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Distributed compressionestimation using wireless sensor networks  The design goals of performance, bandwidth efficiency, scalability, and robustness
 IEEE SIGNAL PROCESSING MAG
, 2006
"... A wireless sensor network (WSN) consists of a large number of spatially distributed signal processing devices (nodes), each with finite battery lifetime and thus limited computing and communication capabilities. When properly programmed and networked, nodes in a WSN can cooperate to perform advance ..."
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Cited by 80 (1 self)
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A wireless sensor network (WSN) consists of a large number of spatially distributed signal processing devices (nodes), each with finite battery lifetime and thus limited computing and communication capabilities. When properly programmed and networked, nodes in a WSN can cooperate to perform advanced signal processing tasks with unprecedented robustness and versatility, thus making WSN an attractive lowcost technology for a wide range of remote sensing and environmental monitoring applications [1], [32].
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.
Decentralized estimation in an inhomogeneous sensing environment
 IEEE TRANS. INF. THEORY
, 2005
"... We consider decentralized estimation of a noisecorrupted deterministic parameter by a bandwidthconstrained sensor network with a fusion center. The sensor noises are assumed to be additive, zero mean, spatially uncorrelated, but otherwise unknown and possibly different across sensors due to varyi ..."
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Cited by 45 (9 self)
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We consider decentralized estimation of a noisecorrupted deterministic parameter by a bandwidthconstrained sensor network with a fusion center. The sensor noises are assumed to be additive, zero mean, spatially uncorrelated, but otherwise unknown and possibly different across sensors due to varying sensor quality and inhomogeneous sensing environment. The classical best linear unbiased estimator (BLUE) linearly combines the realvalued sensor observations to minimize the mean square error (MSE). Unfortunately, such a scheme cannot be implemented in a practical bandwidthconstrained sensor network due to its requirement to transmit realvalued messages. In this paper, we construct a decentralized estimation scheme (DES) where each sensor compresses its observation to a small number of bits with length proportional to the logarithm of its local signaltonoise ratio (SNR). The resulting compressed bits from different sensors are then collected and combined by the fusion center to estimate the unknown parameter. The proposed DES is universal in the sense that each sensor compression scheme requires only the knowledge of local SNR, rather than the noise probability distribution functions (pdf), while the final fusion step is also independent of the local noise pdfs. We show that the MSE of the proposed DES is within a constant factor of PS V of that achieved by the classical centralized BLUE estimator.
Multiterminal sourcechannel communication under orthogonal multiple access
 IEEE Trans. Inform. Theory
, 2005
"... We consider the problem of multiterminal sourcechannel communication where a number of distributed and possibly correlated sources are transmitted through an orthogonal multiple access channel to a common destination. We characterize the optimal tradeoff between the transmission cost Γ and the dist ..."
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Cited by 17 (2 self)
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We consider the problem of multiterminal sourcechannel communication where a number of distributed and possibly correlated sources are transmitted through an orthogonal multiple access channel to a common destination. We characterize the optimal tradeoff between the transmission cost Γ and the distortion vector D as measured against individual sources. Our approach consists of two steps: (1) a multipleletter characterization of the ratedistortion region for the multiterminal source coding; (2) a sourcechannel separation theorem ensuring that all achievable pairs of (Γ, D) can be obtained by combining the ratedistortion region and the orthogonal multiple access channel capacity region. As a corollary, we determine the optimal power and distortion tradeoff in a quadratic Gaussian sensor network under orthogonal multiple access, and show that separate source and channel coding strictly outperforms the uncoded (amplifyforward) transmission, and is in fact optimal in this case. This result is in sharp contrast to the case of nonorthogonal multiple access for which separate source and channel coding is not only suboptimal but also strictly inferior to uncoded transmission [11].
RateConstrained Distributed Estimation in Wireless Sensor Networks
"... Abstract—In this paper, we consider the distributed parameter estimation in wireless sensor networks where a total bit rate constraint is imposed. We study the optimal tradeoff between the number of active sensors and the quantization bit rate for each active sensor to minimize the estimation means ..."
