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• W2096656644 abstract "We consider a distributed sensor network, modeled by the Chief Executive Officer (CEO) problem, in which sensors encode their observations without collaborating with each other and send through rate constrained noiseless channels to a fusion center (FC). We use the successive Wyner-Ziv coding strategy in this problem where sensors have differing quality of observations. We determine the optimal rate allocation scheme to obtain the minimum distortion under a sum-rate constraint. We show that the optimal sum-rate distortion performance for the Gaussian CEO problem is achievable using the successive coding strategy which is inherently a less complex way of obtaining a prescribed distortion. We also determine the achievable rate region and the optimal rate allocation region for the Gaussian CEO problem. We show that if the number of sensors tends to infinity while the sum-rate is finite, the performance of the successive coding strategy with equal rate sensors converges to the rate-distortion function. The same is true when the sum-rate tends to infinity with a finite number of sensors. Finally, we obtain the communication throughput of a Krelay network based on our results for the CEO problem. In many applications of wireless sensor networks, including environmental and structural monitoring, precision farming and remote sensing, a large number of sensors are deployed in a field to measure a physical phenomenon. Each sensor encodes its measurement and sends at a limited rate to a fusion center (FC) for further processing. The scenario is shown in Fig. 1. The key challenge in all these data gathering applications is conserving energy of distributed wireless sensor nodes and maximizing their lifetime. Since the sensor measurements are correlated, the correlation should be exploited to avoid redun- dant transmission. The behavior of wireless sensor networks can be modeled by the CEO problem. In the CEO problem defined in (1), L sensors observe independent noisy versions of the source signal X. Sensors communicate information about their observations to the FC through rate-constrained noiseless channels separately without collaborating. The FC desires to form an optimal estimate of X based on information received from the sensors. The CEO model is shown in Fig. 2. The objective of the CEO problem is to determine the minimum achievable distortion under a sum-rate constraint. By sum-rate, we mean the total rate at which the sensors may communicate information about their observations to the FC." @default.
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• W2096656644 title "Successively Structured Gaussian CEO Problem" @default.
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