Collaborative Allocation and Optimization of Path Planning for Static and Mobile Sensors in Hybrid Sensor Networks for Environment Monitoring and Anomaly Search
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Yanjie Guo↗
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Results for “Network monitoring”
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A Self-Synchronizing Underwater Acoustic Network for Mooring Load Monitoring of a Wave Energy Converter
This paper reports on the development of a self-synchronizing underwater acoustic network developed for remote monitoring of mooring loads in Wave Energy Converters (WECs). This network uses Time Division Multiple Access and operates self-contained with the ability for users to remotely transmit commands to the network as needed. Each node is a self-contained unit, consisting of a protocol adaptor board, an FAU-DPAM underwater acoustic modem and a battery pack. A node can be connected to a load cell, to a topside user or to the WEC. Every node is swapable. The protocol adaptor board, named Protocol Adaptor for Digital LOad Cell (PADLOC) supports a variety of digital load cell message formats (CAN, MODBUS, custom ASCII) and underwater acoustic modem serial formats. PADLOC enables topside users to connect to separate load cells through a user-specific command. This is especially important if the user is monitoring multiple load cells during deployment or maintenance, when the primary data system may be offline. Each PADLOC board handles formatting, buffering and has a one-on-one serial connection with each pair (node) of a digital load cell and acoustic modem. In addition, each PADLOC board handles the timekeeping and power saving features for each node. The only limitation is the data bit rate and delay limitations associated with the underwater acoustic modem. A four node self-synchronizing network has been developed to demonstrate the load cell monitoring capability using the PADLOC technology on the CalWave WEC.
acoustic↗
Neural network approach to Space Shuttle Main Engine health monitoring
A neural network was trained to distinguish anomalies in Space Shuttle Main Engine sensor data from noisy normal steady-state sensor data. Power spectra of successive windows of individual sensor data were presented to a neural network using Kohonen's topological feature map training algorithm. The trained network for each sensor was then tested to determine if it would detect anomalies in the sensor data, and if so, the time at which the anomaly would be detected. Power spectra from a few hundred seconds of actual test data from NASA tests 901-364 and 904-044 were used to test the network. In both cases, the neural network detected the onset of anomalous engine behavior at approximately the same time within each test as the onset times reported by NASA and Rocketdyne experts in their post-test analyses.
Whitehead, Bruce A.↗
Systems and methods for monitoring traffic on industrial control and building automation system networks
Technologies relating to monitoring communications traffic to detect potential attacks on industrial control system networks and building automation system networks are described herein. In an embodiment, a monitoring device receives a plurality of communications from a control network. The monitoring device transmits the communications to a computing device. Based on the communications, the computing device generates a listing of devices that communicated by way of the control network over a period of time, and computes a volume of traffic between each pair of devices in the listing of devices. The computing device then outputs a graphical user interface (GUI) by way of display, the GUI comprising data indicative of the computed volumes of traffic, which may be indicative of a potential attack on the control network.
Jenkins, Chris↗
2019 Groundwater Monitoring Report Project Shoal Area: Subsurface Corrective Action Unit 447
The Project Shoal Area is a site in Nevada where an underground nuclear test was conducted in 1963. It later came to be known as the Shoal, Nevada, Site. Surface contamination at the site has been remediated, but investigation of groundwater contamination resulting from the test is still in the corrective action process. Annual sampling and water-level monitoring are conducted as part of the subsurface corrective action strategy, which has focused on revising the site conceptual model and evaluating the adequacy of the monitoring well network. It has also included enhancements to the monitoring well network to address uncertainties in the groundwater flow direction and the cause of rising water levels in site wells west of the shear zone since the first hydrologic characterization (HC) wells were installed in 1996. Revisions to the site conceptual model and enhancements to the monitoring strategy were provided to Nevada Division of Environmental Protection (NDEP) in the Addendum to: Corrective Action Decision Document/Corrective Action Plan (CADD/CAP) for the Subsurface Corrective Action Unit 447 Shoal, Nevada, Site. NDEP approved the addendum to the CADD/CAP, which included an expanded contaminant boundary and compliance boundary for the site.
