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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Low-Cost Multi-Modal Wireless Sensor Platform for Smart Buildings

Buildings consume 73% of the energy produced in the United States [1, 2]. Advanced sensors, controls, and communications have the potential to reduce the energy consumption of buildings by 20–40% [3, 4]. Currently, installation and wiring costs for sensors are quite high, making it cost-prohibitive for building managers to deploy large quantities of advanced sensors [5]. Wireless sensors have recently been deployed in buildings to provide the information necessary for optimal control of heating, ventilation, and air-conditioning and lighting systems [6, 7]. Wireless sensors have the unique advantage of being suitable for easily retrofitting existing buildings at a minimal labor cost, and the flexibility to be placed at optimal, observable locations. However, current commercially available wireless sensors are still very expensive ($150–300/node). Thus, revolutionary technological improvements in wireless sensors are required to promote inherently low-cost manufacturing for building applications to successfully exploit the energy efficiency opportunities in buildings.In this report, we present the efforts carried out at Oak Ridge National Laboratory and Molex Inc. toward exploring the design, implementation, and performance evaluation of low-cost, self-powered wireless sensors—including temperature, relative humidity, occupancy, and indoor air quality sensors—to enable advanced control applications that reduce energy consumption in buildings. Our focus is to demonstrate a novel multifunctional sensor platform that will evolve with advances in materials technology, low-cost printing techniques, nano-antennas, and co-integration of monitoring, control, and communication circuitry. We also present a low-power and bandwidth-efficient wireless communication technology that can be driven by energy harvesting in self-powered wireless sensors. It is based on code-phase-shift keying (CPSK) spread-spectrum signaling that improves both transmitter range and power consumption. The objective is to minimize the number of components within the wireless transceiver and reduce power consumption to allow sensor networks powered by energy harvesting, thus eliminating the expense and effort of periodically replacing the sensor batteries. Current state-of-the-art radio technologies have demonstrated 25–30 mA current consumption for the transmitter. Our prototype demonstrations lowered the consumption to 4 mA using spread-spectrum-based CPSK so that solar cells can be used with indoor lighting to recharge a battery indefinitely. Therefore, the system can run with no attention or maintenance for the life of the components, which is expected to be 20 years. The developed technology has the potential to generate fully printable wireless sensors with costs on the order of $1–10/node.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Snowflake: An Adaptive Energy and Delay Efficient Scheme for Source Location Privacy in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of a number of resource-constrained sensor nodes and a designated node called a sink, which collects data from the sensor nodes. A WSN can be used in numerous applications such as subject tracking and monitoring, where it is often desirable to keep the location of the subject private. In these types of applications, an adversary can locate the monitored subject, if a location privacy protection scheme is not applied. In this paper, we propose an adaptive energy and delay efficient scheme, called Snowflake, that conceals the location of subjects from a global adversary. Snowflake can be adapted to make the delivery delay smaller, or to make the packet overhead low. The simulation results show that Snowflake performs better than an existing algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Wireless sensor platform

A wireless sensor platform and methods of manufacture are provided. The platform involves providing a plurality of wireless sensors, where each of the sensors is fabricated on flexible substrates using printing techniques and low temperature curing. Each of the sensors can include planar sensor elements and planar antennas defined using the printing and curing. Further, each of the sensors can include a communications system configured to encode the data from the sensors into a spread spectrum code sequence that is transmitted to a central computer(s) for use in monitoring an area associated with the sensors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Wireless Sensor Network Technologies

Wireless sensor network (WSN) technologies have become a focus of development for instrumentation and control (I&C) applications in many critical infrastructure industries. Their implementation includes factories, electric plants and distribution systems, municipal water and sewer facilities, natural gas storage and distribution facilities, and even nuclear facilities. The promise of lower installation cost combined with reliable monitoring of process parameters at virtually any location, when needed, has driven many I&C vendors to offer WSN technology. These technologies are in their infancy and have not been fully tested for security, reliability, and compatibility when used in resilient control systems. The end user now has the difficult task of selecting which technology to use and how to implement a WSN. One of the key parameters in a WSN is sensor battery life. In a WSN some sensors pass on data from other sensors that do not have a direct communication path to the process controller. This extra work takes more power from the battery which decreases the battery life. The current work at CAES investigates how fast these batteries degrade compared to sensor batteries that do not have to process other sensor data. The result of this investigation will indicate how this effects the reliability of sensor networks.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Wireless Sensor Modalities at a Nuclear Plant Site to Collect Vibration Data

