protocols, and a capacitor bank controller. The DERMS algorithm is also called, Grid-Optimization of Solar (GO-Solar) platform which includes predictive state estimation (PSE) and online multiple objective optimization (OMOO) to dispatch the legacy devices and distributed energy resources (e.g., PV). The voltage regulation performance is evaluated under three scenarios, volt-var smart inverter (baseline), and DERMS control for 100% and 30% of PV. The results show that controlling 30% of PV systems with the GO-Solar platform may provide the best balance of control performance and implementation cost.

distributed energy resource management system (DER↗
DOE OSTI2020

Open-Shell Donor–Acceptor Conjugated Polymers with High Electrical Conductivity

Conductive polymers largely derive their electronic functionality from chemical doping, processes by which redox and charge-transfer reactions form mobile carriers. While decades of research have demonstrated fundamentally new technologies that merge the unique functionality of these materials with the chemical versatility of macromolecules, doping and the resultant material properties are not ideal for many applications. Here, it is demonstrated that open-shell conjugated polymers comprised of alternating cyclopentadithiophene and thiadiazoloquinoxaline units can achieve high electrical conductivities in their native “undoped” form. Spectroscopic, electrochemical, electron paramagnetic resonance, and magnetic susceptibility measurements demonstrate that this donor–acceptor architecture promotes very narrow bandgaps, strong electronic correlations, high-spin ground states, and long-range π-delocalization. A comparative study of structural variants and processing methodologies demonstrates that the conductivity can be tuned up to 8.18 S cm -1 . This exceeds other neutral narrow bandgap conjugated polymers, many doped polymers, radical conductors, and is comparable to commercial grades of poly(styrene-sulfonate)-doped poly(3,4-ethylenedioxythiophene). X-ray and morphological studies trace the high conductivity to rigid backbone conformations emanating from strong π-interactions and long-range ordered structures formed through self-organization that lead to a network of delocalized open-shell sites in electronic communication. The results offer a new platform for the transport of charge in molecular systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Securely Aggregated Coded Matrix Inversion

Coded computing is a method for mitigating straggling workers in a centralized computing network, by using erasure-coding techniques. Federated learning is a decentralized model for training data distributed across client devices. In this work we propose approximating the inverse of an aggregated data matrix, where the data is generated by clients; similar to the federated learning paradigm, while also being resilient to stragglers. To do so, we propose a coded computing method based on gradient coding. We modify this method so that the coordinator does not access the local data at any point; while the clients access the aggregated matrix in order to complete their tasks. Here, the network we consider is not centrally administrated, and the communications which take place are secure against potential eavesdroppers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Buyers Collusion in Incentivized Forwarding Networks: A Multi-Agent Reinforcement Learning Study

We present the issue of monetarily incentivized forwarding in a multi-hop mesh network architecture from an economic perspective. It is anticipated that credit-incentivized forwarding and relaying will be a simple method of exchanging transmission power and spectrum for connectivity. However, gateways and forwarding nodes, like any other free market, may create an oligopolistic market for the users they serve. In this study, a coalition scheme between buyers aims to address price control by gateways or nodes closer to gateways. In a Stackelberg competition game, buyer agents (users) and sellers (gateways) make decisions using reinforcement learning (RL), with decentralized Deep Q-Networks to buy and sell forwarding resources. We allow communication links between the buyers with a limited messaging space, without defining a collusion mechanism. The idea is to demonstrate that through messaging, and RL tacit collusion can emerge between agents in a decentralized setup. The multi-agent reinforcement learning (MARL) system is presented and analyzed from a machine-learning perspective. Moreover, MARL dynamics are discussed via mean field analysis to better understand divergence causes and make implementation recommendations for such systems. Finally, the simulation results show the results of coordination among the users.

42 ENGINEERING↗
DOE OSTI2018

Software-defined quantum network switching

We present the design and implementation of a software-defined quantum networking protocol and software switch integrated with a numerical quantum channel simulator. Our protocol design leverages recent advances in the OpenFlow protocol that enable software-defined control and management of optical network traffic using side-channel metadata. We implement this design using customization of the open source vSwitch for optical network routing, and we test the implementation using a numerical simulator of the quantum channel alongside actual network traffic. Our results support the integration of quantum communication with existing optical transport methods.

