Spatio-temporal Whitening of Imaging Sensor Data Streams Using Three-Dimensional Linear Prediction

2008 ◽  
Author(s):  
Hugh L. Kennedy
Keyword(s):  
Data Streams ◽  
Linear Prediction ◽  
Three Dimensional ◽  
Sensor Data ◽  
Spatio Temporal ◽  
Imaging Sensor

scholarly journals REAL-TIME EARTHQUAKE MONITORING WITH SPATIO-TEMPORAL FIELDS

2017 ◽  
Vol IV-4/W2 ◽  
pp. 215-222 ◽  
Author(s):  
J. C. Whittier ◽  
S. Nittel ◽  
I. Subasinghe
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Data Stream ◽  
Apache Spark ◽  
Sensor Data ◽  
Live Streaming ◽  
Earthquake Activity ◽  
Maximum Displacement ◽  
Earthquake Event ◽  
Spatio Temporal

With live streaming sensors and sensor networks, increasingly large numbers of individual sensors are deployed in physical space. Sensor data streams are a fundamentally novel mechanism to deliver observations to information systems. They enable us to represent spatio-temporal continuous phenomena such as radiation accidents, toxic plumes, or earthquakes almost as instantaneously as they happen in the real world. Sensor data streams discretely sample an earthquake, while the earthquake is continuous over space and time. Programmers attempting to integrate many streams to analyze earthquake activity and scope need to write code to integrate potentially very large sets of asynchronously sampled, concurrent streams in tedious application code. In previous work, we proposed the field stream data model (Liang et al., 2016) for data stream engines. Abstracting the stream of an individual sensor as a temporal field, the field represents the Earth’s movement at the sensor position as continuous. This simplifies analysis across many sensors significantly. In this paper, we undertake a feasibility study of using the field stream model and the open source Data Stream Engine (DSE) Apache Spark(Apache Spark, 2017) to implement a real-time earthquake event detection with a subset of the 250 GPS sensor data streams of the Southern California Integrated GPS Network (SCIGN). The field-based real-time stream queries compute maximum displacement values over the latest query window of each stream, and related spatially neighboring streams to identify earthquake events and their extent. Further, we correlated the detected events with an USGS earthquake event feed. The query results are visualized in real-time.

scholarly journals COMPRESSION AND PROGRESSIVE RETRIEVAL OF MULTI-DIMENSIONAL SENSOR DATA

2016 ◽  
Vol XLI-B2 ◽  
pp. 27-33
Author(s):  
P. Lorkowski ◽  
T. Brinkhoff
Keyword(s):  
Data Streams ◽  
Data Transfer ◽  
Lossless Compression ◽  
Data Retrieval ◽  
Sensor Data ◽  
Data Segment ◽  
The World ◽  
Efficient Data ◽  
Spatio Temporal ◽  
Drifting Buoys

Since the emergence of sensor data streams, increasing amounts of observations have to be transmitted, stored and retrieved. Performing these tasks at the granularity of single points would mean an inappropriate waste of resources. Thus, we propose a concept that performs a partitioning of observations by spatial, temporal or other criteria (or a combination of them) into data segments. We exploit the resulting proximity (according to the partitioning dimension(s)) within each data segment for compression and efficient data retrieval. While in principle allowing lossless compression, it can also be used for progressive transmission with increasing accuracy wherever incremental data transfer is reasonable. In a first feasibility study, we apply the proposed method to a dataset of ARGO drifting buoys covering large spatio-temporal regions of the world´s oceans and compare the achieved compression ratio to other formats.

scholarly journals COMPRESSION AND PROGRESSIVE RETRIEVAL OF MULTI-DIMENSIONAL SENSOR DATA

2016 ◽  
Vol XLI-B2 ◽  
pp. 27-33
Author(s):  
P. Lorkowski ◽  
T. Brinkhoff
Keyword(s):  
Data Streams ◽  
Data Transfer ◽  
Lossless Compression ◽  
Data Retrieval ◽  
Sensor Data ◽  
Data Segment ◽  
The World ◽  
Efficient Data ◽  
Spatio Temporal ◽  
Drifting Buoys

Since the emergence of sensor data streams, increasing amounts of observations have to be transmitted, stored and retrieved. Performing these tasks at the granularity of single points would mean an inappropriate waste of resources. Thus, we propose a concept that performs a partitioning of observations by spatial, temporal or other criteria (or a combination of them) into data segments. We exploit the resulting proximity (according to the partitioning dimension(s)) within each data segment for compression and efficient data retrieval. While in principle allowing lossless compression, it can also be used for progressive transmission with increasing accuracy wherever incremental data transfer is reasonable. In a first feasibility study, we apply the proposed method to a dataset of ARGO drifting buoys covering large spatio-temporal regions of the world´s oceans and compare the achieved compression ratio to other formats.

Spatial modeling of air pollution based on data streams from geosensor networks

2018 ◽  
Vol 930 (12) ◽  
pp. 39-43 ◽  
Author(s):  
V.P. Savinikh ◽  
A.A. Maiorov ◽  
A.V. Materuhin
Keyword(s):  
Air Pollution ◽  
Data Streams ◽  
Spatial Modeling ◽  
Spatial Model ◽  
Large City ◽  
Field Tests ◽  
Processing Unit ◽  
Geosensor Network ◽  
Spatio Temporal ◽  
Different Sources

The article is a brief summary of current research results of the authors in the field of spatial modeling of air pollution based on spatio-temporal data streams from geosensor networks. The urban environment is characterized by the presence of a large number of different sources of emissions and rapidly proceeding processes of contamination spread. So for the development of an adequate spatial model is required to make measurements with a large spatial and temporal resolution. It is shown that geosensor network provide researchers with the opportunity to obtain data with the necessary spatio-temporal detail. The article describes a prototype of a geosensor network to build a detailed spatial model of air pollution in a large city. To create a geosensor in the prototype of the system, calibrated gas sensors for a nitrogen dioxide and carbon monoxide concentrations measurement were interfaced to the module, which consist of processing unit and communication unit. At present, the authors of the article conduct field tests of the prototype developed.

scholarly journals Time-selective data fusion for in-network processing in ad hoc wireless sensor networks

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881130 ◽  
Author(s):  
Jaanus Kaugerand ◽  
Johannes Ehala ◽  
Leo Mõtus ◽  
Jürgo-Sören Preden
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Data Streams ◽  
Ad Hoc ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Temporal Consistency ◽  
Variable Delays

This article introduces a time-selective strategy for enhancing temporal consistency of input data for multi-sensor data fusion for in-network data processing in ad hoc wireless sensor networks. Detecting and handling complex time-variable (real-time) situations require methodical consideration of temporal aspects, especially in ad hoc wireless sensor network with distributed asynchronous and autonomous nodes. For example, assigning processing intervals of network nodes, defining validity and simultaneity requirements for data items, determining the size of memory required for buffering the data streams produced by ad hoc nodes and other relevant aspects. The data streams produced periodically and sometimes intermittently by sensor nodes arrive to the fusion nodes with variable delays, which results in sporadic temporal order of inputs. Using data from individual nodes in the order of arrival (i.e. freshest data first) does not, in all cases, yield the optimal results in terms of data temporal consistency and fusion accuracy. We propose time-selective data fusion strategy, which combines temporal alignment, temporal constraints and a method for computing delay of sensor readings, to allow fusion node to select the temporally compatible data from received streams. A real-world experiment (moving vehicles in urban environment) for validation of the strategy demonstrates significant improvement of the accuracy of fusion results.

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