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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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47 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

The Lighter Side of Things: The Inevitable Convergence of the Internet of Things and Cybersecurity

By the year 2020 it is estimated that there will be more than 50 billion devices connected to the Internet. These devices not only include traditional electronics such as smartphones and other mobile compute devices, but also eEnabled technologies such as cars, airplanes and smartgrids. The IoT brings with it the promise of efficiency, greater remote management of industrial processes and further opens the doors to world of vehicle autonomy. However, IoT enabled technology will have to operate and contend in the contested domain of cyberspace. This discussion will touch on the impact that cybersecurity has on IoT and the people, processes and technology required to mitigate cyber risks.

Internet of Things↗
DOE OSTI2025

Resilience of the Electric Grid Through Trustable IoT-Coordinated Assets

The electricity grid has evolved from a physical system to a cyberphysical system with digital devices that perform measurement, control, communication, computation, and actuation. The increased penetration of distributed energy resources (DERs) including renewable generation, flexible loads, and storage provides extraordinary opportunities for improvements in efficiency and sustainability. However, they can introduce new vulnerabilities in the form of cyberattacks, which can cause significant challenges in ensuring grid resilience. We propose a framework in this paper for achieving grid resilience through suitably coordinated assets including a network of Internet of Things devices. A local electricity market is proposed to identify trustable assets and carry out this coordination. Situational Awareness (SA) of locally available DERs with the ability to inject power or reduce consumption is enabled by the market, together with a monitoring procedure for their trustability and commitment. With this SA, we show that a variety of cyberattacks can be mitigated using local trustable resources without stressing the bulk grid. Multiple demonstrations are carried out using a high-fidelity cosimulation platform, real-time hardware-in-the-loop validation, and a utility-friendly simulator.

distributed energy resources↗
DOE OSTI2024

TrustDER: Trusted, Private and Scalable Coordination of Distributed Energy Resources

In this project, the Stanford and SLAC Teams have developed a Trusted, Private and Scalable platform for coordinating Coordination of Distributed Energy Resources (TrustDER). This is a layered system that ensures private, trusted and scalable coordination and monitoring of DERs. It accommodates a variety of resources, such as solar generation, gensets and loads, with a particular focus on battery systems-based resources, as they are a transformational technology experiencing fast growth in adoption by large critical facilities. The platform can be used as standalone or added to existing aggregation systems to enable trust, privacy and resilience. TrustDER consists of layers that address each of the shortcomings of the existing state of the art. Each layer in the platform can operate independently but provides information to the layers above it to enable a novel form of overall coordination architecture. The project consists of several tasks, with each task dedicated to the design of each layer. Task 2 Resource Virtualization defined a software abstraction layer for distributed energy resources (DERs). The goal of this abstraction was to simplify the implementation of algorithms utilizing cooperation of DERs resources in a variety of use cases. Task 3 is on Secure ID for Asset Authentication. Identity Management Systems (IDMS) are a foundational infrastructure for interactions between entities (organizations, users, devices, and services). Secure ID is blockchain-based a distributed identity management system allowing (1) identity provisioning, (2) authentication, (3) authorization, and (4) identity data sharing for IoT-enabled assets on the electricity grid. In this project, the SLAC team focused on designing and testing Keymaker, a protocol for authenticating device identity managed by Secure ID. Task 5 Private and Safe Integration is focused on the design and evaluation of a DER cooperation scheme which allows for the aggregation of DERs without impacting network reliability. The approach is designed based on realistic assumptions regarding data availability, communication infrastructure limitations, and privacy. Task 6 Scalable Distributed Privacy for Information explored how virtualized batteries could be managed privately. Specifically, it examined the case in which a principal provides a partitioned battery to multiple clients. Task 7 Use Cases was to ensure that this technology was applied in relevant situations and scenarios. Primarily, this means that virtualization needed to be employed in a manner that either improved flexibility, bolstered security or privacy, or decreased costs.

25 ENERGY STORAGE↗
DOE OSTI2025

The influence of ambient light spectrum (LED, INC, CFL, Xenon…) on the efficiency of perovskite indoor photovoltaic solar cells for Internet of Things (IoT) applications

This record provides a numerical investigation of a MAGeI₃-based perovskite solar cell with the structure FTO/TiO₂/MAGeI₃/Spiro-OMeTAD, evaluated for both outdoor and indoor light-harvesting applications using the SCAPS-1D simulator. Device performance is analyzed under AM 1.5G sunlight and several artificial light sources, including LED, incandescent, compact fluorescent lamp (CFL), flashlight, and xenon illumination. The study reports initial and optimized power conversion efficiencies and examines the influence of absorber layer thickness and bandgap on device performance under different lighting conditions. The results highlight the potential of MAGeI₃-based perovskite solar cells for indoor energy harvesting and low-power Internet of Things (IoT) applications.