Humble, Travis↗
DOE OSTI2018

Scalable deep text comprehension for Cancer surveillance on high-performance computing

Background: Deep Learning (DL) has advanced the state-of-the-art capabilities in bioinformatics applications which has resulted in trends of increasingly sophisticated and computationally demanding models trained by larger and larger data sets. This vastly increased computational demand challenges the feasibility of conducting cutting-edge research. One solution is to distribute the vast computational workload across multiple computing cluster nodes with data parallelism algorithms. In this study, we used a High-Performance Computing environment and implemented the Downpour Stochastic Gradient Descent algorithm for data parallelism to train a Convolutional Neural Network (CNN) for the natural language processing task of information extraction from a massive dataset of cancer pathology reports. We evaluated the scalability improvements using data parallelism training and the Titan supercomputer at Oak Ridge Leadership Computing Facility. To evaluate scalability, we used different numbers of worker nodes and performed a set of experiments comparing the effects of different training batch sizes and optimizer functions.Results: We found that Adadelta would consistently converge at a lower validation loss, though requiring over twice as many training epochs as the fastest converging optimizer, RMSProp. The Adam optimizer consistently achieved a close 2nd place minimum validation loss significantly faster; using a batch size of 16 and 32 allowed the network to converge in only 4.5 training epochs.Conclusions: We demonstrated that the networked training process is scalable across multiple compute nodes communicating with message passing interface while achieving higher classification accuracy compared to a traditional machine learning algorithm.

Qiu, John X.↗
DOE OSTI2013

Award ER25750: Coordinated Infrastructure for Fault Tolerance Systems Indiana University Final Report

The main purpose of the Coordinated Infrastructure for Fault Tolerance in Systems initiative has been to conduct research with a goal of providing end-to-end fault tolerance on a systemwide basis for applications and other system software. While fault tolerance has been an integral part of most high-performance computing (HPC) system software developed over the past decade, it has been treated mostly as a collection of isolated stovepipes. Visibility and response to faults has typically been limited to the particular hardware and software subsystems in which they are initially observed. Little fault information is shared across subsystems, allowing little flexibility or control on a system-wide basis, making it practically impossible to provide cohesive end-to-end fault tolerance in support of scientific applications. As an example, consider faults such as communication link failures that can be seen by a network library but are not directly visible to the job scheduler, or consider faults related to node failures that can be detected by system monitoring software but are not inherently visible to the resource manager. If information about such faults could be shared by the network libraries or monitoring software, then other system software, such as a resource manager or job scheduler, could ensure that failed nodes or failed network links were excluded from further job allocations and that further diagnosis could be performed. As a founding member and one of the lead developers of the Open MPI project, our efforts over the course of this project have been focused on making Open MPI more robust to failures by supporting various fault tolerance techniques, and using fault information exchange and coordination between MPI and the HPC system software stack from the application, numeric libraries, and programming language runtime to other common system components such as jobs schedulers, resource managers, and monitoring tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Optimizing Energy Efficiency of Plants: Quantitative Analysis of Carbon Flux

The efficiency in harvesting light energy is essential for both crop and feedstock productivity. Key targets for optimization of energy efficiency include energy capture, conversion, translocation and storage. Surprisingly little is known about the mechanisms of carbon allocation and the communication between source and sink. We surmise that regulatory networks that coordinate carbon partitioning and allocation might be control points and thus suitable for optimization by engineering. At a first step towards identifying such regulatory networks, our lab identified key players in missing steps of allocation, specifically cellular sugar uptake (SUT family, founding members identified by my team) and cellular efflux carriers (SWEET superfamily, founding members identified by my team). In plants, both classes of transporters are encoded by multigene families. We show that specific subsets of SWEETs serve critical roles in cellular sucrose efflux in the leaf for phloem loading, in seeds for seed filling, as well as in nectar section and vacuolar storage; similarly SUTs play critical roles in cellular uptake during phloem loading and seed filling. Access to the plant SWEETs provided the clues needed for identifying novel bacterial and human sugar transporters. Moreover, we found that pathogens hijack SWEETs to access the plants energy resources. In contrast to SUTs, which are predominantly proton symporters, SWEETs appear to function as facilitators, and thus must be tightly regulated to prevent spillover of carbon into cell walls. One of the key goals of this project was to identify such carriers, characterize the role of key members in carbon allocation. To decrypt the biological circuits we need novel diagnostic tools that, analogous to oscilloscopes in electrical engineering of circuit boards, allow us to quantify fluxes; here by monitoring sugars. Such probes can provide crucial information on systems properties such as metabolic impedance. To measure steady state metabolite levels with minimal invasive methods, and to develop impedance measurements in vivo, we constructed and optimized a set of probes, namely Förster Resonance Energy Transfer (FRET) nanosensors for glucose and sucrose. The probes monitor the concentration of a specific metabolite with high temporal resolution, and, since encoded genetically, can be targeted to specific cells or even specific cellular compartments and thus obtain measurements with cellular and subcellular resolution. We also developed a new platform for monitoring such sensors in vivo, the RootChip.

