Self-Stabilizing Systems: 6th International Symposium, SSS 2003 San Francisco, CA, USA, June 24–25, 2003 Proceedings

225 Pages·2003·2.42 MB·English

by  Murat Demirbas

Lecture Notes in Computer Science 2704
EditedbyG.Goos,J.Hartmanis,andJ.vanLeeuwen
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Shing-Tsaan Huang Ted Herman (Eds.)
Self-Stabilizing
Systems
6th International Symposium, SSS 2003
San Francisco, CA, USA, June 24-25, 2003
Proceedings
1 3
SeriesEditors
GerhardGoos,KarlsruheUniversity,Germany
JurisHartmanis,CornellUniversity,NY,USA
JanvanLeeuwen,UtrechtUniversity,TheNetherlands
VolumeEditors
Shing-TsaanHuang
NationalCentralUniversity
CollegeofElectricalEngineeringandComputerScience
Chung-Li(32054),Taiwan
E-mail:[email protected]
TedHerman
UniversityofIowa,Dept.ofComputerScience
IowaCity,IA52242,USA
E-mail:[email protected]
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Preface
Self-stabilization is an established principle of modern distributed system de-
sign. The advantages of systems that self-recover from transient failures, tem-
porary security attacks, and spontaneous reconfiguration are obvious. Less well
understood are the inherent costs and design tradeoffs that accompany self-
stabilization. The need for autonomous recovery, not just of entire systems,
but also of individual components, algorithms, and communication protocols,
is becoming more apparent because distributed systems of immense scale are
presently emerging.
The Symposium on Self-Stabilizing Systems (SSS) is the main forum for
research in the area of self-stabilization. This year’s meeting changed the name
fromworkshop tosymposium,inrecognitionofsomematurityinthearea,which
is increasinglyalso representedby papers at the best conferences on distributed
systems. The previous Workshops on Self-Stabilizing Systems (WSS) were held
in 1989, 1995, 1997, 1999, and 2001. This year’s symposium was organized on
short notice, leaving researchers far less time to prepare submissions than in
past workshops (it becomes ever more challenging to fit our conference into the
very active schedules of the research community). Nonetheless, we received 27
submitted papers, which is equal to the number submitted to the workshop of
2001.Outofthesubmittedpapers,theprogramcommitteechose15forinclusion
in these proceedings.
A presentation at the symposium which is not represented in these proceed-
ings was the invited talk by Anish Arora. The title of his presentation was
“Taking Stabilization to the Masses: Problems, Opportunities and Progress.”
ProfessorAroradiscussedrecentcomputing trendsthatderivebenefitfromself-
stabilization principles, as well as some perceived and some real problems that
have thus far preventedwide spreaddeploymentof self-stabilizationtechniques.
Inparticular,hefocusedonwirelesssensornetworks,whichareapromisingplat-
form for stabilization due to thair lightweight model of hardware and software,
their error-pronemeasurementofsensoryinformation,andthe suitability ofthe
forward recovery semantics of many self-stabilizing protocols for their applica-
tions. The first paper in these proceedings deals with one of these applications,
namely tracking of mobile objects, and is illustrative of some of the research
problems and opportunities in this area.
Futurists of applied computing technology dream of a time when computing
devices with attached sensors and actuators can be manufactured in microscale
(MEMS) to nanoscale dimensions; such devices could be deployed in enormous
networks, consisting of millions of nodes with varying levels of computing and
communication resource. In this new regime, issues of fault tolerance, power
conservation,andadaptivitywillbecrucial(see,forexample,LNCS2634).Self-
stabilization and techniques derived from stabilization will no doubt become
routine, and research on these challenging issues will motivate our field. Not
VI Preface
entirely by coincidence, at the same time as researchers investigate nanoscale
technology, other scientists are looking at the mechanisms of life on an even
smallerscale.Computerscience isessentialto the mappingofgeneticcodes,the
modeling of protein folding, and the extraction of patterns from vast genomic
databases.IncopingwiththescaleoftheInternetandimaginedsensornetworks,
it is natural to find parallels in biological systems (the third paper in these
proceedings is an example of this motif).
The symposium of 2003 was held in collaboration with DSN 2003, the In-
ternationalConferenceonDependable SystemsandNetworks,inSanFrancisco,
June 24th and June 25th. We thank Charles B. Weinstock, the general chair of
DSN 2003,for the opportunity to hold the symposium at that venue.We grate-
fully acknowledge the efforts of the SSS program committee members and the
reviewers,who providedthorough reviews on a rather short schedule. In partic-
ular, we thank Anish Arora, Ajoy Datta, and Neeraj Suri for their work with
conference arrangements and many other details to support the symposium.
April 2003 Shing-Tsaan Huang
Ted Herman
Program Committee
Anish Arora, Ohio State University, USA
Joffroy Beauquier, Universit´e de Paris Sud, France
Ajoy K. Datta, Publicity Chair, University of Nevada at Las Vegas, USA
Shlomi Dolev, Ben-Gurion University of the Negev, Israel
Mohamed G. Gouda, University of Texas at Austin, USA
Maria Gradinariu, Universit´e de Rennes, France
Sukumar Ghosh, University of Iowa, USA
Ted Herman, Publications Chair, University of Iowa, USA