54 ENVIRONMENTAL SCIENCES↗
Wilson Corners, Solid Waste Management Unit 001(SWMU 01) 2023 Annual Long-Term Monitoring Report
This report presents a summary of the long-term monitoring (LTM) activities that occurred in 2023 at Wilson Corners, Solid Waste Management Unit 001, at Kennedy Space Center (KSC), Florida. Annual LTM of groundwater is being conducted at the site. Based on results from groundwater sampling activities performed during the 2019 through 2020 LTM reporting period and the 2020 and 2021 DPT groundwater sampling, it was determined that the LTM sampling plan was no longer meeting the goal of LTM because delineation was not verified and the installation of an air sparge (AS) system to treat the area of the High Concentration Plume was recommended. The AS System was installed in late 2022 and early 2023. System start-up activities were initiated in April 2023. Following system startup, several site wells required retrofitting to equip wellheads for withstanding the air pressure released from air sparge wells during system operation. Some site wells also required repair or abandonment, and replacement. Survey of location and top-of-casing of newly installed monitoring wells was combined with scheduled AS system survey activities and was completed in January 2024. The activities presented in this report include the February and April 2023 LTM monitoring well installations; March and April 2023 LTM and performance monitoring well water level gauging and sampling; November 2023 LTM well retrofits and repairs; a summary of December 2023 LTM well abandonments and installations (complete site well abandonment activities will be presented under a separate cover); and January 2024 LTM well survey. During the March and April 2023 sampling events, the low-flow sampling method was used, and samples were analyzed for a select list of volatile organic compounds. In March 2023, groundwater flow for the site was generally to the west was generally consistent with historical observations at the site. The Low Concentration Plume (LCP) continues to extend both horizontally and vertically beyond the terminal depth of the current monitoring well network. Data, inclusive of the 2023 LTM and baseline performance monitoring sampling events, indicate that the LCP encompasses an estimated 19.5 acres, compared to the 2021 LCP footprint, inclusive of the 2020 and 2021 DPT sampling events of 20.7 acres. The vertical extent of VOCs was historically delineated by monitoring wells screened greater than 48 feet below land surface (bls). The results from the three vertical extent monitoring wells screened below 48 feet bls that were sampled during the 2023 LTM indicate that groundwater vinyl chloride concentrations in these three wells are greater than the GCTL. As presented in the 2021 Long-Term Monitoring Report (NASA 2022), the KSCRT agreed to delay deeper investigations in this area to prevent the creation of additional pathways for vertical migration. Based on groundwater sampling activities performed in 2023, recommendations are to perform the next annual LTM sampling event, scheduled for April 2024 and to conduct quarterly performance monitoring of the AS System. The current selection of monitoring wells in the recommended 2024 LTM plan will provide an adequate data set for monitoring groundwater plume behavior; however, the LTM monitoring well network will be evaluated and refined based on 2024 LTM and year one performance monitoring data.
King Linnea↗
A neural network architecture for implementation of expert systems for real time monitoring
Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.
Ramamoorthy, P. A.↗
Detecting anomalous packets in network transfers: investigations using PCA, autoencoder and isolation forest in TCP
Large-scale scientific workflows rely heavily on high-performance file transfers. These transfers require strict quality parameters such as guaranteed bandwidth, no packet loss or data duplication. To have successful file transfers, methods such as predetermined thresholds and statistical analysis need to be done to determine abnormal patterns. Network administrators routinely monitor and analyze network data for diagnosing and alleviating these, making decisions based on their experience. However, as networks grow and become complex, monitoring large data files and quickly processing them, makes it improbable to identify errors and rectify these. Abnormal file transfers have been classified by simply setting alert thresholds, via tools such as PerfSonar and TCP statistics (Tstat). This paper investigates the feasibility of unsupervised feature extraction methods for identifying network anomaly patterns with three unsupervised classification methods—principal component analysis, autoencoder and isolation forest. Here, we collect file transfer statistics from two experiment sets—synthetic iPerf generated traffic and 1000 Genome workflow runs, with synthetically introduced anomalies. Our results show that while PCA and a simple autoencoder finds it difficult to detect clusters, the tree-variant isolation forest is able to identify anomalous packets by breaking down TCP traces into tree classes early.
97 MATHEMATICS AND COMPUTING↗
Multi-purpose time analyzer and monitor for Deep Space Network time synchronization
We report the development of a multi-purpose, automated, and continuously operating Time Analyzer to measure and monitor distributed 1 pps reference signals in the NASA Deep Space Network (DSN).
time interval time stability↗
Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter
The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system.
43 PARTICLE ACCELERATORS↗
A digital data quality monitor for application in the Nascom network data transmission system
Digital data quality monitor for Nascom network data transmission system applications
Herr, D. W.↗
An overview of the Mark 4A monitor and control system
The Deep Space Network (DSN) Monitor and Control System was completely changed during the conversion from Mark 3 to the Mark 4A configuration. The configuration employs shared data processing equipment between several co-located antennas, and incorporates much greater centralization of operations functions. The configuration is described and its performance is compared to that of the Mark 3 era.
