2010
DOI: 10.1007/978-3-642-11482-3_6
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Abstract: Abstract. We present the design of a Wireless Sensor Networks (WSN) level event prediction framework to monitor the network and its operational environment to support proactive self* actions. For example, by monitoring and subsequently predicting trends on network load or sensor nodes energy levels, the WSN can proactively initiate selfreconfiguration. We propose a Map based Predictive Monitoring (MPM) approach where a selected WSN attribute is first profiled as WSN maps, and based on the maps history, predict… Show more

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Cited by 8 publications
(7 citation statements)
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References 12 publications
(25 reference statements)
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“…Applications requiring continuous data collection utilize the data for two prominent use cases: (a) live/real-time decision making, such as surveillance, or (b) offline/delay-tolerant processing such as modeling, analysis [Tolle et al 2005], and inference [Ali et al 2009]. The focus of our current work is on delay-tolerant data collection.…”
Section: The Problem and The Approachmentioning
confidence: 99%
“…Applications requiring continuous data collection utilize the data for two prominent use cases: (a) live/real-time decision making, such as surveillance, or (b) offline/delay-tolerant processing such as modeling, analysis [Tolle et al 2005], and inference [Ali et al 2009]. The focus of our current work is on delay-tolerant data collection.…”
Section: The Problem and The Approachmentioning
confidence: 99%
“…Other techniques offer solutions for efficient spatio-temporal data suppression [5,[15][16][17][18][19], where in addition to the temporal correlations present in the sensor network data, they aim at identifying and exploiting the spatial correlations of the data, as well. Furthermore, previous works have proposed algorithms that help in the selection of representative nodes when we want to monitor large-scale phenomena (i.e., phenomena that evolve over days, or months, and involve several sensor nodes) [20], or when we want to take into account the remaining energy of each individual node [21]. The above techniques help to further reduce the communication cost of the sensor network, and could be applied on top of the model-driven, or data-driven techniques.…”
Section: Data Collectionmentioning
confidence: 99%
“…Random component can be modeled by simple Autoregressive Moving Average (ARMA) models (Def. 1) or even simpler variants, i.e., Autoregressive (AR) and Moving Average (MA) models [3]. These models allow adaptability but are simple enough to be evaluated on common sensor nodes.…”
Section: Stage 2: Temporal Modeling On Clustersmentioning
confidence: 99%
“…In this paper, we deal with continuous data collection. Applications utilize continuously collected data for (a) real-time decision making, such as surveillance, or (b) delay-tolerant processing such as modeling, analysis [17] and inference [3]. In this work, we develop adaptive modeling algorithms that exploit the delay-tolerance of the data collection to maximize data compression.…”
mentioning
confidence: 99%