Power and bandwidth are scarce resources in dense wireless sensor networks and it is widely recognized that joint optimization of the operations of sensing, processing and communication can result in significant savings in the use of network resources. In this paper, a distributed joint source-channel communication architecture is proposed for energy-efficient estimation of sensor field data at a distant destination and the corresponding relationships between power, distortion, and latency are analyzed as a function of number of sensor nodes. The approach is applicable to a broad class of sensed signal fields and is based on distributed computation of appropriately chosen projections of sensor data at the destination – phase-coherent transmissions from the sensor nodes enable exploitation of the distributed beamforming gain for energy efficiency. Random projections are used when little or no prior knowledge is available about the signal field. Distinct features of the proposed scheme include: 1) processing and communication are combined into one distributed projection operation; 2) it virtually eliminates the need for innetwork processing and communication; 3) given sufficient prior knowledge about the sensed data, consistent estimation is possible with increasing sensor density even with vanishing total network power; and 4) consistent signal estimation is possible with power and latency requirements growing at most sub-linearly with the number of sensor nodes even when little or no prior knowledge about the sensed data is assumed at the sensor nodes.

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 sensor-actuator 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 sensor-actuator 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.

Abstract—Source extraction was traditionally done by sensor arrays. Recently, sensor networks have been considered as promising candidates for extraction of multiple sources. In a sensor network, each sensor observes an instantaneous linear mixture of the sources and their observations are corrupted by additive white Gaussian noise. Two sensor network models are adopted. The first one is cluster based, in which a sensor acts as cluster head and performs local extraction of the sources based on its own observation and the received quantized data from the cluster members. Then, the extracted signal is quantized and the quantized data are sent to the sink while the sink performs global extraction of the sources. The other one is cluster free, in which data collected by the sensors are quantized and sent to the sink directly. Then, the sink performs global extraction of the sources. The proposed schemes are evaluated against the benchmarking case where the sensor observations are undistorted. Index Terms—Blind source extraction, distributed estimation, wireless sensor network. I.

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A typical wireless sensor network consists of sensors powered by small batteries that are difficult to replace if not impossible. Hence, the sensor nodes can only transmit a finite number of bits before they run out of energy. Thus, reducing the energy consumption per bit for end-to-end data transmission is an important design consideration for such networks. We assume that each information bit collected by a sensor is useful for a finite amount of time; after this time the information may become irrelevant. Hence all the bits collected by the sensors need to be communicated to a hub node before a certain deadline. Therefore, the maximum endto-end transmission delay for each bit must be controlled to meet a given deadline under the hard energy constraint. Since all layers of the protocol stack affect the energy consumption and delay for the end-to-end transmission of each bit, an

Abstract—This paper addresses the joint estimator and power optimization problem for a sensor network whose mission is to estimate an unknown parameter. We assume a two-hop network where each sensor collects observations from the source that transmits the quantity to be estimated, then amplifies and forwards its observations to a fusion center. The fusion center combines the observations using a Linear Minimum Mean Squared Error (LMMSE) estimator. We study the scenario where multiple parallel channels are available between the source and each sensor as well as between the sensors and the fusion center. We find the global optimal power allocation and estimator design for this network model. We present two practical scenarios of interest that utilize spatial and temporal diversity for which this solution applies, namely, a clustered network model and a single cluster model with an ergodic fading channel. I.

A typical wireless sensor network consists of sensors powered by small batteries that are difficult to replace if not impossible. Hence, the sensor nodes can only transmit a finite number of bits before they run out of energy. Thus, reducing the energy consumption per bit for end-to-end data transmission is an important design consideration for such networks. We assume that each information bit collected by a sensor is useful for a finite amount of time; after this time the information may become irrelevant. Hence all the bits collected by the sensors need to be communicated to a hub node before a certain deadline. Therefore, the maximum endto-end transmission delay for each bit must be controlled to meet a given deadline under the hard energy constraint. Since all layers of the protocol stack affect the energy consumption and delay for the end-to-end transmission of each bit, an