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A methodology for extracting temporal properties from sensor network data streams

Dimitrios Lymberopoulos, Athanasios Bamis, Andreas Savvides
2009 Proceedings of the 7th international conference on Mobile systems, applications, and services - Mobisys '09  
The extraction of temporal characteristics from sensor data streams can reveal important properties about the sensed events.  ...  In this paper we outline a methodology for extracting the temporal properties, in terms of start time and duration, of sensor data streams that can be used in applications such as human, habitat, environmental  ...  Figure 2 : Overview of the proposed methodology for extracting the temporal structure of the sensor data stream.  ... 
doi:10.1145/1555816.1555836 dblp:conf/mobisys/LymberopoulosBS09 fatcat:iaogobl6yfhm7lsgoxpumnlhaa

Investigation of Spatio Temporal Associations in Wireless Sensor Networks

T. Abirami, P. Thangaraj
2012 International Journal of Computer Applications  
The large overhead is a serious obstacle for deploying long lived and large scale sensor networks.  ...  The data collected by the sensors are delivered to the sink and offline analyses on the data to extract patterns are conducted.  ...  [9] presented a centralized algorithm for mining inter-stream associations from sensor networks.  ... 
doi:10.5120/5767-7988 fatcat:gpg6vucqyjbm5i2o5ahdd4ne7y

A Survey on Knowledge Extraction from WSN

2016 International Journal of Science and Research (IJSR)  
WSN generates large amount of data streams. Extracting information from the data is the most important task in WSN. Many methods are available to extract the information from WSN.  ...  Recently, data management and processing for wireless sensor networks are become a topic of active research in several fields of computer science such as distributed system, database systems and data mining  ...  There are data mining techniques which have recently received a great attention to extract intersecting knowledge from these stream data.  ... 
doi:10.21275/v5i1.nov152765 fatcat:jhibvyjx6jeife5iwr7kwz4qaa

Looking to the skies: realising the combined potential of drones and thermal infrared imagery to advance hydrological process understanding in headwaters

Stephen J Dugdale, Julian Klaus, David M Hannah
2022 Water Resources Research  
to the stream network.  ...  This knowledge gap stems largely from our inability to observe many hydrological properties at scales amenable to understanding underlying processes, and is a major barrier to developing more generalized  ...  RGB imagery could help in the identification of correlates (e.g., depressions, water levels, landuse types) of surface saturation or stream network extension, extracted from TIR data.  ... 
doi:10.1029/2021wr031168 fatcat:ulg5xqsczjf4xjpeznw2epsxsi

Use of Social Media Data in Disaster Management: A Survey

Jedsada Phengsuwan, Tejal Shah, Nipun Balan Thekkummal, Zhenyu Wen, Rui Sun, Divya Pullarkatt, Hemalatha Thirugnanam, Maneesha Vinodini Ramesh, Graham Morgan, Philip James, Rajiv Ranjan
2021 Future Internet  
This survey includes the methodologies for social media data classification and event detection as well as spatial and temporal information extraction.  ...  In this paper, we provide a survey of how social media data contribute to disaster management and the methodologies for social media data management and analysis in disaster management.  ...  Abnormal events captured from extracted topics did not happen frequently and covered only a small fraction of the social media data stream.  ... 
doi:10.3390/fi13020046 fatcat:2p5xerp3cngkhb2myi7vmwe4pm

A Systematic Review of Ontology-Based River Streamflow and Flood Data Management Challenges

Muhammad H. Mughal, Zubair A. Shaikh, Zahid H. Khand, Asif Rajput, Faheem Akhtar
2021 Quaid-e-awam University research journal of engineering science & technology  
Coordination limiting factors includes native data acquisition methodology of each stakeholder for their specific needs, the complexity of the domain involving a heterogeneous group of managers, spatio-temporal  ...  In this research, we review the challenges of a large scale spatio-temporal system for streamflow of watersheds and flood disaster management based on the ontological semantic models.  ...  For an efficient forecasting system, a minimum of three heterogeneous systems hydraulic, hydrologic, and data sources for stream gauge or sensor network data required [1] .  ... 
doi:10.52584/qrj.1901.06 fatcat:udahjoj67rafblie5pxqahedte

Data Mining Techniques for Wireless Sensor Networks: A Survey

Azhar Mahmood, Ke Shi, Shaheen Khatoon, Mi Xiao
2013 International Journal of Distributed Sensor Networks  
Recently, data management and processing for wireless sensor networks (WSNs) has become a topic of active research in several fields of computer science, such as the distributed systems, the database systems  ...  This challenge motivates the research community to explore novel data mining techniques dealing with extracting knowledge from large continuous arriving data from WSNs.  ...  Data mining in sensor networks is the process of extracting application-oriented models and patterns with acceptable accuracy from a continuous, rapid, and possibly nonended flow of data streams from sensor  ... 
doi:10.1155/2013/406316 fatcat:wdrq5vffqbcylcdqvqinx4vjui

Flexibility Assessment of Heat Exchanger Networks: From a Thorough Data Extraction to Robustness Evaluation

