MPM: Map Based Predictive Monitoring for Wireless Sensor Networks

Ali, Azad; Khelil, Abdelmajid; Shaikh, Faisal Karim; Suri, Neeraj

doi:10.1007/978-3-642-11482-3_6

Cited by 8 publications

(7 citation statements)

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%

Adaptive Hybrid Compression for Wireless Sensor Networks

Ali

Khelil

Suri

et al. 2015

ACM Trans. Sen. Netw.

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Wireless Sensor Networks (WSNs) are often deployed to sample the desired environmental attributes and deliver the acquired samples to a central station, termed as the sink, for processing as needed by the application. Many applications stipulate high granularity and data accuracy that results in high data volumes. However, sensor nodes are battery powered, and sending the requested large amounts of data rapidly depletes their energy. Fortunately, environmental attributes (e.g., temperature, pressure) often exhibit spatial and temporal correlations. Moreover, a large class of applications such as scientific analysis and simulations tolerate high latency for sensor data collection. Hence, we exploit the spatiotemporal correlation of sensor readings while benefiting from possible data delivery latency tolerance to minimize the amount of data to be transported to the sink. Accordingly, we develop a fully distributed adaptive hybrid compression scheme that exploits both spatial and temporal data redundancies and fuses both temporal and spatial compression for maximal data compression with accuracy guarantees. We present two main contributions: (i) an adaptive modeling technique that allows frugal and maximized temporal compression on resource-constraint sensor nodes by exploiting the data collection latency, and (ii) a novel model-based hierarchical clustering technique that allows for maximized spatial compression resulting into a hybrid compression scheme. Compared to the existing spatiotemporal compression approaches, our approach is fully decentralized and the proposed clustering scheme is based on sensor data models rather than instantaneous sensor data values, which allows merging nearby nodes with similar models into large clusters over a longer period of time rather than specific time instances. The analysis for computation and message overheads, the analysis for theoretical compressibility, and simulations using real-world data demonstrate that our proposed scheme can provide significant communication/energy savings without sacrificing the accuracy of collected data.

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Section: The Problem and The Approachmentioning

confidence: 99%

Adaptive Hybrid Compression for Wireless Sensor Networks

Ali

Khelil

Suri

et al. 2015

ACM Trans. Sen. Netw.

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“…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%

Real-Time Data Analytics in Sensor Networks

Palpanas

2012

Managing and Mining Sensor Data

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Abstract. The proliferation of Wireless Sensor Networks (WSNS) in the past decade has provided the bridge between the physical and digital worlds, enabling the monitoring and study of physical phenomena at a granularity and level of detail that was never before possible. In this study, we review the efforts of the research community with respect to two important problems in the context of WSNS: real-time collection of the sensed data, and real-time processing of these data series.

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“…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.…”

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confidence: 99%

An adaptive and composite spatio-temporal data compression approach for wireless sensor networks

Ali

Khelil

Szczytowski

et al. 2011

Proceedings of the 14th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

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Wireless Sensor Networks (WSN) are often deployed to sample the desired environmental attributes and deliver the acquired samples to the sink for processing, analysis or simulations as per the application needs. Many applications stipulate high granularity and data accuracy that results in high data volumes. Sensor nodes are battery powered and sending the requested large amount of data rapidly depletes their energy. Fortunately, the environmental attributes (e.g., temperature, pressure) often exhibit spatial and temporal correlations. Moreover, a large class of applications such as scientific measurement and forensics tolerate high latencies for sensor data collection. Accordingly, we develop a fully distributed adaptive technique for spatial and temporal innetwork data compression with accuracy guarantees. We exploit the spatio-temporal correlation of sensor readings while benefiting from possible data delivery latency tolerance to further minimize the amount of data to be transported to the sink. Using real data, we demonstrate that our proposed scheme can provide significant communication/energy savings without sacrificing the accuracy of collected data. In our simulations, we achieved data compression of up to 95% on the raw data requiring around 5% of the original data to be transported to the sink. Categories and Subject Descriptors General TermsAlgorithms, Design, Measurement, Performance Keywords spatial and temporal correlations, hierarchical clustering, energy efficiency, modeling, approximation, accuracy * Research supported in part by HEC and DFG GRK 1362 (TUD GKMM).Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. THE PROBLEM AND THE APPROACHIn WSN deployments, sensor nodes are often distributed over the monitoring area for unattended environmental monitoring or for supervisory functions. The typical WSN functionality being (i) local event detection and reporting it to the sink, and (ii) continuous data collection by sampling the environment and sending the samples to the sink. 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. As examples, various scientific applications, such as, volcano monitoring [21] or eco-systems [17] [5], require detailed ambient data with high spatio-temporal sampling resolution for fine-granular understanding of the physical processes. For such applications, a WSN is essentially ...

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MPM: Map Based Predictive Monitoring for Wireless Sensor Networks

Cited by 8 publications

References 12 publications

Adaptive Hybrid Compression for Wireless Sensor Networks

Adaptive Hybrid Compression for Wireless Sensor Networks

Real-Time Data Analytics in Sensor Networks

An adaptive and composite spatio-temporal data compression approach for wireless sensor networks

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