One of the major contributors to the total operating costs of domestic nuclear fleet of reactors today is the operation and maintenance (O&M) costs. These include labor-intense preventive maintenance programs involving manually-performed inspection, calibration, testing, and maintenance of plant assets at periodic frequency and time-based replacement of assets at periodic frequency, irrespective of their conditions. This has resulted in a labor-centric business model to achieve high capacity factors. To build an optimal maintenance program, it’s time to transition from this labor-centric business model to a technology-centric business model. Fortunately, there are technologies (advanced sensor, data analytics, and risk assessment methodologies) that will support this transition. The technology-centric business model will result in significant plant life extension and reduction of time-based maintenance activities. This will drive down O&M costs as labor is a rising cost and technology is a declining cost. This approach will lay the foundation for real-time condition assessment of plant assets, allowing condition-based maintenance to enhance plant safety, reliability, and economics of operation. The goal of this project is to address challenges in the area of digital monitoring, i.e., the application of advanced sensor technologies (particularly wireless sensor technologies) and science-based data analytic capabilities to advance online monitoring and predictive maintenance in nuclear plants to improve plant performance (efficiency gain and economic competitiveness). To achieve the project goal, in partnership with Exelon Generating Company (Exelon), researchers from Idaho National Laboratory (INL) and Oak Ridge National Laboratory (ORNL) are performing research and development (R&D) to demonstrate application of wireless sensors using the distributed antenna system and advanced data analytics to achieve predictive maintenance. In the report, wireless vibration sensors, vibration data and its indicator are described. The wireless vibration sensors presented in this report support three types of wireless communication, namely, Wi-Fi, cellular, and 900 MHz. These wireless vibration sensors are considered by partner plant site for installation on plant asset to enable online vibration monitoring to replace periodic measurements. These vibration data along with other plant process data will be utilized to develop diagnostic and prognostic models.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Autonomous management of a recursive area hierarchy for large scale wireless sensor networks using multiple parents

Large scale wireless sensor networks have been proposed for applications ranging from anomaly detection in an environment to vehicle tracking. Many of these applications require the networks to be distributed across a large geographic area while supporting three to five year network lifetimes. In order to support these requirements large scale wireless sensor networks of duty-cycled devices need a method of efficient and effective autonomous configuration/maintenance. This method should gracefully handle the synchronization tasks duty-cycled networks. Further, an effective configuration solution needs to recognize that in-network data aggregation and analysis presents significant benefits to wireless sensor network and should configure the network in a way such that said higher level functions benefit from the logically imposed structure. NOA, the proposed configuration and maintenance protocol, provides a multi-parent hierarchical logical structure for the network that reduces the synchronization workload. It also provides higher level functions with significant inherent benefits such as but not limited to: removing network divisions that are created by single-parent hierarchies, guarantees for when data will be compared in the hierarchy, and redundancies for communication as well as in-network data aggregation/analysis/storage.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Blockchain Secured Alternative to Mixed Routing / Non-Routing Nodes Wireless Sensor Network Topologies for Industrial Settings

Mesh networking of wireless sensors implies that individual sensor nodes will be able to communicate with neighboring nodes thereby forming a communication fabric. In the case of traditional wireless sensors, their radio frequency coverage – or RF footprint – is defined by the maximum separation distance between neighboring nodes while still achieving some level of, typically degrading from maximum, information transfer (throughput). Security issues arise in the situations where the sensor nodes are deployed near the physical boundary of an industrial site (edge nodes). In the more general case of the sensors relying on omnidirectional antennas, the edge nodes’ RF footprint may extend beyond the edge of the facility. If the edge nodes perform as routing nodes, then it may be possible for similar sensors/devices outside of the facility boundary to join the network. Here, while various schemes have been implemented to address this security issue, we report on the possibility of using blockchain for security of such edge nodes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Design Considerations for Secure Wireless Sensor Communication Systems in Harsh Electromagnetic Environments of Nuclear Reactor Facilities

Wireless sensors can potentially play a significant role in safety, efficiency, and reliability of the instrumentation and control process in current and next generation nuclear power reactors. While conventional narrowband wireless sensors have shown a certain level of success in some nuclear power plants (NPPs), the radio frequency (RF) propagation challenges posed by the heavy metallic and cluttered environment of NPPs has prevented their widespread use in such operations. Furthermore, these challenges include RF wave propagation in harsh (reflective, absorptive, cluttered) environments, data security issues, and RF interference to and from other devices in the vicinity of a nuclear reactor core. In this paper, first we address how ultrawideband (UWB) RF technology can complement the narrowband (i.e., WiFi) solutions that have been used in some NPPs by providing an alternative solution in addressing the signal propagation issues in such electromagnetically harsh environments. Second, we discuss and present the UWB software simulation results on multipath harsh environments, and then address the data security issues. In the final sections of the paper, we present the experimental results of using UWB signaling in a representative harsh environment conducted at the Massachusetts Institute of Technology research reactor site. We plan to develop the UWB communications hardware based on the results of this paper and report on its performance in the field with emphasis on the security aspects of the system in a subsequent paper.

