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Spatiotemporal Coverage in Fusion-Based Sensor Networks

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The Art of Wireless Sensor Networks

Part of the book series: Signals and Communication Technology ((SCT))

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Abstract

Wireless sensor networks (WSNs) have been increasingly available for critical applications such as security surveillance and environmental monitoring. As a fundamental performance measure of WSNs, coverage characterizes how well a sensing field is monitored by a network. Two facets of coverage, i.e., spatial coverage and temporal coverage, quantify the percentage of area that is well monitored by the network and the timeliness of the network in detecting targets appearing in the sensing field, respectively. Although advanced collaborative signal processing algorithms have been adopted by many existing WSNs, most previous analytical studies on spatiotemporal coverage of WSNs are conducted based on overly simplistic sensing models (e.g., the disc model) that do not capture the stochastic nature of sensing. In this chapter, we attempt to bridge this gap by exploring the fundamental limits of spatiotemporal coverage based on stochastic data fusion models that fuse noisy measurements of multiple sensors. We derive the scaling laws between spatiotemporal coverage, network density, and signal-to-noise ratio (SNR). We show that data fusion can significantly improve spatiotemporal coverage by exploiting the collaboration among sensors when several physical properties of the target signal are known. In particular, for signal path loss exponent of \(k\) (typically between \(2.0\) and \(5.0\)), we prove that \(\rho _f{/}\rho _d = {\mathcal {O}}(\delta ^{2/k})\), where \(\rho _f\) and \(\rho _d\) are the densities of uniformly deployed sensors that achieve full spatial coverage or minimum detection delay under the fusion and disc models, respectively, and \(\delta \) is SNR. Our results help understand the limitations of the previous analytical results based on the disc model and provide key insights into the design of WSNs that adopt data fusion algorithms. Our analyses are verified through extensive simulations based on both synthetic data sets and data traces collected in a real deployment for vehicle detection.

Part of this book chapter was written when Rui Tan was with Michigan State University.

The work presented in this chapter was supported in part by the National Science Foundation under grant CNS-0954039 (CAREER) and Singapore’s Agency for Science, Technology and Research (A\(\star \)STAR) under the Human Sixth Sense Programme.

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Notes

  1. 1.

    Several types of sensors (e.g., acoustic sensor) only sample signal intensity at a given sampling rate. The signal energy can be obtained by preprocessing the time series of a given interval, which has been commonly adopted to avoid the transmission of raw data [10, 13, 14, 25, 39].

  2. 2.

    Numerically, the network density \(\rho \) will not be very large when the \(\alpha \)-delay approaches one. For instance, according to Lemma 2, suppose the sensing range \(r\) is \(5\,\text {m}\), the \(\alpha \)-delay under the disc model is \(1+10^{-5}\) when \(\rho =0.15\).

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Authors and Affiliations

  1. Advanced Digital Sciences Center, Singapore, Singapore

    Rui Tan

  2. Michigan State University, East Lansing, MI, 48824, USA

    Guoliang Xing

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Correspondence to Rui Tan .

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  1. College of Engineering and Computer Science, Department of Computer and Information Science, University of Michigan-Dearborn, Dearborn, Michigan, USA

    Habib M. Ammari

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Tan, R., Xing, G. (2014). Spatiotemporal Coverage in Fusion-Based Sensor Networks. In: Ammari, H. (eds) The Art of Wireless Sensor Networks. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40066-7_4

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  • DOI: https://doi.org/10.1007/978-3-642-40066-7_4

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