editorial resource-constrained signal processing in sensor...

EditorialResource-Constrained Signal Processing in Sensor Networks

Shuli Sun,1 Wendong Xiao,2 and Kung Yao3

1 School of Electronics Engineering, Heilongjiang University, Harbin 150080, China2 School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China3 Electrical Engineering Department, University of California, Los Angeles, CA 90095-1594, USA

Correspondence should be addressed to Shuli Sun; [email protected]

Received 13 February 2014; Accepted 13 February 2014; Published 17 March 2014

Copyright © 2014 Shuli Sun et al.This is an open access article distributed under theCreative CommonsAttribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Sensor network is composed of spatially distributed sensornodes to monitor the phenomenon of interest, where eachsensor node takes measurements and transmits them to thedata-processing center (fusion center). The main advantagesof sensor network include its low cost, rapid deployment,self-organization, flexibility, and fault tolerance. Sensor net-works, especially wireless sensor networks, have attractedsignificant research interests due to their wide applicationsand technological opportunities challenged by the limitedresources, such as the battery-powered node energy andwireless bandwidth. Usually wireless channel in the networkis also unreliable, which results in random transmissiondelays and packet losses when the sensor data are exchangedamong nodes or sent to the data-processing center.Moreover,the quantization or compression of the sensor data is desiredto tradeoff between the signal processing performance andthe required network resources. It is very important to designcollaborative signal processing algorithms and systems withnetwork and sensor resource management under the uncer-tain network environment featured with the delayed, lost,or/and quantized data.

The main focus of this special issue will be on the newresults of resource-constrained signal processing in sen-sor networks. It will provide an international platform forresearchers to summarize themost recent development in thefield. After a rigorous peer-reviewing process, 9 papers havebeen selected for publication. These papers cover the topicsincluding compressed sensing, data compression, distributedestimation, consensus, and tracking.

In the paper entitled “IDMA-based compressed sensingfor ocean monitoring information acquisition with sensor

networks” by G. Liu and W. Kang, an interleave-divisionmultiple-access- (IDMA-) based compressed sensing schemeis proposed for underwater sensor networks with applica-tions to underwater environmental monitoring. The pro-posed scheme consists of three components: data samplingwith randomly selected sensors, interleave-divisionmultiple-access of the sampled packets, and information recoverywith the successfully accessed measurements after chip-by-chip (CBC) multiuser detection (MUD). In the paperentitled “Data reduction with quantization constraints fordecentralized estimation in wireless sensor networks” by Y.Weng, the unknown vector estimation problem is consideredfor bandwidth constrained wireless sensor network. Due tothe power and communication bandwidth limitations, eachsensor node must compress its data and transmit them to thefusion center. Both centralized and decentralized estimationframeworks are developed. The closed-form solution for thecentralized estimation framework is proposed. The compu-tational complexity of decentralized estimation problem isproven to be NP-hard and a Gauss-Seidel algorithm is alsoproposed to search for an optimal solution. In the paperentitled “Weighted measurement fusion quantized filteringwith bandwidth constraints and missing measurements insensor networks” by J. Ding et al., the estimation problemof a dynamic stochastic variable in a sensor network isstudied, where the quantization of scalar measurement,the optimization of the bandwidth scheduling, and thecharacteristic of transmission channels are considered. Twoweighted measurement fusion (WMF) quantized Kalmanfilters based on the quantized measurements arriving at thefusion center are presented for the imperfect channels with

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2014, Article ID 756196, 2 pageshttp://dx.doi.org/10.1155/2014/756196

2 Mathematical Problems in Engineering

missing measurements in sensor networks. It is shown thatthey have the reduced computational cost and the sameaccuracy as the corresponding centralized fusion filter. Theapproximate solution for the optimal bandwidth-schedulingproblem is given under a limited bandwidth constraint. In thepaper entitled “Distributed fusion estimation for multisensormultirate systems with stochastic observation multiplicativenoises” by F. Peng and S. Sun, a distributed fusion estimationalgorithm is presented for a class of multisensor multiratesystems with observation multiplicative noises. Samplingperiod of each sensor is uniform and the integer multipleof the state updates period. Moreover, different sensors havedifferent sampling rates and observations of sensors aresubject to the stochastic uncertainties ofmultiplicative noises.In the paper entitled “Average consensus analysis of distributedinference with uncertain Markovian transition probability” byW. I. Kim et al., the average consensus problem is studiedfor the distributed inference in a wireless sensor networkunder the Markovian communication topology with uncer-tain transition probability. A sufficient condition is presentedfor the average consensus of linear distributed inferencealgorithm. Based on linear matrix inequalities and numericaloptimization, a designmethod is provided for fast distributedinference. In the paper entitled “IMM filter based humanmotion tracking using a wireless sensor network” by S. Zhangand W. Xiao, using low cost range wireless sensor nodes,an novel sensor selection algorithm is proposed for humantracking based on the interactingmultiplemodel filter (IMM)techniques and considering both the tracking accuracy andthe energy cost. In the paper entitled “Unknown clutter esti-mation by FMM approach in multitarget tracking algorithm”by N. Lv et al., a multitarget tracking algorithm based onclutter model estimation is proposed to deal with severebias caused by unknown and complex clutters. Multitar-get likelihood function is established with FMM. In thisframe, the algorithm of expectation maximum (EM) andMarkov Chain Monte Carlo (MCMC) are both consulted inFMM parameters estimation. Furthermore, target numberand multitarget states can be estimated precisely after theclutter model is fitted. In the paper entitled “Models andalgorithms for tracking target with coordinated turn motion”by X. Yuan et al., firstly a number of widely used models arecompared under the single model tracking framework, andthe suggestions on the choice of models for different practicaltarget tracking problems are given; then, in themultiplemod-els (MM) framework, the algorithm based on expectationmaximization (EM) algorithm is derived, including both thebatch form and the recursive form. In the paper entitled“Self-similarity super resolution for resource-constrained imagesensor node in wireless sensor networks” by Y. Wang et al.,a self-similarity super resolution with low computation costand high recovery performance is proposed. In the self-similarity image super resolution model, a small size sparsedictionary is learned from the image itself. The most similarpatch is searched and specially combined during the sparseregulation iteration to preserve the detailed information.

The guest editors hope that this special issue can providea snapshot of the latest advances in sensor networks andstimulatemore research interest and efforts in sensor network

research and development. They would like to acknowledgeall authors for their efforts in submitting high-quality papersand are also very grateful to the reviewers for their profes-sional contributions.

Shuli SunWendong Xiao

Kung Yao

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