Editorial
Erchin Serpedin, Hongbin Li, Aleksandar Dogandžić, Huaiyu Dai, and Paul Cotae
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TXDistributed Signal Processing Techniques for Wireless Sensor
Networks
77843, USA
Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken,
NJ 07030, USA
Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011,
USA
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh,
NC 27695, USA
Department of Electrical and Computer Engineering, University of Texas at San Antonio, San
Antonio, TX 78249, USA
Recent advances in micro electromechanical systems (MEMS) technology have enabled
the design of low-power low-cost smart sensors equipped with multiple onboard
functions such as sensing, computing and communications. Such intelligent devices
networked through wireless links have been referred to as wireless sensor networks and
recognized as one of the most important technologies for the 21st century. Wireless
sensor networks hold the promise to revolutionize the sensing technology for a broad
spectrum of applications, including infrastructure monitoring and surveillance, disaster
management, monitoring the health status of humans, plants, animals and industrial
machines, etc.
Wireless sensor networks can be viewed as a special case of wireless ad hoc networks,
and assume a multi-hop communication framework with no centralized infrastructure and
where the sensors cooperate spontaneously by forwarding each other's packets for
delivery from a source to a destination node. The multi-hop nature of sensor networks is
imposed by energy-consumption reasons because of the super-linear power loss of
wireless transmissions with respect to the propagation distance.
In general, the design of wireless sensor networks is subjected to a number of challenges:
low energy consumption which manifests in minimal energy expenditure in each sensor
node and efficient usage of power-saving sleep/wake-up modes, scalability in the
presence of a large number of sensors, possibility of frequent node failures and network
topology changes, collaborative signal processing and data aggregation techniques to
cope with the large number of sensors which might congest the network with information,
and efficient communication protocols to deal with the special broadcast communication
paradigm and the increased possibility of packet collisions and congestions for nodes
operating in closely spaced transmission ranges.
The scope of this special issue was to present the state-of-the-art and emerging
distributed signal processing techniques that deal with some of the above-mentioned
design challenges. This special issue consists of seven papers that treat important signal
processing aspects such as compression, quantization, estimation, detection,
synchronization and localization in wireless sensor networks. A short description of the
contributions brought by these papers is next presented.
In the paper ``Energy-Constrained Optimal Quantization for Wireless Sensor Networks”,
X. Luo and G. B. Giannakis deal with the important problem of designing efficient
quantizers that ensure optimal reconstruction at the fusion center of the measurements
yielded by a sensor as well as the estimation of a deterministic parameter by exploiting
the measurements collected by a set of sensors. The design is carried out under power
constraints and information such as channel propagation effects, modulation, and energy
consumed by transceiver circuitry is considered into the analysis. The effect of channel
coding on the reconstruction performance is also studied, and the optimum number of
quantization bits and energy levels are derived.
The problem of designing an optimal-level distributed transform for wavelet based
spatio-temporal data compression in wireless sensor networks is addressed by S. Zhou et
al. in the paper ``Ring Based Optimal-Level Distributed Wavelet Transform With
Arbitrary Filter Length For Wireless Sensor Networks.” This paper proposes a distributed
optimal-level spatio-temporal compression algorithm based on the ring model for general
wavelets with arbitrary supports. The proposed compression algorithm accommodates a
broad range of wavelet functions, effectively exploits the temporal and spatial correlation
of data measurements, and achieves significant reduction in energy consumption and
delay for data gathering in sensor clusters.
In the paper `` Distortion-Rate Bounds for Distributed Estimation using Wireless Sensor
Networks,” D. Schizas et al. address the problem of centralized and distributed rateconstrained
estimation of random signal vectors by exploiting a network of wireless
sensors (encoders) that communicate with a fusion center (decoder). Within the proposed
framework, the authors of this paper determine lower and upper bounds on the
corresponding distortion-rate (D-R) function using both centralized as well as distributed
estimation techniques.
The paper ``Distributed Event Region Detection in Wireless Sensor Networks,” coauthored
by J. Fang and H. Li, proposes a graph-based method for distributed eventregion
detection in wireless sensor networks. The proposed detection scheme exploits a
graphical model to take into account the fact that events occurring in geographically
neighboring sensors present a statistical dependency. The proposed detection scheme
admits also energy and bandwidth efficient distributed implementations.