09 BIOMASS FUELS↗
DOE OSTI2021

Chess Master Project

This final technical report tracks the accomplishments of the Chess Master Project to the statement of project objects and resulting commercialization of the technology. Objectives for the project include 1) to sustain critical energy delivery functions during a cyber intrusion, control system operators need the ability to automate identification and containment of the affected network areas, and re-route critical information and control flows around; and 2) to effectively isolate impacted network areas and re-route critical flows, control system network operators need a global view of all the communication flows and have a method to proactively determine the whitelisted communications and how to respond to communications when adversarial behavior is detected.

42 ENGINEERING↗
DOE OSTI2024

Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits

The primary objective of the study was development of a machine learning (ML)-based workflow for fracture hit (“frac hit”) detection and monitoring using shale oil-field data such as drilling surveys, production history (oil and produced water), pressure, and fracking start time and duration records. The ML method takes advantage of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks to identify the frac hits due to hydraulic communication between the fracking child well(s) and the producing parent well(s) within the same pad (intra-pad interaction) and/or on different pads (inter-pad interaction). It utilizes time series of pressure and production data from within a pad and from adjacent pads. The workflow can capture time variable features of frac hits when the model architecture is deep and wide enough, with enough trainable parameters for deep learning and feature extraction, as demonstrated in this paper by using training and testing subsets of the field data from selected neighboring pads with over a couple of hundred wells. The study was focused on frac-hit interaction among paired wells and demonstrated that the ML model, once trained, can predict the frac-hit probability.

58 GEOSCIENCES↗
DOE OSTI2011

Process for anodizing a robotic device

A robotic device has a base and at least one finger having at least two links that are connected in series on rotary joints with at least two degrees of freedom. A brushless motor and an associated controller are located at each joint to produce a rotational movement of a link. Wires for electrical power and communication serially connect the controllers in a distributed control network. A network operating controller coordinates the operation of the network, including power distribution. At least one, but more typically two to five, wires interconnect all the controllers through one or more joints. Motor sensors and external world sensors monitor operating parameters of the robotic hand. The electrical signal output of the sensors can be input anywhere on the distributed control network. V-grooves on the robotic hand locate objects precisely and assist in gripping. The hand is sealed, immersible and has electrical connections through the rotary joints for anodizing in a single dunk without masking. In various forms, this intelligent, self-contained, dexterous hand, or combinations of such hands, can perform a wide variety of object gripping and manipulating tasks, as well as locomotion and combinations of locomotion and gripping.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Creating an Energy Intelligent Campus: Data Integration Challenges and Solutions at a Large Research Campus

Rich, well-organized building performance and energy consumption data enable a host of analytic capabilities for building owners and operators, from basic energy benchmarking to detailed fault detection and system optimization. Unfortunately, data integration for building control systems is challenging and costly in any setting. Large portfolios of buildings--campuses, cities, and corporate portfolios--experience these integration challenges most acutely. These large portfolios often have a wide array of control systems, including multiple vendors and nonstandard communication protocols. They typically have complex information technology (IT) networks and cybersecurity requirements and may integrate distributed energy resources into their infrastructure. Although the challenges are significant, the integration of control system data has the potential to provide proportionally greater value for these organizations through portfolio-scale analytics, comprehensive demand management, and asset performance visibility. As a large research campus, the National Renewable Energy Laboratory (NREL) experiences significant data integration challenges. To meet them, NREL has developed an architecture for effective data collection, integration, and analysis, providing a comprehensive view of data integration based on functional layers. The architecture is being evaluated on the NREL campus through deployment of three pilot implementations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Short-Term Forecasting Across a Network for the Autonomous Wind Farm: Preprint

