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Estimation diversity and energy efficiency in distributed sensing
 IEEE Transactions on Signal Processing
, 2007
"... Abstract—Distributed estimation based on measurements from multiple wireless sensors is investigated. It is assumed that a group of sensors observe the same quantity in independent additive observation noises with possibly different variances. The observations are transmitted using amplifyandforw ..."
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Cited by 63 (1 self)
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Abstract—Distributed estimation based on measurements from multiple wireless sensors is investigated. It is assumed that a group of sensors observe the same quantity in independent additive observation noises with possibly different variances. The observations are transmitted using amplifyandforward (analog) transmissions over nonideal fading wireless channels from the sensors to a fusion center, where they are combined to generate an estimate of the observed quantity. Assuming that the best linear unbiased estimator (BLUE) is used by the fusion center, the equalpower transmission strategy is first discussed, where the system performance is analyzed by introducing the concept of estimation outage and estimation diversity, and it is shown that there is an achievable diversity gain on the order of the number of sensors. The optimal power allocation strategies are then considered for two cases: minimum distortion under power constraints; and minimum power under distortion constraints. In the first case, it is shown that by turning off bad sensors, i.e., sensors with bad channels and bad observation quality, adaptive power gain can be achieved without sacrificing diversity gain. Here, the adaptive power gain is similar to the array gain achieved in multipleinput singleoutput (MISO) multiantenna systems when channel conditions are known to the transmitter. In the second case, the sum power is minimized under zerooutage estimation distortion constraint, and some related energy efficiency issues in sensor networks are discussed. Index Terms—Distributed estimation, energy efficiency, estimation diversity, estimation outage.
Linear Coherent Decentralized Estimation
"... Abstract—We consider the distributed estimation of an unknown vector signal in a resource constrained sensor network with a fusion center. Due to power and bandwidth limitations, each sensor compresses its data in order to minimize the amount of information that needs to be communicated to the fusio ..."
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Cited by 47 (1 self)
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Abstract—We consider the distributed estimation of an unknown vector signal in a resource constrained sensor network with a fusion center. Due to power and bandwidth limitations, each sensor compresses its data in order to minimize the amount of information that needs to be communicated to the fusion center. In this context, we study the linear decentralized estimation of the source vector, where each sensor linearly encodes its observations and the fusion center also applies a linear mapping to estimate the unknown vector signal based on the received messages. We adopt the mean squared error (MSE) as the performance criterion. When the channels between sensors and the fusion center are orthogonal, it has been shown previously that the complexity of designing the optimal encoding matrices is NPhard in general. In this paper, we study the optimal linear decentralized estimation when the multiple access channel (MAC) is coherent. For the case when the source and observations are scalars, we derive the optimal power scheduling via convex optimization and show that it admits a simple distributed implementation. Simulations show that the proposed power scheduling improves the MSE performance by a large margin when compared to the uniform power scheduling. We also show that under a finite network power budget, the asymptotic MSE performance (when the total number of sensors is large) critically depends on the multiple access scheme. For the case when the source and observations are vectors, we study the optimal linear decentralized estimation under both bandwidth and power constraints. We show that when the MAC between sensors and the fusion center is noiseless, the resulting problem has a closedform solution (which is in sharp contrast to the orthogonal MAC case), while in the noisy MAC case, the problem can be efficiently solved by semidefinite programming (SDP). Index Terms—Distributed estimation, energy efficiency, multiple access channel, linear sourcechannel coding, convex optimization. I.
Estimation from misaligned observations with limited feedback
 In 39th Conference on Information Sciences and Systems (CISS 2005
, 2005
"... Abstract — In remote sensing applications with large numbers of sensors, the global correlation structure of the sensor observations may not be known locally at the sensors. However, some knowledge of this correlation structure can enable the sensors to communicate collaboratively to avoid interfere ..."
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Cited by 5 (2 self)
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Abstract — In remote sensing applications with large numbers of sensors, the global correlation structure of the sensor observations may not be known locally at the sensors. However, some knowledge of this correlation structure can enable the sensors to communicate collaboratively to avoid interference and combat channel noise. A model is proposed for the uncertainty in correlation structure, called a fading observation model. An example of multiplicative fading observations with Gaussian sources is analyzed. For this example, the endtoend distortion scales to 0 with the number of sensors M like M −1 without fading under one scheme, but with fading the same scheme induces a distortion that is bounded away from 0. A singlebit feedback scheme is proposed to align the sensor observations that yields a scaling rate of M −1/3. A more complicated class of fading observations for Gaussian sources is described that suggests that relative phase uncertainty is the dominant source of misalignment. I.
A Little Feedback can Simplify Sensor Network Cooperation
 IEEE J. SELECT. AREAS COMMUN
"... Shannon’s discovery of digital communication has shaped the architecture of virtually all communication systems in use today. The digital communication paradigm is built around the notion of bits and uses careful coding to deliver bits reliably endtoend. It has been shown that this architectural ..."
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Cited by 1 (0 self)
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Shannon’s discovery of digital communication has shaped the architecture of virtually all communication systems in use today. The digital communication paradigm is built around the notion of bits and uses careful coding to deliver bits reliably endtoend. It has been shown that this architectural principle can lead to a very significant performance penalty in wireless sensor networks. For a limited class of network scenarios, it was shown that optimal architectures are analog in nature, simple and scalable. In this paper, we show that more generally, simple analog architectures crucially depend on feedback to the sensors. Interesting questions then concern the amount of feedback needed and the resulting tradeoff with performance. This paper provides rulesofthumb for the selection of the number of feedback bits.