42 ENGINEERING↗
DOE OSTI2024

Near-Field Passive Wireless Sensor for High-Temperature Metal Corrosion Monitoring

This work focuses on the fabrication and evaluation of a passive wireless sensor for the monitoring of the temperature and corrosion of a metal material at high temperatures. An inductor–capacitor (LC) resonator sensor was fabricated through the screen printing of Ag-based inks on dense polycrystalline Al 2 O 3 substrates. The LC design was modeled using the ANSYS HFSS modeling package, with the LC passive wireless sensors operating at frequencies from 70 to 100 MHz. The wireless response of the LC was interrogated and received by a radio frequency signal generator and spectrum analyzer at temperatures from 50 to 800 °C in real time. The corrosion kinetics of the Cu 110 was characterized through thermogravimetric (TGA) analysis and microscopy images, and the oxide thickness growth was then correlated to the wireless sensor signal under isothermal conditions at 800 °C. The results showed that the wireless signal was consistent with the corrosion kinetics and temperature, indicating that these two characteristics can be further deconvoluted in the future. In addition, the sensor also showed a magnitude- and frequency-dependent response to crack/spallation events in the oxide corrosion layer, permitting the in situ wireless identification of these catastrophic events on the metal surface at high temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

River Dissolved Oxygen Prediction Using Machine Learning Models and Wireless Sensor Measurements

Simultaneous flooding&heat and droughts&heat events can potentially destabilize hydro-meteorological conditions to deteriorate the water quality of Neches River. Machine learning (ML) models utilizing wireless sensor measurements have been applied to predict water quality and optimize various water management strategies. This study aims to develop ML models to predict dissolved oxygen (DO) prediction under various hydro-meteorological conditions and enhance water management decision-making. Wireless sensor measurements of DO, water temperature, sample depth, conductivity, turbidity, and pH, along with discharge from the United States Geological Survey stations, are collected for model inputs at the Pine Island Bayou C749 station (PIB-C749) and Neches River Saltwater Barrier (SWB). Multilayer perceptron neural networks, recurrent neural networks, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) with and without attention mechanism (AT) are tested to determine the best model, which is applied the rolling forecast method to predict 14-day DO. Traditional and recurrent transfer learning (TL and RTL) methods are adopted to overcome insufficient data at the SWB. The input feature importance analysis using the integrated gradients (IG) algorithm is applied to determine dominant inputs. The results show LSTM-based models are capable handling long sequential data. AT-BiLSTM and RTL-LSTM demonstrate the best performance at the PIB-C749 (RMSE=0.054) and the SWB (RMSE=0.028), respectively. TL and RTL methods significantly improve model performance at the SWB. DO, temperature, and pH show higher importance, consistent with hydrodynamics and water chemistry. Both best models are applied to predict 14-day DO and demonstrate reasonable performance for decision-making. Hydro-meteorological conditions of 2017 flood and 2012 drought events are simulated and reveal that possible hypoxia occurs after flooding due to increasing temperature and turbidity, and DO concentration decreases significantly under heat and drought conditions. In conclusion, LSTM-based models utilizing wireless sensor data can be a timely and effective approach to make appropriate decisions on water resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Controller-Based Energy-Aware Wireless Sensor Network Routing Using Quantum Algorithms

Energy-efficient routing in wireless sensor networks has attracted attention from researchers in both academia and industry, most recently motivated by the opportunity to use software-defined network-inspired approaches. These problems are NP-hard, with algorithms needing computation time that scales faster than polynomials in the problem size. Consequently, heuristic algorithms are used in practice, which are unable to guarantee optimally. In this article, we show proof-of-principle for the use of a quantum annealing processor instead of a classical processor, to find optimal or nearly optimal solutions very quickly. Our preliminary results for small networks show that this approach using quantum computing has great promise and may open the door for other significant improvements in the efficacy of network algorithms.

