Missing Event Prediction in Sensor Data Streams Using Kalman Filters

Vijayakumar, Nithya N.; Plale, Beth

doi:10.1201/9781420082333.ch9

Cited by 14 publications

(11 citation statements)

References 12 publications

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“…In order to compare measurements from the GPS with that from the InSAR analysis, measurements from corresponding dates had to be established. Kalman filtering has been widely used to integrate time series observations (Vijayakumar and Plale 2008). Therefore, a Kalman filter was used to build a model to interpolate the missing records and predict the measurements corresponding to the InSAR dates in this study.…”

Section: Validation Of Insar Resultsmentioning

confidence: 99%

Land Subsidence Monitoring in Greater Vancouver Through Synergy of InSAR and Polarimetric Analysis

Chen

Wang

Huang

2018

Canadian Journal of Remote Sensing

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Up-to-date spatial information on ground movements and land use is useful for emergency management of coastal regions. Time series InSAR techniques have proven to be effective tools for providing the former; however, InSAR results alone cannot be used to characterize the relationship between movements and land use. The focus of this study is to evaluate the potential to use high resolution radar satellite imagery for monitoring urban land subsidence associated with the construction of new building in Canadian coastal cities. To do this, we propose to integrate InSAR and polarimetric SAR information for deformation analysis. The methodology included multidimensional small baseline subset (MSBAS) InSAR analysis, polarimetric SAR change detection, and integration of the coherence, deformation, and polarimetric information for identifying the urban surface movements related to new buildings. The study was conducted in the Vancouver region, BC using RADARSAT-2 satellite data of ultra-fine mode and fine-quad mode acquired during 2010-2016. Results demonstrated that the integration of polarimetric and InSAR data permitted identification of ground movement, and the association of these movements to the new constructions in the urban environment. Several locations have been experiencing subsidence at a rate of up to 10 cm/year, and horizontal motion of 5 cm/year. R ESUM E men ee dans la r egion de Vancouver, en Colombie-Britannique a l'aide de donn ees satellitaires RADARSAT-2 acquise entre 2010 et 2016 dans les modes ultra-fin et quad-fin. Les r esultats d emontrent que l'int egration des donn ees polarim etriques et InRSO permet d'identifier les mouvements des sols et de les associer aux nouvelles constructions dans un environnement urbain. Plusieurs endroits d emontrent de l'affaissement a un rythme allant jusqu' a 10 cm/an et des mouvements horizontaux de 5 cm/an.

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Section: Validation Of Insar Resultsmentioning

confidence: 99%

Land Subsidence Monitoring in Greater Vancouver Through Synergy of InSAR and Polarimetric Analysis

Chen

Wang

Huang

2018

Canadian Journal of Remote Sensing

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“…Probabilistic Matrix Factorization: There are two major advantages of using probabilistic matrix factorization (PMF) [30] for handling missing IoT sensor data. First is the dimensionality reduction, which is the underlying property of matrix factorization.…”

Section: Incremental Space-time Model (Istm)mentioning

confidence: 99%

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Krishnamurthi

Kumar

Gopinathan

et al. 2020

Sensors

194

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In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

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“…Miao et al [39] did a comprehensive survey about incomplete data management. In order to obtain complete data, some studies imputed the missing attributes by applying rule-based (exact matching over all dimensions) [21], statistical-based (exact matching over partial dimensions) [37], filter-based [58], pattern-based [61], or analysis-based [45] imputation methods. For example, [61] imputed the missing attributes in streams by finding the k most similar patterns from l time series.…”

Section: Related Workmentioning

confidence: 99%

Skyline queries over incomplete data streams

2019

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Nowadays, efficient and effective processing over massive stream data has attracted much attention from the database community, which are useful in many real applications such as sensor data monitoring, network intrusion detection, and so on. In practice, due to the malfunction of sensing devices or imperfect data collection techniques, real-world stream data may often contain missing or incomplete data attributes. In this paper, we will formalize and tackle a novel and important problem, named skyline query over incomplete data stream (Sky-iDS), which retrieves skyline objects (in the presence of missing attributes) with high confidences from incomplete data stream. In order to tackle the Sky-iDS problem, we will design efficient approaches to impute missing attributes of objects from incomplete data stream via differential dependency (DD) rules. We will propose effective pruning strategies to reduce the search space of the Sky-iDS problem, devise cost-model-based index structures to facilitate the data imputation and skyline computation at the same time, and integrate our proposed techniques into an efficient Sky-iDS query answering algorithm. Extensive experiments have been conducted to confirm the efficiency and effectiveness of our Sky-iDS processing approach over both real and synthetic data sets.

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Missing Event Prediction in Sensor Data Streams Using Kalman Filters

Cited by 14 publications

References 12 publications

Land Subsidence Monitoring in Greater Vancouver Through Synergy of InSAR and Polarimetric Analysis

Land Subsidence Monitoring in Greater Vancouver Through Synergy of InSAR and Polarimetric Analysis

An Overview of IoT Sensor Data Processing, Fusion, and Analysis Techniques

Skyline queries over incomplete data streams

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