Q. Chaudhari and E. Serpedin, in the paper ``Clock Estimation for Long-Term
Synchronization in Wireless Sensor Networks with Exponential Delays,” deal with the
maximum likelihood estimation of the clock parameters (phase, skew, and drift) in twoway
timing exchange mechanisms and in networks with exponentially distributed delays.
The paper entitled ``Extension of Pairwise Broadcast Clock Synchronization for Multi-
Cluster Sensor Networks,” co-authored by K. L. Noh et al., proposes a novel clock
synchronization protocol to minimize the overall energy consumption in wireless sensor
networks that assume general multi-cluster topologies. The proposed synchronization
approach relies on a receiver-only synchronization approach and it can be viewed as a
generalization of the Pairwise Broadcast Synchronization (PBS) protocol. Like PBS, the
proposed synchronization approach exhibits the distinct advantage that the number of
sensor nodes can be synchronized by only over-hearing time message exchanges between
pairs of nodes, and therefore it reduces significantly the overall network-wide energy
consumption by decreasing the number of required timing messages for synchronization.
Finally, in the paper ``Optimization of sensor locations and sensitivity analysis for engine
health monitoring using minimum interference algorithms,” P. Cotae et al. address the
problem of optimal placement of sensors in the presence of additive white Gaussian noise
(AWGN) by considering the sensors as systems that present full communications
capabilities and by minimizing the RF-interference induced by the wireless
communication channels among the sensor nodes. Numerical simulations and a
sensitivity analysis study are presented to illustrate the robustness of the proposed
algorithm.
The editors of this special issue would like to express their heartfelt ``Thank You!” to all
the people (editors, authors, and reviewers) who supported the publication of this special
issue.
Erchin Serpedin received (with highest distinction) the Diploma of Electrical
Engineering from the Polytechnic Institute of Bucharest, Bucharest, Romania, in 1991.
He received the specialization degree in signal processing and transmission of
information from Ecole Superieure D'Electricite, Paris, France, in 1992, the M.Sc. degree
from Georgia Institute of Technology, Atlanta, GA, in 1992, and the Ph.D. degree in
Electrical Engineering from the University of Virginia, Charlottesville, VA, in January
1999. In July 1999, he joined Texas A&M University in College Station, as an assistant
professor, and where currently holds the position of associate professor. His research
interests lie in the areas of signal processing, bioinformatics and telecommunications. He
received the NSF Career Award in 2001, the CCCT 2004 Best Conference Award, the
Outstanding Faculty Award in 2004, NRC Fellow Award in 2005, and TEES Award in
2005. He is currently serving as an associate editor for the IEEE Communications Letters,
IEEE Transactions on Signal Processing, IEEE Transactions on Communications, IEEE
Transactions on Wireless Communications, EURASIP Journal on Advances in Signal
Processing and EURASIP Journal on Bioinformatics and Systems Biology. Dr. Serpedin
served also as a technical co-chair of the Communications Theory Symposium at
Globecom 2006 Conference, and VTC Fall 2006: Wireless Access Track.
Hongbin Li received the B.S. and M.S. degrees from the University of Electronic
Science and Technology of China, Chengdu, in 1991 and 1994, respectively, and the
Ph.D. degree from the University of Florida, Gainesville, in 1999, all in electrical
engineering. From July 1996 to May 1999, he was a Research Assistant with the
Department of Electrical and Computer Engineering, University of Florida. He was a
Summer Visiting Faculty Member of the Air Force Research Laboratory, Rome, NY, in
summers 2003 and 2004. Since July 1999, he has been with the Department of Electrical
and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, where he is
an Associate Professor. His current research interests include wireless communications
and networking, statistical signal processing, and radars. Dr. Li is a member of Tau Beta
Pi and Phi Kappa Phi. He received the Harvey N. Davis Teaching Award in 2003 and the
Jess H. Davis Memorial Award for excellence in research in 2001 from Stevens Institute
of Technology, and the Sigma Xi Graduate Research Award from the University of
Florida in 1999. He is a member of the Sensor Array and Multichannel (SAM) Technical
Committee of the IEEE Signal Processing Society. He is an Associate Editor for the
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (1/2003 to 12/2006),
the IEEE SIGNAL PROCESSING LETTERS (1/2005 to 12/2006), and the IEEE
TRANSACTIONS ON SIGNAL PROCESSING (10/2006 to present), and serves as a
Guest Editor for EURASIP Journal on Applied Signal Processing Special Issue on
Distributed Signal Processing Techniques for Wireless Sensor Networks.
