Information-Theoretic Performance Analysis of Sensor Networks via Markov Modeling of Time Series Data

Yue Li, Devesh K. Jha, Asok Ray, Thomas A. Wettergren

    Research output: Contribution to journalArticle

    4 Citations (Scopus)

    Abstract

    This paper presents information-theoretic performance analysis of passive sensor networks for detection of moving targets. The proposed method falls largely under the category of data-level information fusion in sensor networks. To this end, a measure of information contribution for sensors is formulated in a symbolic dynamics framework. The network information state is approximately represented as the largest principal component of the time series collected across the network. To quantify each sensor's contribution for generation of the information content, Markov machine models as well as x-Markov (pronounced as cross-Markov) machine models, conditioned on the network information state, are constructed; the difference between the conditional entropies of these machines is then treated as an approximate measure of information contribution by the respective sensors. The x-Markov models represent the conditional temporal statistics given the network information state. The proposed method has been validated on experimental data collected from a local area network of passive sensors for target detection, where the statistical characteristics of environmental disturbances are similar to those of the target signal in the sense of time scale and texture. A distinctive feature of the proposed algorithm is that the network decisions are independent of the behavior and identity of the individual sensors, which is desirable from computational perspectives. Results are presented to demonstrate the proposed method's efficacy to correctly identify the presence of a target with very low false-alarm rates. The performance of the underlying algorithm is compared with that of a recent data-driven, feature-level information fusion algorithm. It is shown that the proposed algorithm outperforms the other algorithm.

    Original languageEnglish (US)
    Pages (from-to)1898-1909
    Number of pages12
    JournalIEEE Transactions on Cybernetics
    Volume48
    Issue number6
    DOIs
    StatePublished - Jun 1 2018

    Fingerprint

    Sensor networks
    Time series
    Sensors
    Information fusion
    Target tracking
    Local area networks
    Entropy
    Textures
    Statistics

    All Science Journal Classification (ASJC) codes

    • Software
    • Control and Systems Engineering
    • Information Systems
    • Human-Computer Interaction
    • Computer Science Applications
    • Electrical and Electronic Engineering

    Cite this

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    title = "Information-Theoretic Performance Analysis of Sensor Networks via Markov Modeling of Time Series Data",
    abstract = "This paper presents information-theoretic performance analysis of passive sensor networks for detection of moving targets. The proposed method falls largely under the category of data-level information fusion in sensor networks. To this end, a measure of information contribution for sensors is formulated in a symbolic dynamics framework. The network information state is approximately represented as the largest principal component of the time series collected across the network. To quantify each sensor's contribution for generation of the information content, Markov machine models as well as x-Markov (pronounced as cross-Markov) machine models, conditioned on the network information state, are constructed; the difference between the conditional entropies of these machines is then treated as an approximate measure of information contribution by the respective sensors. The x-Markov models represent the conditional temporal statistics given the network information state. The proposed method has been validated on experimental data collected from a local area network of passive sensors for target detection, where the statistical characteristics of environmental disturbances are similar to those of the target signal in the sense of time scale and texture. A distinctive feature of the proposed algorithm is that the network decisions are independent of the behavior and identity of the individual sensors, which is desirable from computational perspectives. Results are presented to demonstrate the proposed method's efficacy to correctly identify the presence of a target with very low false-alarm rates. The performance of the underlying algorithm is compared with that of a recent data-driven, feature-level information fusion algorithm. It is shown that the proposed algorithm outperforms the other algorithm.",
    author = "Yue Li and Jha, {Devesh K.} and Asok Ray and Wettergren, {Thomas A.}",
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    Information-Theoretic Performance Analysis of Sensor Networks via Markov Modeling of Time Series Data. / Li, Yue; Jha, Devesh K.; Ray, Asok; Wettergren, Thomas A.

    In: IEEE Transactions on Cybernetics, Vol. 48, No. 6, 01.06.2018, p. 1898-1909.

    Research output: Contribution to journalArticle

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