Ordering for Reduced Transmission Energy Detection in Sensor Networks Testing a Shift in the Mean of a Gaussian Graphical Model

Zhang, Jiangfan; Chen, Zhongyue; Blum, Rick S.; Lu, Xuanxuan; Xu, Wen

doi:10.1109/tsp.2017.2655492

Cited by 11 publications

(14 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that for all k > 1, there is a j < k such that S k ⊆ C j for any decomposable undirected graph G. A mapping q : {2, 3, ..., K} → {1, 2, ...., K} is defined to specify an association between each separator set and some unique cliques such that [5] q (k)…”

Section: A Decomposable Gaussian Graphical Modelsmentioning

confidence: 99%

“…for any decomposable undirected graph G. Let Q j denote the set of indices of the separators which are associated with the j-th clique via the mapping q in (5), that is,…”

Section: A Decomposable Gaussian Graphical Modelsmentioning

confidence: 99%

“…Proof: The proof of this theorem is omitted, since it follows the proof in [5]. The difference from [5] is that we need to employ P D,k (δ (1) ) in (28) and P f,k (δ (0) ) in (29) which are completely different for this new problem. The other difference from [5] is that we add zero as a choice in (32) since the average number of transmissions saved K s makes sense when it is non-negative in reality.…”

Section: B Lower Bound On the Average Number Of Transmissions Savedmentioning

confidence: 99%

“…Great attention has been devoted to distributed detection in sensor networks for both military and civilian applications, such as security, tactical surveillance, defense operations, disaster prediction and health care monitoring [1]. However, since spatially distributed sensor nodes have limited battery capacity, energy efficiency is an important topic [2] [3] [4] [5]. Recently, for the shift-in-mean detection problem, [4] proposed an approach where transmissions can be ordered and halted before all transmissions have taken place based on the assumption that the observations are independent and identically distributed.…”

Section: Introductionmentioning

confidence: 99%

“…However, covariance matrix testing problems employing ordered transmissions have not been addressed yet. This paper differs from the previous work on ordered transmissions for the shift-in-mean problem [4] [5], and focuses on problems testing the covariance matrix [6] [7] by employing a Gaussian Graphical Model (GGM) formulation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Testing the Structure of a Gaussian Graphical Model With Reduced Transmissions in a Distributed Setting

Chen

Blum

Sadler

et al. 2019

IEEE Trans. Signal Process.

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Testing a covariance matrix following a Gaussian graphical model (GGM) is considered in this paper based on observations made at a set of distributed sensors grouped into clusters. Ordered transmissions are proposed to achieve the same Bayes risk as the optimum centralized energy unconstrained approach but with fewer transmissions and a completely distributed approach. In this approach, we represent the Bayes optimum test statistic as a sum of local test statistics which can be calculated by only utilizing the observations available at one cluster. We select one sensor to be the cluster head (CH) to collect and summarize the observed data in each cluster and intercluster communications are assumed to be inexpensive. The CHs with more informative observations transmit their data to the fusion center (FC) first. By halting before all transmissions have taken place, transmissions can be saved without performance loss. It is shown that this ordering approach can guarantee a lower bound on the average number of transmissions saved for any given GGM and the lower bound can approach approximately half the number of clusters when the minimum eigenvalue of the covariance matrix under the alternative hypothesis in each cluster becomes sufficiently large.

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Section: A Decomposable Gaussian Graphical Modelsmentioning

confidence: 99%

“…for any decomposable undirected graph G. Let Q j denote the set of indices of the separators which are associated with the j-th clique via the mapping q in (5), that is,…”

Section: A Decomposable Gaussian Graphical Modelsmentioning

confidence: 99%

Section: B Lower Bound On the Average Number Of Transmissions Savedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Testing the Structure of a Gaussian Graphical Model With Reduced Transmissions in a Distributed Setting

Chen

Blum

Sadler

et al. 2019

IEEE Trans. Signal Process.

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A Time-Varying Opportunistic Multiple Access for Delay-Sensitive Inference in Wireless Sensor Networks

Cohen

Malachi

2019

IEEE Access

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We consider distributed transmission scheduling for inference over multiple access channels (MAC) using a wireless sensor network (WSN). The sensors transmit their data simultaneously using common shaping waveforms through finite-state Markovian fading channels, and the fusion center (FC) receives a superposition of the analog transmitted signals. The inference task is computed by the FC and is based on data received from the sensors. We study the case of delay-sensitive inference, where each sensor must schedule its transmission in one of D consecutive time slots. The essence of the problem is to schedule transmissions by exploiting the channel diversity over time slots to minimize the expected transmission energy consumed during the inference task. We formulate the transmission scheduling problem as a finite-horizon Markov decision process (MDP) with a continuous state space. By judiciously exploiting the inherent structure of the associated dynamic programming (DP) problem, we prove that the optimal solution obeys a time-varying threshold-based policy with low complexity (thus avoiding the general intractable complexity of DP with the problem size). We then establish a novel Time-varying Opportunistic Multiple Access (TOMA) protocol based on the structured DP solution. INDEX TERMSDynamic programming (DP), inference over multiple access channels (MAC), Markov decision process (MDP), MAC protocols, wireless sensor networks (WSNs). KOBI COHEN received the B.Sc. and Ph.D. degrees in electrical engineering from Bar-

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Subgradient Descent Learning Over Fading Multiple Access Channels With Over-the-Air Computation

Gez

Cohen

2023

IEEE Access

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A short version of this paper that introduces the algorithm, and preliminary simulation results was accepted for presentation at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2023 [1]. In this journal version we include: (i) A detailed discussion on the implementation of the algorithm; (ii) a rigorous theoretical analysis of the algorithm with detailed proofs; (iii) more extensive simulation results; and (iv) a detailed discussion of the results, and comprehensive discussion and comparison with the existing literature.

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Ordering for Reduced Transmission Energy Detection in Sensor Networks Testing a Shift in the Mean of a Gaussian Graphical Model

Cited by 11 publications

References 28 publications

Testing the Structure of a Gaussian Graphical Model With Reduced Transmissions in a Distributed Setting

Testing the Structure of a Gaussian Graphical Model With Reduced Transmissions in a Distributed Setting

A Time-Varying Opportunistic Multiple Access for Delay-Sensitive Inference in Wireless Sensor Networks

Subgradient Descent Learning Over Fading Multiple Access Channels With Over-the-Air Computation

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