Leflang, J. G.↗
Watchmen 3.0.0 Users Guide: Revision 6
Watchmen is a research software application developed by Pacific Northwest National Laboratory (PNNL) that incorporates the scientific and operational expertise for reviewing data from treaty monitoring radionuclide stations. The radionuclide stations are part of the International Monitoring System (IMS), a worldwide network to monitor for nuclear explosions. The data the IMS produces are critical to determine if a radionuclide release event is from a nuclear explosion. Stations deliver their measurements and system status to the International Data Centre (IDC), which forwards it via email to all subscribers. Watchmen is capable of processing data from several radioxenon station types.
97 MATHEMATICS AND COMPUTING↗
Watchmen 3.1.0 Users Guide: Revision 7
Watchmen is a research software application developed by Pacific Northwest National Laboratory (PNNL) that incorporates the scientific and operational expertise for reviewing data from treaty monitoring radionuclide stations. The radionuclide stations are part of the International Monitoring System (IMS), a worldwide network to monitor for nuclear explosions. The data the IMS produces are critical to determine if a radionuclide release event is from a nuclear explosion. Stations deliver their measurements and system status to the International Data Centre (IDC), which forwards it via email to all subscribers. Watchmen is capable of processing data from several radioxenon station types.
97 MATHEMATICS AND COMPUTING↗
Watchmen v3.2.0 User Guide: Revision 8
Watchmen is a research software application developed by Pacific Northwest National Laboratory (PNNL) that incorporates the scientific and operational expertise for reviewing data from treaty monitoring radionuclide stations. The radionuclide stations are part of the International Monitoring System (IMS), a worldwide network to monitor for nuclear explosions. The data the IMS produces are critical to determine if a radionuclide release event is from a nuclear explosion. Stations deliver their measurements and system status to the International Data Centre (IDC), which forwards it via email to all subscribers. Watchmen is capable of processing data from several radioxenon station types.
97 MATHEMATICS AND COMPUTING↗
Watchmen 3.3.0 User Guide: Revision 9
Watchmen is a research software application developed by Pacific Northwest National Laboratory (PNNL) that incorporates the scientific and operational expertise for reviewing data from treaty monitoring radionuclide stations. The radionuclide stations are part of the International Monitoring System (IMS), a worldwide network to monitor for nuclear explosions. The data the IMS produces are critical to determine if a radionuclide release event is from a nuclear explosion. Stations deliver their measurements and system status to the International Data Centre (IDC), which forwards it via email to all subscribers. Watchmen is capable of processing data from several radioxenon station types.
97 MATHEMATICS AND COMPUTING↗
Automated monitor and control for deep space network subsystems
The problem of automating monitor and control loops for Deep Space Network (DSN) subsystems is considered and an overview of currently available automation techniques is given. The use of standard numerical models, knowledge-based systems, and neural networks is considered. It is argued that none of these techniques alone possess sufficient generality to deal with the demands imposed by the DSN environment. However, it is shown that schemes that integrate the better aspects of each approach and are referenced to a formal system model show considerable promise, although such an integrated technology is not yet available for implementation. Frequent reference is made to the receiver subsystem since this work was largely motivated by experience in developing an automated monitor and control loop for the advanced receiver.
Smyth, P.↗
NCC Simulation Model: Simulating the operations of the network control center, phase 2
The simulation of the network control center (NCC) is in the second phase of development. This phase seeks to further develop the work performed in phase one. Phase one concentrated on the computer systems and interconnecting network. The focus of phase two will be the implementation of the network message dialogues and the resources controlled by the NCC. These resources are requested, initiated, monitored and analyzed via network messages. In the NCC network messages are presented in the form of packets that are routed across the network. These packets are generated, encoded, decoded and processed by the network host processors that generate and service the message traffic on the network that connects these hosts. As a result, the message traffic is used to characterize the work done by the NCC and the connected network. Phase one of the model development represented the NCC as a network of bi-directional single server queues and message generating sources. The generators represented the external segment processors. The served based queues represented the host processors. The NCC model consists of the internal and external processors which generate message traffic on the network that links these hosts. To fully realize the objective of phase two it is necessary to identify and model the processes in each internal processor. These processes live in the operating system of the internal host computers and handle tasks such as high speed message exchanging, ISN and NFE interface, event monitoring, network monitoring, and message logging. Inter process communication is achieved through the operating system facilities. The overall performance of the host is determined by its ability to service messages generated by both internal and external processors.
Benjamin, Norman M.↗