Supporting Topographic Queries in a Class of Networked Sensor Systems

Singh, Manisha; Prasanna, Viktor K.

doi:10.1109/percomw.2005.77

Cited by 9 publications

(6 citation statements)

References 9 publications

(13 reference statements)

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“…The problem of boundary detection and determining the extent of an event in sensor networks has been investigated in [8,[16][17][18][19]. Chintalapudi and Govindan presented localized edge detection techniques based on statistics, image processing, and classification [16].…”

Section: Boundary Detectionmentioning

confidence: 99%

Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains

Vahabi

Gupta

Albano

et al. 2015

International Journal of Distributed Sensor Networks

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The vision of the Internet of Things (IoT) includes large and dense deployment of interconnected smart sensing and monitoring devices. This vast deployment necessitates collection and processing of large volume of measurement data. However, collecting all the measured data from individual devices on such a scale may be impractical and time-consuming. Moreover, processing these measurements requires complex algorithms to extract useful information. Thus, it becomes imperative to devise distributed information processing mechanisms that identify application-specific features in a timely manner and with low overhead. In this paper, we present a feature extraction mechanism for dense networks that takes advantage of dominance-based medium access control (MAC) protocols to (i) efficiently obtain global extrema of the sensed quantities, (ii) extract local extrema, and (iii) detect the boundaries of events, by using simple transforms that nodes employ on their local data. We extend our results for a large dense network with multiple broadcast domains (MBD). We discuss and compare two approaches for addressing the challenges with MBD and we show through extensive evaluations that our proposed distributed MBD approach is fast and efficient at retrieving the most valuable measurements, independent of the number sensor nodes in the network.

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Section: Boundary Detectionmentioning

confidence: 99%

Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains

Vahabi

Gupta

Albano

et al. 2015

International Journal of Distributed Sensor Networks

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“…Anand et al [20] propose an in-network data aggregation approach for aggregating spatial data at multiple resolution points, but do not deal with how feature regions can be constructed and maintained. Singh et al [21] address the problem of building topographic maps, and Singh and Prasanna [22] demonstrate how the information stored in topographic maps can be used for effi ciently routing and resolving topographic queries.…”

Section: Related Workmentioning

confidence: 99%

A communication‐efficient framework for outlier‐free data reporting in data‐gathering sensor networks

Jayashree¹,

Arumugam²,

Meenakshi³

2008

Int J Network Mgmt

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SUMMARYIn this paper, we address the problem of reducing the communication cost and hence the energy costs incurred in data-gathering applications of a sensor network. Environmental data depicts a huge amount of correlation in both the spatial and temporal domains. We exploit these temporal-spatial correlations to address the aforementioned problem. More specifi cally, we propose a framework that partitions the physical sensor network topology into a number of feature regions. Each sensor node builds a data model that represents the underlying structure of the data. A representative node in each feature region communicates only the model coeffi cients to the sink, which then uses them to answer queries. The temporal and spatial similarity has special meaning in outlier cleaning too. We use a modifi ed z-score technique to precisely label the outliers and use the spatial similarity to confi rm whether the outliers are due to a true change in the phenomenon under study or due to faulty sensor nodes.

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“…In the sensor network community, the contour detection and simplification problem has received a lot of attention. Many boundary (physical or data space) detection algorithms [3,8,17,18,23,25,28] have been proposed, in which each sensor detects and stores a part of the boundary. None of the distributed heuristics proposed for compressing the boundary representation of the contours [12,26,32] are able to provide worst-case approximation guarantees, with the exception of [2], which is valid only for a single polygon.…”

Section: Related Workmentioning

confidence: 99%

Approximate isocontours and spatial summaries for sensor networks

Gandhi¹,

Hershberger²,

Suri³

2007

Proceedings of the 6th International Conference on Information Processing in Sensor Networks - IPSN '07

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We consider the problem of approximating a family of isocontours in a sensor field with a topologically-equivalent family of simple polygons. Our algorithm is simple and distributed, it gracefully adapts to any user-specified representation size k, and it delivers a worst-case guarantee for the quality of approximation. In particular, we prove that the topology-respecting Hausdorff error in our k-vertex approximation is within a small constant factor of the optimal error possible with Θ(k/ log m) vertices, where m is the number of contours. Evaluation of the algorithm on real data suggests that the size increase factor in practice is a constant near 2.6, and shows no error increase. Our simulation results using a variety of synthetic and real data show that the algorithm smoothly handles complex isocontours, even for representation sizes as small as 32 or 48. Because isocontours are widely used to represent and communicate bi-variate signals, our technique is broadly applicable to innetwork aggregation and summarization of spatial data in sensor networks.

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Supporting Topographic Queries in a Class of Networked Sensor Systems

Abstract: Abstract

Cited by 9 publications

References 9 publications

Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains

Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains

A communication‐efficient framework for outlier‐free data reporting in data‐gathering sensor networks

Approximate isocontours and spatial summaries for sensor networks

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