Distributed inference in wireless sensor networks

Veeravalli, Venugopal V.; Varshney, Pramod K.

doi:10.1098/rsta.2011.0194

Cited by 109 publications

(66 citation statements)

References 126 publications

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“…Our results for QAM can be viewed as a generalization of classical results on the hard decision penalty for binary communication in [1]- [3], while our results for larger PSK alphabets draw on the relatively recent low-SNR analysis in [4]. Prior work: There is a large body of related literature dating back more than three decades on the broad subjects of "multiterminal inference" [5]- [7], "distributed hypothesis testing" [8]- [10], and "distributed detection" [11]- [15]. In general, the setting in these problems is to have multiple agents forward quantized observations to a fusion center which then applies a detection or estimation rule according to a certain performance objective.…”

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confidence: 99%

Distributed Reception with Hard Decision Exchanges

Brown

Madhow

et al. 2014

IEEE Trans. Wireless Commun.

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Abstract-This paper considers the problem of jointly processing messages received over a forward link from a single distant transmitter to a cooperative receive cluster connected by a local area network with finite available throughput. For N cooperating receivers, ideal distributed receive beamforming with direct exchange of unquantized observations leads to an Nfold gain in signal-to-noise ratio (SNR) for equal-gain additive white Gaussian noise channels, with significant additional gains over fading channels due to diversity. It is shown in this paper that a significant portion of these gains can be obtained simply by exchanging hard decisions among some or all of the nodes in the receive cluster. Mutual information computations and simulations of LDPC-coded systems show that optimal combining of hard decisions tends to perform within 0.5-2 dB of ideal receive beamforming. For the low per-node SNR regime of interest with large receive clusters, asymptotic analysis of a suboptimal combining technique termed "pseudo-beamforming" shows that distributed reception with hard decision exchanges performs within 1-2 dB of ideal receive beamforming.

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confidence: 99%

Distributed Reception with Hard Decision Exchanges

Brown

Madhow

et al. 2014

IEEE Trans. Wireless Commun.

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“…Since many practical channel models such as path-loss model and Rayleigh fading model have non-negative channel gains, we assume that b is a non-negative vector in the rest of this section. 1 Since this is a likelihood ratio test, all the other rules are dominated. Therefore, their removal does not result any loss in network performance.…”

Section: Lemmamentioning

confidence: 99%

“…Jamming attacks in detection networks have a significant impact on today's world due to the wide range of applications of these networks [1]. Therefore, several attempts have been made in the past literature to address jamming attacks in detection networks.…”

Section: Introduction and System Modelmentioning

confidence: 99%

On Strategic Multi-Antenna Jamming in Centralized Detection Networks

Nadendla

Sharma

Varshney

2017

IEEE Signal Process. Lett.

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Abstract-In this paper, we model a complete-information zero-sum game between a centralized detection network with a multiple access channel (MAC) between the sensors and the fusion center (FC), and a jammer with multiple transmitting antennas. We choose error probability at the FC as the performance metric, and investigate pure strategy equilibria for this game, and show that the jammer has no impact on the FC's error probability by employing pure strategies at the Nash equilibrium. Furthermore, we also show that the jammer has an impact on the expected utility if it employs mixed strategies.

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“…Thus, to prove thatŴ is a primitive matrix, it is sufficient 3 to show thatŴ has a single eigenvalue with maximum modulus of 1. In [27], the authors showed that when…”

Section: ) All Eigenvalues Ofŵ Are In a Unit Circle;mentioning

confidence: 99%

“…Distributed detection is a well studied topic in the detection theory literature [1]- [3]. The traditional distributed detection framework comprises of a group of spatially distributed nodes which acquire the observations regarding the phenomenon of interest and send them to the fusion center (FC) where a global decision is made.…”

Section: Introductionmentioning

confidence: 99%

Data Falsification Attacks on Consensus-Based Detection Systems

Kailkhura

Brahma

Varshney

2017

IEEE Trans. on Signal and Inf. Process. over Networks

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This paper considers the problem of detection in distributed networks in the presence of data falsification (Byzantine) attacks. Detection approaches considered in the paper are based on fully distributed consensus algorithms, where all of the nodes exchange information only with their neighbors in the absence of a fusion center. In such networks, we characterize the negative effect of Byzantines on the steady-state and transient detection performance of the conventional consensus based detection algorithms. To address this issue, we study the problem from the network designer's perspective. More specifically, we first propose a distributed weighted average consensus algorithm that is robust to Byzantine attacks. We show that, under reasonable assumptions, the global test statistic for detection can be computed locally at each node using our proposed consensus algorithm. We exploit the statistical distribution of the nodes' data to devise techniques for mitigating the influence of data falsifying Byzantines on the distributed detection system. Since some parameters of the statistical distribution of the nodes' data might not be known a priori, we propose learning based techniques to enable an adaptive design of the local fusion or update rules.

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Distributed inference in wireless sensor networks

Cited by 109 publications

References 126 publications

Distributed Reception with Hard Decision Exchanges

Distributed Reception with Hard Decision Exchanges

On Strategic Multi-Antenna Jamming in Centralized Detection Networks

Data Falsification Attacks on Consensus-Based Detection Systems

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