Decentralized Estimation in an Inhomogeneous Sensing Environment

Jian, Xi Gao; Luo, Zhi-Quan

doi:10.1109/tit.2005.855580

Cited by 216 publications

(171 citation statements)

References 14 publications

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“…It is easy to see that when the variable is exactly equal to a quantization centroid, there is zero probability of choosing another centroid. Therefore, it follows from [11] that the message…”

Section: N (T)} and A Set Of Edges E(t) ⊆ V(t) × V(t) An Edge In Gramentioning

confidence: 99%

“…The best known one is gossip-based algorithms which have attracted considerable recent attention [5]- [10]. In addition, limitations on the sensor cost, bandwidth, and energy budget dictate that information transmitted between sensors has to be quantized in practice [11]- [14]. However, to the best of the authors' knowledge, few authors have considered the distributed parameter estimation problem for unreliable WSNs by an asynchronous quantization communication framework so far.…”

Section: Introductionmentioning

confidence: 99%

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Distributed parameter estimation in unreliable sensor networks via broadcast gossip algorithms

et al. 2016

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Abstract-In this paper, we present an asynchronous algorithm to estimate the unknown parameter under an unreliable network which allows new sensors to join and old sensors to leave, and can tolerate link failures. Each sensor has access to partially informative measurements when it is awakened. In addition, the proposed algorithm can avoid the interference among messages and effectively reduce the accumulated measurement and quantization errors. Based on the theory of stochastic approximation, we prove that our proposed algorithm almost surely converges to the unknown parameter. Finally, we present a numerical example to assess the performance and the communication cost of the algorithm.Index Terms-Broadcast gossip algorithm, distributed parameter estimation, quantized communication, unreliable sensor network.

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Section: N (T)} and A Set Of Edges E(t) ⊆ V(t) × V(t) An Edge In Gramentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed parameter estimation in unreliable sensor networks via broadcast gossip algorithms

et al. 2016

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“…Sometimes, transmitting a real value is not convenient for relay nodes. Then g i has to be quantized before the transmission [9]. The quantization error must also be counted.…”

Section: Training Based Channel Estimationmentioning

confidence: 99%

Optimal Training Design for Channel Estimation in Amplify and Forward Relay Networks

Cui

Gao

Nallanathan

2007

IEEE GLOBECOM 2007-2007 IEEE Global Telecommunications Conference

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Abstract-In this paper, we study training based channel estimation for relay networks using the amplify-and-forward (AF) transmission scheme. We first point out that separately estimating the channel from source to relay and the channel from relay to destination incurs several problems. We then propose a new estimation scheme that directly estimates the overall channel from source to destination. The proposed channel estimation well matches the AF based space time coding that was developed recently. Both linear least-square estimator and minimum meansquare-error estimator are studied. The corresponding optimal training sequences and the optimal precoding matrices are also derived.

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“…Data fusion in WSNs has been studied extensively in, for instance, [2,17,[19][20][21]. Scheduling for WSNs has been extensively studied in, for example, [1,11].…”

Section: Introductionmentioning

confidence: 99%

On a tandem queue with batch service and its applications in wireless sensor networks

et al. 2017

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We present a tandem network of queues 0, . . . , s − 1. Customers arrive at queue 0 according to a Poisson process with rate λ. There are s independent batch service processes at exponential rates μ 0 , . . . , μ s−1 . Service process i, i = 0, . . . , s−1, at rate μ i is such that all customers of all queues 0, . . . , i simultaneously receive service and move to the next queue. We show that this system has a geometric product-form steady-state distribution. Moreover, we determine the service allocation that minimizes the waiting time in the system and state conditions to approximate such optimal allocations. Our model is motivated by applications in wireless sensor networks, where s observations from different sensors are collected for data fusion. We demonstrate that both optimal centralized and decentralized sensor scheduling can be modeled by our queueing model by choosing the values of μ i appropriately. We quantify the performance gap between the centralized and decentralized schedules for arbitrarily large sensor networks.

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Decentralized Estimation in an Inhomogeneous Sensing Environment

Cited by 216 publications

References 14 publications

Distributed parameter estimation in unreliable sensor networks via broadcast gossip algorithms

Distributed parameter estimation in unreliable sensor networks via broadcast gossip algorithms

Optimal Training Design for Channel Estimation in Amplify and Forward Relay Networks

On a tandem queue with batch service and its applications in wireless sensor networks

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