Model-Driven Data Acquisition in Sensor Networks

By Amol Deshpande, Carlos Guestrin, Samuel Madden, Joseph M Hellerstein, W. Hong. Very Large Data Bases Conference, 2004

1,254 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/393c73fccc56fdeb480e4f1c980b14182711a69d

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In Computer Networks and Communications

In Signal Processing

Citations in the corpus

A literature survey on smart cities (2015, background): “Like all other architecture propositions, the acquisition of divergent urban data is the basis of all smart city concepts, as well as the ﬁrst step of a data-centric strategy [62].”
ActiveClean: Interactive Data Cleaning For Statistical Modeling (2016, neutral): “studied data acquisition in sensor networks [11].”
MacroBase: Prioritizing Attention in Fast Data (2016, methodology): “ic analyses provided by systems such as TAG [33], MacroBase executes statistical models. Related work has examined modeling challenges in related domains (e.g., in data acquisition in sensor networks [18] and in-network sensor network anomaly detection [40,50]). In MacroBase, we seek to draw upon th”
Model-based approximate querying in sensor networks (2005, methodology): “The query was a range query over temperature of all attributes, looking for nodes with temperatures in the range [14,18].”
Approximate Data Collection in Sensor Networks using Probabilistic Models (2006, methodology): “More recently, the BBQ system [8] proposes using probabilistic modeling techniques to optimize data acquisition for sensor network queries.”
Synopsis diffusion for robust aggregation in sensor networks (2004, methodology): “Model-based Aggregation. Model-based aggregation [ Deshpande et al. 2004; Mukhopadhyay et al. 2004] uses temporal correlation of data to correct errors due to transient losses in sensor networks.”
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models (2016, background): “Deshpande et al. studied data acquisition in sensor networks [12].”
From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices (2016, methodology): “The BBQ approach builds a multi-dimensional Gaussian probability density function of the sensors’ likely data values and then uses conditional probabilities to determine, in an iterative manner, the next sensor whose value is most likely to resolve a given query [40].”
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies (2008, neutral): “A simple, yet often effective (cf. Deshpande et al., 2004), approach is to assume that the temperatures have a (multivariate) Gaussian joint distribution.”
A Survey of Uncertain Data Algorithms and Applications (2009, background): “example, data from sensors [35] may be highly correlated”
Towards Real-Time Information Processing of Sensor Network Data Using Computationally Efficient Multi-output Gaussian Processes (2008, methodology): “Examples here include the use of GPs to represent spatial correlations between sensors in order that they may be positioned to maximise mutual information [10], and the use of multi-variate Gaussians to represent correlations between different sensors and sensor types for energy efficient querying o”
Near-optimal sensor placements in Gaussian processes (2005, neutral): “A simple, yet often effective (Deshpande et al., 2004), approach is to assume that the temperatures have a (multivariate) Gaussian joint distribution.”