In an autonomous wind farm, turbines will use information from nearby turbines to achieve wind farm-level objectives such as optimizing the overall performance of a wind farm, ensuring resiliency when other sensors fail, and adapting to changing local conditions. In this paper, the wind farm can be modeled as a network within which turbines (nodes) share information across designated communication channels, with a focus on turbines at the outside of the wind farm capturing local effects and sharing that information with downstream turbines. Understanding of varied inflow conditions can be especially important in complex terrain. This information can be used to monitor turbines, self-organize turbines into groups, and predict the power performance of a wind farm. In particular, this paper describes an autonomous wind farm that incorporates information from local sensors in real time to predict wind speed and wind direction at each turbine over a short-term horizon. Results indicate that the estimate of wind direction can be used to improve the knowledge of the wind speed and direction over the persistence method on a 10- 15-minute time horizon. These short-term forecasts can also be used to facilitate advanced control methods such as feedforward control within a wind farm.

17 WIND ENERGY↗
DOE OSTI2019

Short-Term Forecasting Across a Network for the Autonomous Wind Farm

In an autonomous wind farm, turbines will use information from nearby turbines to achieve wind farm-level objectives such as optimizing the overall performance of a wind farm, ensuring resiliency when other sensors fail, and adapting to changing local conditions. In this paper, the wind farm can be modeled as a network within which turbines (nodes) share information across designated communication channels, with a focus on turbines at the outside of the wind farm capturing local effects and sharing that information with downstream turbines. Understanding of varied inflow conditions can be especially important in complex terrain. This information can be used to monitor turbines, self-organize turbines into groups, and predict the power performance of a wind farm. In particular, this paper describes an autonomous wind farm that incorporates information from local sensors in real time to predict wind speed and wind direction at each turbine over a short-term horizon. Results indicate that the estimate of wind direction can be used to improve the knowledge of the wind speed and direction over the persistence method on a 10-15-minute time horizon. These short-term forecasts can also be used to facilitate advanced control methods such as feedforward control within a wind farm.

17 WIND ENERGY↗
DOE OSTI2020

ModuleOT: A Hardware Security Module for Operational Technology: Preprint

With increasing penetration levels of distributed energy resources (DERs) on the distribution grid, as well as new technological advancements in the cyber space, new cyberattack vectors are being introduced, and the available attack surface is constantly increasing. Despite this increasing risk, the standard IEEE 1547-2018 does not yet recommend cybersecurity measures for DERs. To address this and to better protect data on the distribution grid - from the standpoints information security as well as operational security - ModuleOT has been developed. The module aims to significantly reduce cyberattack vectors by improving data privacy for user applications. This is accomplished by performing the core functions of encryption, authentication, authorization, certificate management, and user access control. The module integrates a custom security application with hardware cryptographic acceleration. The application secures all communications using Transmission Control Protocol over Internet Protocol (TCP/IP). These include the three most commonly used communications protocols for power systems information exchange: Modbus, Distributed Network Protocol 3 (DNP3), and Smart Energy Profile 2.0 (SEP2.0). These three protocols are also supported by IEEE 1547-2018 for all DER devices. This paper tests the data encryption/decryption feature on a physical networking test bed with emulated Modbus devices reporting grid data and presents the results.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Software defined networking moving target defense honeypot

Disclosed herein are systems, methods, and storage media for thwarting cyber-attacks and data theft. A computing system receives packets and compares with a configuration resource. The computing system determines that the packet does not match the configuration resource and transmits a packet to a decoy environment via an SDN switch. The decoy environment is configured to generate time-out, service unavailable, or restricted access messages. In some embodiments, the computing system determines the packet does match the configuration and transmits the packet to a production network via an SDN switch. The SDN switch is communicatively connected to the decoy environment through a first channel and communicatively connected to a production environment through a separate second channel. The computing system is further configured to create and transmit to the SDN switch, rules to manage transmitting the packet to either the decoy environment or the production environment.

Lyle, Joshua A.↗