42 ENGINEERING↗
DOE OSTI2017

Path Finding for Maximum Value of Information in Multi-Modal Underwater Wireless Sensor Networks

Here, we consider underwater multi-modal wireless sensor networks (UWSNs) suitable for applications on submarine surveillance and monitoring, where nodes offload data to a mobile autonomous underwater vehicle (AUV) via optical technology, and coordinate using acoustic communication. Sensed data are associated with a value, decaying in time. In this scenario, we address the problem of finding the path of the AUV so that the Value of Information (VoI) of the data delivered to a sink on the surface is maximized. We define a Greedy and Adaptive AUV Path-finding (GAAP) heuristic that drives the AUV to collect data from nodes depending on the VoI of their data. For benchmarking the performance of AUV path-finding heuristics, we define an integer linear programming (ILP) formulation that accurately models the considered scenario, deriving a path that drives the AUV to collect and deliver data with the maximum VoI. In our experiments GAAP consistently delivers more than 80 percent of the theoretical maximum VoI determined by the ILP model. We also compare the performance of GAAP with that of other strategies for driving the AUV among sensing nodes, namely, random paths, TSP-based paths and a “lawn mower”-like strategy. Our results show that GAAP always outperforms every other heuristic in terms of delivered VoI, also obtaining higher energy efficiency.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Hamilton: Flexible, Open Source $10 Wireless Sensor System for Energy Efficient Building Operation

Sensors for improving building performance are rapidly populating the market, driven in part by the drive to reduce greenhouse gas emissions resulting from energy production as well as improve the interior environment for healthy and more productive spaces. UC Berkeley has led wireless sensor development over the past 25 years (e.g., Telos mote), with the Hamilton (named after Alexander Hamilton on the US $10 bill) as the most recent. The Hamilton sensor was designed as a low-cost high-performance sensor that is modular and interoperable. The objective of the Hamilton project was to create, evaluate and establish the technological foundations for secure and easy to deploy building energy efficiency applications utilizing pervasive, low-cost wireless sensors integrated with traditional Building Management Systems (BMS), consumer-sector building components, and powerful data analytics. The project included iterative hardware design, incorporating a high-performance database (BTrDb, http://btrdb.io/), creating and iterating the development of secure data middleware (BOSSwave, WAVE/WAVEMQ), working with and pushing the development of an open-source tiny operating system RiotOS, and implementing and improving protocols such as Thread/OpenThread and TCP/IP. The hardware benefited from careful design to drive down the cost; the design included a System-on-a-Chip (SoC), chip antenna, single crystal and five passive components. Careful design of the operating system created a low-power design to enable a long life with small batteries. The hardware included several sensors: temperature, radiant temperature, relative humidity, magnetometer, accelerometer, and light, with an optional occupancy (Passive InfraRed) sensor. The project was the basis of several applications, both internal to the research team and other researchers and professionals at other institutions. Several applications used the sensor hardware as the basis for other complex devices. Other applications used the sensors to improve building performance through interoperating with the building Heating Ventilation and Air-Conditioning (HVAC) system, such as using occupancy and/or distributed temperature sensing to reduce HVAC zone energy while still providing thermal comfort and to reduce peak loads in small commercial buildings. We demonstrated cloud-based energy analytics, implemented a schedule and a Model Predictive Controller in a small commercial building to optimize HVAC energy, occupancy and electricity price. Initial integration of these technological innovations was performed through the creation of execution containers containing the WAVE agent and various driver, proxy, or building system function logic. The research added to the understanding of efficient sensor hardware, secure middleware, time-series data management (high performance database), efficient communication protocols, and interoperating with applications and building systems. The project showed the technical effectiveness and economic feasibility of creating a low-cost, modular, and easy-to-deploy sensor. Through conversations with multiple end users, the research team discovered that many customers wanted data management and services in addition to the sensors. HamiltonIOT developed packages of sensors, border router, and data services to provide a seamless “plug-and-play” sensor deployment. Some customers were willing to pay for higher quality sensors (such as light); some customers wanted a robust enclosure (waterproof).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