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Cited by 12 (0 self)
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Abstract—In this paper, we consider the distributed parameter estimation in wireless sensor networks where a total bit rate constraint is imposed. We study the optimal tradeoff between the number of active sensors and the quantization bit rate for each active sensor to minimize the estimation meansquare error (MSE). To facilitate the solution, we first introduce a concept of equivalent 1bit MSE function. Next, we present an optimal distributed estimation algorithm for homogeneous sensor networks based on minimizing the equivalent 1bit MSE function. Then, we present a quasioptimal distributed estimation algorithm for heterogeneous sensor networks, which is also based on the equivalent 1bit MSE function, and the upper bound of the estimation MSE of the proposed algorithm is addressed. Furthermore, a theoretical nonachievable lower bound of the estimation MSE under the total bit rate constraint is stated and it is shown that our proposed algorithm is quasioptimal within a factor 2.2872 of the theoretical lower bound. Simulation results also show that significant reduction in estimation MSE is achieved by our proposed algorithm when compared with other uniform methods. Index Terms—Best linear unbiased estimator (BLUE), collaborative signal processing, distributed estimation, distributed signal processing, wireless sensor networks. I.
Power Scheduling for Wireless Sensor and Actuator
 Networks, Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN
, 2007
"... We previously presented a model for some wireless sensor and actuator network (WSAN) applications based on the vector space tools of frame theory. In this WSAN model there is a weight associated to each sensoractuator link denoting the importance of that communication link to the actuation fidelity ..."
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Cited by 7 (2 self)
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We previously presented a model for some wireless sensor and actuator network (WSAN) applications based on the vector space tools of frame theory. In this WSAN model there is a weight associated to each sensoractuator link denoting the importance of that communication link to the actuation fidelity. These weights were shown to be useful in pruning away communication links to reduce the number of active channels. Inspired by recent work in power scheduling for decentralized estimation, we investigate the optimal allocation of system resources for achieving a desired actuation fidelity. In this scheme, each sensor acquires a noisy observation and sends a message to a subset of actuators using an MQAM transmission strategy. The message sent on each sensoractuator communication link is quantized with a variable number of bits, with the number of bits optimized to minimize the total network power consumption subject to a constraint on the actuation distortion. We show analytically and verify through simulation that performing this optimal power scheduling can yield significant power savings over communication strategies that use a fixed number of bits on each communication link. 1.
Asymptotics and power allocation for state estimation over fading channels
 IEEE TRANS. AEROSP. ELECTRON. SYST
, 2011
"... State estimation of linear systems using analog amplify and forwarding with multiple sensors, for both multiple access and orthogonal access schemes is considered. Optimal state estimation can be achieved at the fusion center using a timevarying Kalman filter. We show that in many situations, the e ..."
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Cited by 7 (1 self)
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State estimation of linear systems using analog amplify and forwarding with multiple sensors, for both multiple access and orthogonal access schemes is considered. Optimal state estimation can be achieved at the fusion center using a timevarying Kalman filter. We show that in many situations, the estimation error covariance decays at a rate of 1=M when the number of sensors M is large. We consider optimal allocation of transmission powers that 1) minimizes the sum power usage subject to an error covariance constraint, and 2) minimizes the error covariance subject to a sum power constraint. In the case of fading channels with channelstate information, the optimization problems are solved using a greedy approach, while for fading channels without channel state information (CSI) but with channel statistics available, a suboptimal linear estimator is derived.
Energyefficient selective forwarding for sensor networks
 in Proc. Workshop on Energy in Wireless Sensor Networks (WEWSN’08), in conjunction with DCOSS’08
, 2008
"... Abstract—In this paper a new energyefficient scheme for data transmission in wireless sensor networks is proposed. It is based on the idea of selective forwarding: sensor nodes only transmit the most relevant messages, discarding the least important ones. To do so, messages are assumed to be graded ..."
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Cited by 4 (1 self)
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Abstract—In this paper a new energyefficient scheme for data transmission in wireless sensor networks is proposed. It is based on the idea of selective forwarding: sensor nodes only transmit the most relevant messages, discarding the least important ones. To do so, messages are assumed to be graded with an importance value, and a forwarding threshold, which depends on the sensor consumption patterns, the available energy resources and the information obtained from the neighborhood, is applied to these values. In this approach, the sensor decision also depends on the expected behavior of neighboring nodes, so as to maximize not only the transmission efficiency, but also the performance of the whole communication up to the destination node. Simulation results show that the proposed scheme increases the network lifetime, and maximizes the global importance of the messages received by the sink node. Index Terms—selective forwarding, energyefficiency, message importance, sensor networks
Minimizing transmit power for coherent communications in wireless sensor networks with finiterate feedback
 IEEE Trans. Signal Process
, 2008
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