Lucille Payet, Raphaële Thery Hétreux, Gilles Hétreux, Florent Bourgeois, Pascal Floquet
2018 Chemical engineering research & design  
As a consequence, to evaluate its value, the first step is to perform an enhanced data collection by identifying the most frequent disturbances and by pointing out the critical streams i.e. the streams  ...  In this paper, a methodology relying on several models is developed to address this issue: a Mass Equilibrium Summation enthalpy non-linear model (MESH) dedicated to the enhanced data collection, a Mixed  ...  The LGC greatly acknowledge TOTAL and the ADEME (French Environment and Energy Management Agency) for the financial support of this project.  ... 
doi:10.1016/j.cherd.2017.11.036 fatcat:6dzj442kdzagbigblygpkebmny

Analysis of Deep Neural Networks For Human Activity Recognition in Videos – A Systematic Literature Review

Hadiqa Aman Ullah, Sukumar Letchmunan, M. Sultan Zia, Umair Muneer Butt, Fadratul Hafinaz Hassan
2021 IEEE Access  
Based on IDT, Two-stream CNN [52] proposes video dynamics mining schemes to extract temporal information by calculating the motion intensity of each frame of a video sequence and developing action data  ...  Most of the papers use existing popular networks for image data to extract features from video sequences such as AlexNet, VGG, DenseNet, ResNet, ResNeXt, GoogLeNet, Inception and MobileNet. 3D convolutional  ...  She has previously served as a lecturer in the field of Computer Science and IT and has one journal publication before. Her research interests are Data Science, Machine learning, and Computer vision.  ... 
doi:10.1109/access.2021.3110610 fatcat:ussooxm7azfljpb5prsm7creaa

Event dashboard: Capturing user-defined semantics events for event detection over real-time sensor data

Jonathan Yu, Kerry Taylor
2013 International Semantic Web Conference  
Our approach allows the event description to be abstracted from specific interfaces of a sensor network and to be used for querying of sensor data.  ...  Event descriptions can subsequently be deployed through a semantic mediator to complex event processing and stream processing implementations over a sensor network.  ...  We thank our colleagues at CSIRO: Bradford Sherman for his expertise in the water quality, and Laurent Lefort for his helpful review of this work.  ... 
dblp:conf/semweb/YuT13 fatcat:g6gcdvfzxjbvnewsmc3qzsds6u

Using Trend Extraction and Spatial Trends to Improve Flood Modeling and Control [chapter]

Jacob Hale, Suzanna Long, Vinayaka Gude, Steven Corns
2021 Data Visualization [Working Title]  
A long short-term memory (LSTM) network is created to develop a univariate time series value for river stage prediction that improves the temporal resolution and accuracy of forecasts.  ...  This chapter considers publicly available data sets and data visualization techniques that can be adapted for use by all community planners and decision makers.  ...  This location provides a suitable candidate to test the methodology presented given the extent of the flood event and data availability. First, data is gathered from a nearby stream gauge.  ... 
doi:10.5772/intechopen.96347 fatcat:vpvdfwe6bfhqplnwfzoo5nytwm

A Reasonable Exploration of Data Mining Techniques in Wireless Sensor Network

Shubhie Agarwal, Seema Maitrey, Poonam Rana, Pankaj Singh
2016 International Journal of Computer Applications  
Data mining techniques of wireless sensor network are different from traditional techniques.  ...  By seeing these shortcomings and special characteristics of WSNs, there is a need for data mining technique designed for WSNs.  ...  Another method LW (Light weight classification)[26] a one pass algorithm is used for mining on-board mining of data stream in sensor network.  ... 
doi:10.5120/ijca2016911928 fatcat:73so6y6ykfhzvkehjzekxwtjui

An event abstraction layer for the integration of geosensor data

Alejandro Llaves, Werner Kuhn
2014 International Journal of Geographical Information Science  
For the data integration, we represent event-related information extracted from multiples sources under a common event model.  ...  Spatio-temporal properties of the event are inferred from the geosensor location and the observation timestamps.  ...  Acknowledgements This research was carried out at the Institute for Geoinformatics, University of Muenster and it was funded by the European project ENVISION (FP7-249170).  ... 
doi:10.1080/13658816.2014.882513 fatcat:exqk6rfmmvbtxjfjrjdmghwcni

Wireless Sensor Networks Fault Identification Using Data Association

Y.
2012 Journal of Computer Science  
Approach: This large overhead becomes a hurdle for the deployment of long term large scale sensor networks.  ...  Sensor which collect data hand them over to the sink which is followed by offline data analyses to extract patterns.  ...  As the overhead becomes a serious hurdle for the deployment of long-lived and large-scale sensor network, data mining techniques are applied in-network to locate data patterns from raw data streams so  ... 
doi:10.3844/jcssp.2012.1501.1505 fatcat:phfn63jvpra7hbxxhrtiiu6x7u

Analysis of Spatio-temporal Representations for Robust Footstep Recognition with Deep Residual Neural Networks

Omar Costilla Reyes, Ruben Vera-Rodriguez, Patricia Scully, Krikor B. Ozanyan
2018 IEEE Transactions on Pattern Analysis and Machine Intelligence  
We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification.  ...  We perform a feature analysis of deep residual neural networks showing effective clustering of clients footstep data and provide insights of the feature learning process.  ...  Foster, Hujun Yin and Bernardino Romera-Paredes for useful discussions. O. Costilla-Reyes would like to acknowledge CONACyT (Mexico) for a studentship.  ... 
doi:10.1109/tpami.2018.2799847 pmid:29994418 fatcat:7vra2lo26rennoamvuo6fuh4vu
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