Adaptive Quantization for Distributed Estimation in Energy-Harvesting Wireless Sensor Networks: A Game-Theoretic Approach

Liu, Hua; Liu, Guiyun; Liu, Yonggui; Mo, Lei; Chen, Hongbin

doi:10.1155/2014/217918

Cited by 8 publications

(9 citation statements)

References 24 publications

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“…Considering (11) we realize that there is a one-to-one correspondence between θ n and P d n . Also, considering (4) we realize that there is a one-to-one correspondence between µ n,l and π n,l .…”

Section: Letjmentioning

confidence: 99%

“…Assuming that each node performs amplify-andforward (AF) relaying toward the FC, and the FC applies maximum likelihood estimation, the author derived the optimal scaling factor at each node, such that the Fisher information of the unknown source is maximized. The authors in [11] proposed a game-theoretic approach to model the distributed estimation problem in an energy harvesting WSN, where each sensor (as a player in the game) desires to maximize its own utility, given the complete information about other players' utilities and strategies.…”

Section: A Literature Reviewmentioning

confidence: 99%

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Optimal Local Thresholds for Distributed Detection in Energy Harvesting Wireless Sensor Networks

Ardeshiri

Yazdani

Vosoughi

2018

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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We consider a wireless sensor network, consisting of K heterogeneous sensors and a fusion center (FC), that is tasked with solving a binary distributed detection problem. Each sensor is capable of harvesting and storing energy for communication with the FC. For energy efficiency, a sensor transmits only if the sensor test statistic exceeds a local threshold θ k , its channel gain exceeds a minimum threshold, and its battery state can afford the transmission. Our proposed transmission model at each sensor is motivated by the channel inversion power control strategy in the wireless communication community. Considering a constraint on the average energy of transmit symbols, we study the optimal θ k 's that optimize two detection performance metrics: (i) the detection probability PD at the FC, assuming that the FC utilizes the optimal fusion rule based on Neyman-Pearson optimality criterion, and (ii) Kullback-Leibler distance (KL) between the two distributions of the received signals at the FC conditioned by each hypothesis. Our numerical results indicate that θ k 's obtained from maximizing the KL distance are near-optimal. Finding these thresholds is computationally efficient, as it requires only K one-dimensional searches, as opposed to a K-dimensional search required to find the thresholds that maximize PD.

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Section: Letjmentioning

confidence: 99%

Section: A Literature Reviewmentioning

confidence: 99%

Optimal Local Thresholds for Distributed Detection in Energy Harvesting Wireless Sensor Networks

Ardeshiri

Yazdani

Vosoughi

2018

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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“…Energy and bandwidth efficiency algorithms on WSN have been widely investigated. Various signal processing methods have been proposed [2]. Prior study of energy and bandwidth efficiency on WSN assumes that the distribution of sensor noise observation is known [3].…”

Section: Introductionmentioning

confidence: 99%

Optimal Energy Allocation Scheme in Distributed Estimation for Wireless Sensor Networks over Rayleigh Fading Channels

Wardihani

2015

International Journal of Distributed Sensor Networks

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We focus on the issue of energy and bandwidth limitations in Wireless Sensor Networks (WSN). To tackle this issue, we propose the optimal energy allocation scheme and the optimal number of quantization bits by using alternating optimization method. Firstly, we determine the optimal energy allocation scheme which minimizes the reconstruction error on fusion center by keeping the number of quantization bits per sensor fixed. Secondly, we determine the optimal number of quantization bits per sensor such that the energy allocation scheme achieves the minimum reconstruction error. To find the optimal energy allocation scheme and the optimal number of quantization bits jointly, we propose an iterative algorithm. The results show that the proposed algorithms do not only achieve better results than equal energy allocation scheme but also produce reconstruction error close to that of unquantized estimation. This paper also investigates the effects of sensor noise observation and propagation losses on the design of optimal energy allocation scheme. The optimal energy allocation scheme suggests the allocation of more energy to sensors with small variance noise of sensor observation.

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“…We propose two simply repeated games instead of the extensive game [15] to meet the given estimation performance requirement. Different from the decentralized method [16][17][18], our game-theoretic approach is distributed and each node is selfish.…”

Section: Introductionmentioning

confidence: 99%

Repeated Game for Distributed Estimation in Autonomous Clustered Wireless Sensor Networks

Liu¹,

Liu²,

Chen³

et al. 2015

International Journal of Distributed Sensor Networks

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A commonly encountered problem in wireless sensor networks (WSNs) applications is to reconstruct the state of nature, that is, distributed estimation of a parameter of interest through WSNs’ observations. However, the distributed estimation in autonomous clustered WSNs faces a vital problem of sensors’ selfishness. Each sensor autonomously decides whether or not to transmit its observations to the fusion center (FC) and not be controlled by the fusion center (FC) any more. Thus, to encourage cooperation within selfish sensors, infinitely and finitely repeated games are firstly modeled to depict sensors’ behaviors. Then, the existences of Nash equilibriums for infinitely and finitely repeated games are discussed. Finally, simulation results show that the proposed Nash equilibrium strategies are effective.

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Adaptive Quantization for Distributed Estimation in Energy-Harvesting Wireless Sensor Networks: A Game-Theoretic Approach

Cited by 8 publications

References 24 publications

Optimal Local Thresholds for Distributed Detection in Energy Harvesting Wireless Sensor Networks

Optimal Local Thresholds for Distributed Detection in Energy Harvesting Wireless Sensor Networks

Optimal Energy Allocation Scheme in Distributed Estimation for Wireless Sensor Networks over Rayleigh Fading Channels

Repeated Game for Distributed Estimation in Autonomous Clustered Wireless Sensor Networks

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