Distributed Remote Vector Gaussian Source Coding for Wireless Acoustic Sensor Networks

In this paper, we consider the problem of remote vector Gaussian source coding for a wireless acoustic sensor network. Each node receives messages from multiple nodes in the network and decodes these messages using its own measurement of the sound field as side information. The node's measurement and the estimates of the source resulting from decoding the received messages are then jointly encoded and transmitted to a neighboring node in the network. We show that for this distributed source coding scenario, one can encode a so-called conditional sufficient statistic of the sources instead of jointly encoding multiple sources. We focus on the case where node measurements are in form of noisy linearly mixed combinations of the sources and the acoustic channel mixing matrices are invertible. For this problem, we derive the rate-distortion function for vector Gaussian sources and under covariance distortion constraints.Comment: 10 pages, to be presented at the IEEE DCC'1

Source Coding in Networks with Covariance Distortion Constraints

We consider a source coding problem with a network scenario in mind, and formulate it as a remote vector Gaussian Wyner-Ziv problem under covariance matrix distortions. We define a notion of minimum for two positive-definite matrices based on which we derive an explicit formula for the rate-distortion function (RDF). We then study the special cases and applications of this result. We show that two well-studied source coding problems, i.e. remote vector Gaussian Wyner-Ziv problems with mean-squared error and mutual information constraints are in fact special cases of our results. Finally, we apply our results to a joint source coding and denoising problem. We consider a network with a centralized topology and a given weighted sum-rate constraint, where the received signals at the center are to be fused to maximize the output SNR while enforcing no linear distortion. We show that one can design the distortion matrices at the nodes in order to maximize the output SNR at the fusion center. We thereby bridge between denoising and source coding within this setup

Distributed Remote Vector Gaussian Source Coding with Covariance Distortion Constraints

In this paper, we consider a distributed remote source coding problem, where a sequence of observations of source vectors is available at the encoder. The problem is to specify the optimal rate for encoding the observations subject to a covariance matrix distortion constraint and in the presence of side information at the decoder. For this problem, we derive lower and upper bounds on the rate-distortion function (RDF) for the Gaussian case, which in general do not coincide. We then provide some cases, where the RDF can be derived exactly. We also show that previous results on specific instances of this problem can be generalized using our results. We finally show that if the distortion measure is the mean squared error, or if it is replaced by a certain mutual information constraint, the optimal rate can be derived from our main result.Comment: This is the final version accepted at ISIT'1

Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks

The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks

Intelligent and Secure Underwater Acoustic Communication Networks

Underwater acoustic (UWA) communication networks are promising techniques for medium- to long-range wireless information transfer in aquatic applications. The harsh and dynamic water environment poses grand challenges to the design of UWA networks. This dissertation leverages the advances in machine learning and signal processing to develop intelligent and secure UWA communication networks. Three research topics are studied: 1) reinforcement learning (RL)-based adaptive transmission in UWA channels; 2) reinforcement learning-based adaptive trajectory planning for autonomous underwater vehicles (AUVs) in under-ice environments; 3) signal alignment to secure underwater coordinated multipoint (CoMP) transmissions. First, a RL-based algorithm is developed for adaptive transmission in long-term operating UWA point-to-point communication systems. The UWA channel dynamics are learned and exploited to trade off energy consumption with information delivery latency. The adaptive transmission problem is formulated as a partially observable Markov decision process (POMDP) which is solved by a Monte Carlo sampling-based approach, and an expectation-maximization-type of algorithm is developed to recursively estimate the channel model parameters. The experimental data processing reveals that the proposed algorithm achieves a good balance between energy efficiency and information delivery latency. Secondly, an online learning-based algorithm is developed for adaptive trajectory planning of multiple AUVs in under-ice environments to reconstruct a water parameter field of interest. The field knowledge is learned online to guide the trajectories of AUVs for collection of informative water parameter samples in the near future. The trajectory planning problem is formulated as a Markov decision process (MDP) which is solved by an actor-critic algorithm, where the field knowledge is estimated online using the Gaussian process regression. The simulation results show that the proposed algorithm achieves the performance close to a benchmark method that assumes perfect field knowledge. Thirdly, the dissertation presents a signal alignment method to secure underwater CoMP transmissions of geographically distributed antenna elements (DAEs) against eavesdropping. Exploiting the low sound speed in water and the spatial diversity of DAEs, the signal alignment method is developed such that useful signals will collide at the eavesdropper while stay collision-free at the legitimate user. The signal alignment mechanism is formulated as a mixed integer and nonlinear optimization problem which is solved through a combination of the simulated annealing method and the linear programming. Taking the orthogonal frequency-division multiplexing (OFDM) as the modulation technique, simulation and emulated experimental results demonstrate that the proposed method significantly degrades the eavesdropper\u27s interception capability

Distributed space–time cooperative schemes for underwater acoustic communications

Author Posting. © IEEE, 2008. This article is posted here by permission of IEEE for personal use, not for redistribution. The definitive version was published in IEEE Journal of Oceanic Engineering 33 (2008): 489-50, doi:10.1109/JOE.2008.2005338.In resource limited, large scale underwater sensor networks, cooperative communication over multiple hops offers opportunities to save power. Intermediate nodes between source and destination act as cooperative relays. Herein, protocols coupled with space-time block code (STBC) strategies are proposed and analyzed for distributed cooperative communication. Amplify-and-forward-type protocols are considered, in which intermediate relays do not attempt to decode the information. The Alamouti-based cooperative scheme proposed by Hua (2003) for flat-fading channels is generalized to work in the presence of multipath, thus addressing a main characteristic of underwater acoustic channels. A time-reversal distributed space-time block code (TR-DSTBC) is proposed, which extends the dual-antenna TR-STBC (time-reversal space-time block code) approach from Lindskog and Paulraj (2000) to a cooperative communication scenario for signaling in multipath. It is first shown that, just as in the dual-antenna STBC case, TR along with the orthogonality of the DSTBC essentially allows for decoupling of the vector intersymbol interference (ISI) detection problem into separate scalar problems, and thus yields strong performance (compared with single-hop communication) and with substantially reduced complexity over nonorthogonal schemes. Furthermore, a performance analysis of the proposed scheme is carried out to provide insight on the performance gains, which are further confirmed via numerical results based on computer simulations and field data experiments

Robust minimum energy wireless routing for underwater acoustic communication networks

Marine robots are an increasingly attractive means for observing and monitoring the ocean, but underwater acoustic communications remain a major challenge. The channel exhibits long delay spreads with frequency-dependent attenuation; moreover, it is time-varying. We consider the minimum energy wireless transmission problem [MET], augmented by the practical condition that constraints on link power must be satisfied in probability. For this, we formulate the robust counterpart of the multicommodity mixed-integer linear programming (MILP) model from Haugland and Yuan [1], and derive scaled power levels that account for uncertainty. Our main result is that the deterministic formulation with these scaled power levels recovers exactly the optimal robust solution in the absence of correlations, and therefore allows for efficient solution via MILP. This approach achieves significant power improvements over heuristics, and naturally lends itself to vehicle networks.United States. Office of Naval Research (Grant N00014-09-1-0700