Shing-Tsaan Huang, Program Chair, National Central University, Taiwan
Colette Johnen, Universit´e de Paris Sud, France
Toshimitsu Masuzawa, Osaka University, Japan
Neeraj Suri, TU Darmstadt, Germany
Vincent Villain, Universit´e de Picardie, France
Referees
U. Arumugam S. Bapat J.R.S. Blair
A.E. Campos J.A. Cobb A. Cournier
B. Cukic M. Demirbas S. Devismes
F. Ga¨rtner M. Gairing S.T. Hedetniemi
M. Herlihy S.S. Hung E. Jung
J. Kiniwa P. Kristiansen S.S. Kulkarni
F. Manne V. Naik M. Nesterenko
C.F. Orellana H. Pagnia M. Paulitsch
F. Petit G. Shah P. Srimani
W. Steiner S. Tixeuil K. Vinodkrishnan
A. Yora C.Y. Yang
Table of Contents
A Pursuer-Evader Game for Sensor Networks ...............................1
Murat Demirbas, Anish Arora, and Mohamed G. Gouda
Collision-Free Communication in Sensor Networks .........................17
Sandeep S. Kulkarni and Umamaheswaran Arumugam
Self-Stabilizing Pulse Synchronization
Inspired by BiologicalPacemaker Networks ................................32
Ariel Daliot, Danny Dolev, and Hanna Parnas
Self-Stabilizing Algorithms for {k}-Domination ............................49
Martin Gairing, Stephen T. Hedetniemi, Petter Kristiansen,
and Alice A. McRae
Self-Stabilizing Group Communication in Directed Networks
(Extended Abstract) .......................................................61
Shlomi Dolev and Elad Schiller
Lyapunov Analysis of Neural Network Stability
in an Adaptive Flight Control System ......................................77
Sampath Yerramalla, Edgar Fuller, Martin Mladenovski,
and Bojan Cukic
Self-Stabilizing Token Circulation on Uniform Trees
by Using Edge-Tokens .....................................................92
Shing-Tsaan Huang and Su-Shen Hung
Self-Stabilizing Atomicity Refinement
Allowing Neighborhood Concurrency .....................................102
S´ebastien Cantarell, Ajoy K. Datta, and Franck Petit
A New Self-Stabilizing k-out-of-(cid:2) Exclusion Algorithm on Rings ...........113
Ajoy K. Datta, Rachid Hadid, and Vincent Villain
A Framework of Safe Stabilization ........................................129
Sukumar Ghosh and Alina Bejan
A Method for Evaluating Efficiency of Protocols
on the Asynchronous Shared-State Model .................................141
Yoshihiro Nakaminami, Toshimitsu Masuzawa, and Ted Herman
Time-Efficient Self-Stabilizing Algorithms
through Hierarchical Structures ...........................................154
Felix C. Ga¨rtner and Henning Pagnia
A Stabilizing Solution to the Stable Path Problem ........................169
Jorge A. Cobb, Mohamed G. Gouda, and Ravi Musunuri
X Table of Contents
Route Preserving Stabilization ............................................184
Colette Johnen and S´ebastien Tixeuil
An Improved Snap-Stabilizing PIF Algorithm .............................199
L´elia Blin, Alain Cournier, and Vincent Villain
Author Index ...........................................................215
(cid:1)
A Pursuer-Evader Game for Sensor Networks
Murat Demirbas1, Anish Arora1, and Mohamed G. Gouda2
1 Department of Computer& Information Science, TheOhio State University
Columbus, Ohio43210 USA
2 Department of Computer Sciences, The University of Texas at Austin
Austin, TX 78712 USA
Abstract. Inthispaperaself-stabilizingprogramforsolvingapursuer-
evader problem in sensor networks is presented. The program can be
tuned for tracking speed or energy efficiency. In the program, sensor
motes close to the evader dynamically maintain a “tracking” tree of
depth R that is always rooted at the evader. The pursuer, on the other
hand,searches thesensornetworkuntilitreachesthetrackingtree,and
then follows the treeto its root in order to catch theevader.
1 Introduction
Due toitsimportanceinmilitarycontexts,pursuer-evadertrackinghasreceived
significantattention[3,16,17,4]andhasbeenposedbytheDARPAnetworkem-
bedded software technology (NEST) program as a challenge problem. Here, we
consider the problem in the context of wireless sensor networks. Such networks
comprisingpotentiallymanythousandsoflow-costandlow-powerwirelesssensor
nodeshaverecentlybecamefeasible,thankstoadvancesinmicroelectromechan-
icalsystemstechnology,andarebeing regardedasa realisticbasisfordeploying
large-scale pursuer evader tracking.
Previous work on the pursuer-evader problem is not directly applicable to
tracking in sensor networks, since these networks introduce the following chal-
lenges:Firstly,sensornodeshaveverylimited computationalresources(e.g.,8K
RAM and 128K flash memory); thus, centralized algorithms are not suitable
for sensor networks due to their larger computational requirements. Secondly,
sensor nodes are energy constrained; thus, algorithms that impose an excessive
communication burden on nodes are not acceptable since they drain the bat-
tery powerquickly.Thirdly,sensornetworksarefault-prone:messagelossesand
corruptions(due to fading,collusion,and hidden node effect), and node failures
(due to crash and energy exhaustion) are the norm rather than the exception.
Thus, sensor nodes can lose synchrony and their programs can reach arbitrary
states [14]. Finally, on-site maintenance is not feasible; thus, sensor networks
should be self-healing. Indeed, one of the emphases of the NEST program is to
(cid:1) This work was partially sponsored by DARPA contract OSU-RF #F33615-01-C-
1901, NSF grant NSF-CCR-9972368, an Ameritech Faculty Fellowship, and two
grants from Microsoft Research.
S.-T.HuangandT.Herman(Eds.):SSS2003,LNCS2704,pp.1–16,2003.
(cid:1)c Springer-VerlagBerlinHeidelberg2003