14 SOLAR ENERGY↗
DOE OSTI2025

Enhancing Smart Home Privacy: A Tutorial on Local Differential Privacy Techniques for Frequency and Mean Estimation

The ubiquity of Internet of Things (IoT) systems has seamlessly integrated into our daily lives, particularly in smart homes where devices continuously monitor and optimize our living environments. These systems significantly contribute to home automation, energy efficiency, and overall comfort. However, this widespread connectivity poses inherent risks linked to the streaming of sensitive household data, necessitating robust privacy preservation mechanisms. This tutorial systematically examines privacy preservation through local differential privacy (LDP), with a particular focus on frequency and mean estimation techniques for smart home applications. Here, we present a comprehensive taxonomy of smart home data formats and provide detailed implementation guidance for event-based and w-event LDP mechanisms. Through practical examples using smart thermostats and HVAC systems, we demonstrate how these techniques can be effectively deployed in real-world scenarios. The tutorial concludes by examining emerging research directions, including adaptive privacy budgets and federated learning approaches, establishing a foundation for privacy-preserving smart home deployments.

Kotevska, Olivera [Oak Ridge National Laboratory (↗
DOE OSTI2026

Generalizable Web User Interface for Scalable and Streamlined Deployment of Building Energy Management Systems in Small and Medium-Sized Commercial Buildings

Small and medium-sized commercial buildings (SMCBs) comprise 94% of US commercial buildings yet face significant barriers to implementing building energy management systems despite advances in smart device technology. Existing solutions present critical limitations: cloud-based API solutions simplify deployment but create vendor lock-in constraints; commercial integrated software solutions ensure compatibility via standardized protocols but require substantial cost and technical expertise; open-source IoT platforms offer cost-effective vendor independence but provide insufficient standardized protocol support for commercial building automation. This research presents a generalizable web user interface framework that bridges the gap between evolving smart device capabilities and lagging software infrastructure for SMCBs. The proposed system integrates VOLTTRON open-source middleware with an automated configuration converter that transforms unified specifications written in YAML, a human-readable data-serialization format, into system-specific files, streamlining manual setup processes. The vendor-agnostic architecture supports industry-standard protocols (BACnet and Modbus) and semantic building models while providing adaptive web interfaces that dynamically adjust to various building configurations. Demonstrations through simulation-based testing and a field deployment show automatic interface adaptation across heterogeneous HVAC systems and multizone monitoring. The automated configuration converter also substantially reduces labor-intensive setup.

Chung, Jihoon [ORNL] (ORCID:0000000184880815)↗
DOE OSTI2026

Simulation-Based Validation of An Open-Source, Scalable Framework for Building Energy Management in Small and Medium-Sized Commercial Buildings

Abstract: Small and medium-sized commercial buildings (SMCBs) represent 94% of U.S. commercial buildings but encounter substantial obstacles in adopting Building Energy Management (BEM) systems. Current approaches exhibit fundamental limitations: vendor-specific API platforms restrict interoperability through proprietary ecosystems; commercial automation software demands extensive technical expertise and licensing costs; open-source IoT solutions lack native support for building automation protocols and semantic models. This paper introduces a configuration-driven web interface framework addressing the gap between smart device advancements and accessible BEM software infrastructure for SMCBs. The framework leverages VOLTTRON middleware integrated with an automated converter that processes unified YAML configurations into heterogeneous system files, reducing required configuration artifacts from six separate files to a single unified specification. The system architecture enables vendor-agnostic operation through BACnet and Modbus protocols while supporting semantic building model integration via automated Brick Schema parsing. Configuration-driven interfaces automatically adapt to diverse HVAC types without custom development. Simulation-based validation using BOPTEST demonstrates automatic interface generation between fan coil and hydronic systems, with the automated converter successfully generating all platform-specific outputs from the single YAML input. The result demonstrates the framework's capability to streamline BEM system deployment through reduced configuration complexity. This work bridges simulation capabilities with operational deployment, demonstrating how virtual testbeds validate generalizable software frameworks for real-world building automation.