SELF-POWERED WIRELESS SENSOR NODE POWER MODELING BASED ON IEEE 802.11 COMMUNICATION PROTOCOL

Design and technical advancements in sensing, processing, and wireless communication capabilities of small, portable devices known as wireless sensor nodes (WSNs) have drawn extensive research attention and are vastly applied in science and engineering applications. The WSNs are typically powered by a chemical battery source that has a load dependent finite lifetime. Most applications, including the nuclear industry applications, require WSNs to operate for an extended period of time beginning with their deployment. To ensure longevity, it is important to develop self-powered WSNs. The benefit of self-powered WSNs goes far beyond the cost savings of removing the need for cable installation and maintenance. Self-powered WSNs will potentially offer significant expansion in remote monitoring of nuclear facilities, and provide important data on plant equipment and component status during normal operation, as well as in case of abnormal operation, station blackouts or post-accident evaluation. Advancements in power harvesting technologies enable electric energy generation from many sources, including kinetic, thermal, and radiated energy. For the ongoing research at Idaho National Laboratory, a solid-state thermoelectric-based technology, the thermoelectric generator (TEG), is used to convert thermal energy to power a WSN. The design and development of TEGs to power WSNs that would remain active for a long period of time requires comprehensive understanding of WSN operational. This motivates the research in modeling the lifetime, i.e., power consumption, of a WSN by taking into consideration various node and network level activities. A WSN must perform three essential tasks: sense events, perform quick local information processing of sensed events, and wirelessly exchange locally processed data with the base station or with other WSNs in the network. Each task has a power cost per unit tine and an additional cost when switching between tasks. There are number of other considerations that must also be taken into account when computing the power consumption associated with each task. The considerations includes: number of events occurring in a fixed active time period and the duration of each event, event-information processing time, total communication time, number of retransmission, etc. Additionally, at the network level the communication of information data packets between WSNs involves collisions, latency, andretransmission, which result in unanticipated power losses. This paper presents stochastic modeling of power demand for a schedule-driven WSN utilizing Institute of Electrical and Electronics Engineers, IEEE, 802.11 communication protocols. The model captures the generic operation of a schedule-driven WSN when an external event occurs, i.e., sensing, following by processing, and followed by communication. The results are verified via simulation.

IEEE 802.11 Protocol↗
DOE OSTI2018

Energy-Harvesting Based Wireless Sensor Using LoRa Protocol

This software can be applied with an integrated wireless sensor node that can sense environmental variables, process the variables, and transmit using the LoRa protocol. The software accompanies an embedded circuit layout that has sensing, processing, and communication circuitry. The software effectively retrieves data from a sensor node, processes them and transmits over the air while conserving on board energy.

Kuruganti, Phani Teja V↗
DOE OSTI2017

On Efficient Deployment of Wireless Sensors for Coverage and Connectivity in Constrained 3D Space

Sensor networks have been used in a rapidly increasing number of applications in many fields. This work generalizes a sensor deployment problem to place a minimum set of wireless sensors at candidate locations in constrained 3D space to k-cover a given set of target objects. By exhausting the combinations of discreteness/continuousness constraints on either sensor locations or target objects, we formulate four classes of sensor deployment problems in 3D space: deploy sensors at Discrete/Continuous Locations (D/CL) to cover Discrete/Continuous Targets (D/CT). We begin with the design of an approximate algorithm for DLDT and then reduce DLCT, CLDT, and CLCT to DLDT by discretizing continuous sensor locations or target objects into a set of divisions without sacrificing sensing precision. Furthermore, we consider a connected version of each problem where the deployed sensors must form a connected network, and design an approximation algorithm to minimize the number of deployed sensors with connectivity guarantee. For performance comparison, we design and implement an optimal solution and a genetic algorithm (GA)-based approach. Extensive simulation results show that the proposed deployment algorithms consistently outperform the GA-based heuristic and achieve a close-to-optimal performance in small-scale problem instances and a significantly superior overall performance than the theoretical upper bound.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Wireless Sensor Networks for Improved Snow Water Equivalent and Runoff Estimates

We leverage the frontiers of the Internet of Things technology in a recently developed end-to-end wireless sensor network (WSN) system that samples, collects, stores, and displays mountain hydrology measurements in near real-time. At the core of the system lies an ultra-low power, radio channel-hoping, and self-organizing mesh that allows for remote autonomous sampling of snow. Such properties, combined with a rugged weather-sealed design of the devices and multi-level data replication, provides reliable real-time data at spatial and temporal scales previously impractical to achieve in mountain environments. The system was deployed at three 1 km 2 sites across the North Fork of the Feather River basin with a cluster of 12 sensor nodes for each location. Measurements show that existing operational autonomous systems are non-representative spatially, with biases that can reach up to 50%. A comparison between a wet and dry year showed that snow depths exhibit strong multi-scale inter-year spatial stationarity with major rank conservation. Temporally dense analysis using elastic net regression shows that dominant features at the sub-km 2 scale are site-dependent and differ from the watershed scale. Newly introduced explanatory variables, based on the nearest neighbor with a Landsat assimilated historical product, consistently explained up to 90% of the variance in the watershed-scale SWE for both years. At two WSN sites, lagged cross-correlation of snowmelt with stream flow measurements showed a significant improvement of up to 100% compared with existing systems, suggesting that WSNs can be instrumental in improving runoff forecasting and water management.

47 OTHER INSTRUMENTATION↗