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The path-loss exponent (PLE) is a key parameter in wireless propagation channels. Therefore, obtaining the knowledge of the PLE is rather significant for assisting wireless communications and networking to achieve a better performance. Most existing methods for estimating the PLE not only require nodes with known locations but also assume an omni-directional PLE. However, the location information might be unavailable or unreliable and, in practice, the PLE might change with the direction.

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This work examines the charging power allocation and beam selection problem for distributed estimation in wireless passive sensor networks, where the sensors are charged over the air by RF-enabled energy sources. A two-phase replenishment and transmission cycle is considered. In the replenishment phase, each wireless charger emits power over the air through a carefully selected beam and power to replenish the wireless sensors.

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This paper investigates the use of wireless power transfer (WPT) for {measurement sensing} and information transmission in a wireless sensor network (WSN) performing distributed parameter estimation using an adaptive diffusion least mean-squares (LMS) strategy. We consider a hybrid WSN consisting of common sensor nodes (CNs) and super sensor nodes (SNs) that are capable of WPT. In each diffusion iteration, all nodes sense measurements and exchange parameter estimates with their neighbors. Each SN also transfers wireless power to its neighboring CNs via beamforming.

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In this paper, we propose a novel energy harvesting (EH)-aware sensor selection policy. Our goal is to minimize the distortion in the reconstruction of the underlying source subject to the causality constraints imposed by the EH process at the sensor nodes. Besides, we determine the optimal power allocation for a given sensor selection (which admits a two-dimensional directional waterfilling interpretation) as the solution of an offline convex optimization problem. To that aim, we propose an iterative procedure.

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Distributed filter in networks mainly involves two stages, local estimation by private observation and information fusion with neighbor nodes based on the underlying topology. Since Bayesian game is a powerful tool to analyze the interaction equilibrium of multi-player with incomplete information in networks, we combine the recursive LMMSE filter with network game of quadratic utilities under the Bayesian filtering framework. In our algorithm, the nodes update their local beliefs on the unknown state by private observations and historical actions from neighbors in network.

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This paper addresses channel-robust compressed sensing (CS) acquisition of sparse sources under complexity-constrained encoding over noisy channels in wireless sensor networks. We propose a single-sensor joint source-channel coding method based on channel-optimized vector quantization by designing a CS-aware encoder-decoder pair to minimize the end-to-end mean square error (MSE) distortion of the signal reconstruction. As our key target is to obtain tolerable encoding complexity at the resource-limited sensor, the method relies on vector pre-quantization of the measurement space.

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Telemonitoring of biosignals is a growing area of research due to the aging world population. Telemonitoring utilizes a wireless body-area network (WBAN) consisting of wearable biosignal sensors equipped with ultra low power radios. The measured data from each sensor on the patient is sent to a smartphone, which then sends the data to a healthcare provider via the internet. To enable real-time telemonitoring of the biosignals, it is desirable to have accurate timestamped data from the sensors in the WBAN.

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