Chung, Jihoon [ORNL] (ORCID:0000000184880815)↗
DOE OSTI2024

Metal Oxide vs Organic Semiconductor Charge Extraction Layers for Halide Perovskite Indoor Photovoltaics

Halide perovskite indoor photovoltaics (PVs) are highly promising to autonomously power the billions of microelectronic sensors in the emerging and disruptive technology of the Internet of Things (IoT). However, how the wide range of different types of hole extraction layers (HELs) impacts the indoor light harvesting of perovskite solar cells is still elusive, which hinders the material selection and industrial–scale fabrication of indoor perovskite photovoltaics. In the present study, new insights are provided regarding the judicial selection of HELs at the buried interface of halide perovskite indoor photovoltaics. This study unravels the detrimental and severe light–soaking effect of metal oxide transport layer–based PV devices under the indoor lighting effect for the first time, which then necessitates the interface passivation/engineering for their reliant performance. This is not a stringent criterion under 1 sun illumination. By systematically investigating the charge carrier dynamics and sequence of measurements from dark, light–soaked, interlayer–passivated device, the bulk and interface defects are decoupled and reveal the gradual defect passivation from shallow to deep level traps. Thus, the present study puts forward a useful design strategy to overcome the deleterious effect of metal oxide HELs and employ them in halide perovskite indoor PVs.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

Dynamic Anomaly Response and Integrated Analysis (DARIA): A Fault Investigation Toolset Supporting Earth-Independent Operations in Future Crewed Mars Missions

NASA's Moon to Mars Objectives outline a strategic vision for human spaceflight culminating in crewed missions to Mars. A critical component of this objective is the development of systems that are capable of being Earth-Independent Operated (EIO). A key aspect of EIO is the ability to rapidly detect, diagnose, and respond to anomalies in crew-supporting habitat and connected systems. To address this, the Dynamic Anomaly Response and Integrated Analysis (DARIA) architecture has been developed. DARIA incorporates a network of compact, wireless sensing devices called the System for Telemetry Amalgamation of Multimodal PrognosticS (STAMPS) for increased state awareness of EIO habitats. Able to perform on-the-fly data acquisition, STAMPS are connected to integrated anomaly data dashboards for crew visualization. DARIA is designed to seamlessly integrate into various off-world environments, including the International Space Station, Lunar Gateway, Artemis Base Camp, and future Mars habitats. By leveraging Commercial Off-The-Shelf (COTS) hardware, the NASA Internet of Things (NASA IoT) framework, and EIO fault detection methods, DARIA provides a cost-effective and adaptable solution for anomaly detection and response in EIO habitats.

Diagnostics↗
DOE OSTI2025

High efficiency RF sources developments

Calabazas Creek Research, Inc. (CCR) and its collaborators are developing high efficiency RF sources operating from a few hundred MHz to C-Band and power levels from tens to hundreds of kilowatts with the goal of providing MW-relevant sources. The efficiencies approach or exceed 80% with projected costs as low as $0.50/ Watt. Sources under development include magnetrons with phase and amplitude control, single and multi-beam klystrons, multi-beam power grid tubes, and multiple beam IOTs. A magnetron system achieved more than 80% efficiency with fast amplitude control using modulation of the phase locking signal. This would be a low cost, high efficiency RF source for superconducting accelerators. An L-Band, single beam klystron was built with simulated efficiency of 80%. The klystron has yet to be tested to confirm the simulation results. CCR is currently developing a multi-beam klystron to produce more than 200 kW CW at 80% efficiency. Also in development is a multiple beam triode to produce 200 kW CW from 300 MHz to approximately 1 GHz. Not only does the simulated efficiency exceed 75%, but it would be the lowest cost RF source in this frequency range. Finally, CCR recently concluded research for a multiple beam IOT at 700 MHz using third harmonic drive to boost efficiency toward 85%. Successful development and transition to production of these sources will significantly alter the cost/performance landscape for RF power generation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

TRIM: AI Guided Random Number Generation for Resource-Constrained IoT Systems

Random numbers often serve as the backbone for many security solutions in diverse domains such as cryptography, side channel leakage prevention, and moving target defense. However, generating true random numbers requires a physical source of entropy (e.g. hardware, quantum, environmental phenomenon) making it difficult to realize at a large scale and at a low cost. On the flip side, pseudorandom number generators (easy to implement) following a specific distribution (e.g. Gaussian) can be easily compromised given a sufficient amount of traces. In this work, we have developed a machine learning-guided generative approach that can be used to create portable, resource-efficient, and cost-effective random number generators with high throughput and true randomness characteristics. We implement the proposed approach as a highly parameterized framework and perform extensive evaluation for different settings. The framework was able to learn from true random sources such as irrational numbers and environmental audio noise and imitate those sources towards generating new good quality random numbers on demand. We have generated more than 1 billion bits and observed robust performance in terms of true randomness metrics obtained from NIST SP 800-22 and FIPS 140-1 randomness test suites achieving a throughput of up to 142.85 Mbps. Compared to the state-of-the-art (SOTA) technique, the iso-cost setup of our framework can achieve more than 500 Mbps in a distributed setting. We have evaluated the efficacy of running the true randomness imitation AI models on target edge devices such as Raspberry Pi 4 (Model B), Nvidia Jetson Nano, Nvidia Jetson Orin Nano and Nvidia Jetson Xavier. We have also looked at the security of the TRIM framework itself against different adversarial threat models.

Cybersecurity↗