Aleksandar Dogandzic received the Dipl. Ing. degree (summa cum laude) in electrical
engineering from the University of Belgrade, Yugoslavia, in 1995, and the M.S. and Ph.D.
degrees in electrical engineering and computer science from the University of Illinois at
Chicago (UIC) in 1997 and 2001, respectively, under the guidance of Prof. A. Nehorai. In
August 2001, he joined the Department of Electrical and Computer Engineering, Iowa
State University, Ames, as an Assistant Professor. His research interests are in statistical
signal processing theory and applications. Dr. Dogandzic received the Distinguished
Electrical Engineering M.S. Student Award by the Chicago Chapter of the IEEE
Communications Society in 1996. Hewas awarded the Aileen S. AndrewFoundation
Graduate Fellowship in 1997, the UIC University Fellowship in 2000, and the 2001
Outstanding Thesis Award in the Division of Engineering, Mathematics, and Physical
Sciences, UIC. He is the recipient of the 2003 Young Author Best Paper Award and 2004
Signal Processing Magazine Award by the IEEE Signal Processing Society.
Huaiyu Dai received the B.E. and M.S. degrees in electrical engineering from Tsinghua
University, Beijing, China, in 1996 and 1998, respectively, and the Ph.D. degree in
electrical engineering from Princeton University, Princeton, NJ, in 2002.He was with
Bell Labs, Lucent Technologies, Holmdel, NJ, during summer 2000, and with AT&T
Labs-Research, Middletown, NJ, during summer 2001. Currently, he is an Assistant
Professor of Electrical and Computer Engineering at North Carolina State University,
Raleigh. His research interests are in the general areas of communication systems and
networks, advanced signal processing for digital communications, and communication
theory and information theory. His current research focuses on distributed signal
processing and crosslayer design (with a physical layer emphasis) in wireless ad hoc and
sensor networks, distributed, multicell, multiuser MIMO communications, and associated
information-theoretic and computation-theoretic analysis.
Paul Cotae was born in Falticeni, Romania, on June 21, 1955. He received the Dipl.Ing.
and M.S. degrees in communication and electronic engineering from the Technical
University of Iassy, Iasi, Romania, in 1980 and the Ph.D. degree in telecommunications
from Politechnica University of Bucharest, Bucharest, Romania, in 1992. Since 1984, he
has been with the Department of Electrical Engineering, Technical University of Iassy,
where he conducted research and teaching in the area of digital communications as a Full
Professor. From 1994 to 1998, he spent four years in the USA at the University of
Colorado at Colorado Springs and Boulder as a Fulbright Scholar and Visiting Associate
Professor, where he did research and teaching with the Electrical and Computer
Engineering Department and Applied Mathematics Department, respectively. He also
served as a Consultant to Navsys Corporation, Colorado Springs, in 1997. Currently, he
is with the University of Texas, San Antonio. His current research interests include
multiple access, modulation and coding, mobile communications, and digital
communication systems. He has authored or coauthored more than 90 papers in these
areas and four books. Dr. Cotae serves as an Associate Editor for IEEE
COMMUNICATIONS LETTERS, and he has been on the Technical Program Committee
and Session Chair of IEEE Conferences such as GLOBECOM (2003–2006), VTC Spring
2005, and ICC 2005 and 2006. He is a member of HKN (Eta Kappa Nu), the American
Society for Engineering Education, and the Society for Industrial and Applied
Mathematics.
ليست هناك تعليقات:
إرسال تعليق