Scalable Information-Driven Sensor Querying and Routing for Ad Hoc Heterogeneous Sensor Networks

Chu, Maurice; Haußecker, Horst; Zhao, Feng

doi:10.1177/10943420020160030901

Cited by 517 publications

(270 citation statements)

References 6 publications

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“…TinyDB and Cougar [45,28] provide declarative interfaces to acquiring data from sensor networks. Several systems propose to use probabilistic modeling techniques to answer queries over sensor networks [21,12,10], though these have typically used specific models rather than a generalized implementation in an existing relational database. Finally, as discussed in Section 3, our system generalizes and significantly enriches the abstraction of model-based views that was originally proposed in the MauveDB system [13].…”

Section: Related Workmentioning

confidence: 99%

Online Filtering, Smoothing and Probabilistic Modeling of Streaming data

Kanagal

Deshpande

2008

2008 IEEE 24th International Conference on Data Engineering

116

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In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a probabilistic database view. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms commonly used to implement dynamic probabilistic models, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept up-to-date as new readings arrive. We develop novel techniques to convert the queries on the model-based view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over sensor data from the Intel Lab dataset that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of such tight integration between dynamic probabilistic models and database systems.

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Section: Related Workmentioning

confidence: 99%

Online Filtering, Smoothing and Probabilistic Modeling of Streaming data

Kanagal

Deshpande

2008

2008 IEEE 24th International Conference on Data Engineering

116

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“…This recursion specializes the general space recursion (8) to the linear/Gaussian case. Instead of the partial posteriors f`xn|z…”

Section: Time-space-sequential Distributed Kalman Filtermentioning

confidence: 99%

“…Distributed state estimation in wireless sensor networks has received some attention recently (e.g., [1][2][3][4][5][6][7][8]). It is often desirable to limit inter-sensor communications to nearby sensors [1].…”

Section: Introductionmentioning

confidence: 99%

Time-space-sequential algorithms for distributed Bayesian state estimation in serial sensor networks

Hlinka¹,

Hlawatsch²

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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We consider distributed estimation of a time-dependent, random state vector based on a generally nonlinear/non-Gaussian state-space model. The current state is sensed by a serial sensor network without a fusion center. We present an optimal distributed Bayesian estimation algorithm that is sequential both in time and in space (i.e., across sensors) and requires only local communication between neighboring sensors. For the linear/Gaussian case, the algorithm reduces to a time-space-sequential, distributed form of the Kalman filter. We also demonstrate the application of our state estimator to a target tracking problem, using a dynamically defined "local sensor chain" around the current target position.

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“…The choice of this active subset of sensors is balanced by the communication costs needed for communication of all sensors. A leaded is selected in this protocol that decides the optimal subset of sensors necessary for information sensing from the network [22,26].…”

Section: Topology Control Based On Irregular Sensor Modelmentioning

confidence: 99%

Effectual Routing in Wireless Sensor Networks- a Review

Chhabra¹,

Patheja²

2014

International Journal of Advanced Research in Computer and Comm

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A wireless sensor network is an enormous assortment of sensor nodes with inadequate power supply and self-conscious computational competence. Due to the circumscribed communication range and high density of sensor nodes, packet dispatching in sensor networks is usually performed through multi-hop data transmission. For that reason, routing in wireless sensor networks has been deliberated as an imperative field of research over the past decade. These days, multipath routing techniques is widely used in wireless sensor networks to improve network performance over and done with efficient utilization of accessible network resources. Consequently, the main intention of this review is to present the conception of the various routing approaches and its fundamental experiments, as well as the basic motivations for exploiting these techniques in wireless sensor networks. Routing procedures for WSNs are responsible for selecting and maintaining the routes in the network and ensure reliable and effective communication in inadequate periods. The energy constriction of WSNs make energy saving become the most significant objective of various routing algorithms. In this paper, a review of routing protocols and algorithms used in WSNs is presented with energy efficiency as the main goalmouth.

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Scalable Information-Driven Sensor Querying and Routing for Ad Hoc Heterogeneous Sensor Networks

Cited by 517 publications

References 6 publications

Online Filtering, Smoothing and Probabilistic Modeling of Streaming data

Online Filtering, Smoothing and Probabilistic Modeling of Streaming data

Time-space-sequential algorithms for distributed Bayesian state estimation in serial sensor networks

Effectual Routing in Wireless Sensor Networks- a Review

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