Algorithms for Sensor Systems: 11th International Symposium on Algorithms and Experiments for Wireless Sensor Networks, ALGOSENSORS 2015, Patras, Greece, September 17-18, 2015, Revised Selected Papers

238 Pages·2015·3.96 MB·English

by  Prosenjit Bose

Prosenjit Bose
Leszek Antoni Gasieniec
Kay Römer
Roger Wattenhofer (Eds.)
6 Algorithms
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for Sensor Systems
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11th International Symposium on Algorithms and Experiments
for Wireless Sensor Networks, ALGOSENSORS 2015
Patras, Greece, September 17–18, 2015, Revised Selected Papers
123
Lecture Notes in Computer Science 9536
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Lancaster University, Lancaster, UK
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Carnegie Mellon University, Pittsburgh, PA, USA
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University of Surrey, Guildford, UK
Jon M. Kleinberg
Cornell University, Ithaca, NY, USA
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ETH Zurich, Zürich, Switzerland
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Stanford University, Stanford, CA, USA
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Weizmann Institute of Science, Rehovot, Israel
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Indian Institute of Technology, Madras, India
Bernhard Steffen
TU Dortmund University, Dortmund, Germany
Demetri Terzopoulos
University of California, Los Angeles, CA, USA
Doug Tygar
University of California, Berkeley, CA, USA
Gerhard Weikum
Max Planck Institute for Informatics, Saarbrücken, Germany
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Prosenjit Bose Leszek Antoni G sieniec
(cid:129)
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Kay R mer Roger Wattenhofer (Eds.)
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Algorithms
for Sensor Systems
11th International Symposium on Algorithms and Experiments
for Wireless Sensor Networks, ALGOSENSORS 2015
Patras, Greece, September 17–18, 2015
Revised Selected Papers
123
Editors
Prosenjit Bose Kay Römer
Carleton University Graz University of Technology
Ottawa, ON Graz
Canada Austria
Leszek AntoniGąsieniec RogerWattenhofer
University of Liverpool ETHZurich
Liverpool Zürich
UK Switzerland
ISSN 0302-9743 ISSN 1611-3349 (electronic)
Lecture Notesin Computer Science
ISBN 978-3-319-28471-2 ISBN978-3-319-28472-9 (eBook)
DOI 10.1007/978-3-319-28472-9
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Preface
ALGOSENSORS, the International Symposium on Algorithms and Experiments for
Wireless Sensor Networks, is an international forum dedicated to the algorithmic
aspects of wireless networks, static or mobile. The 11th edition of ALGOSENSORS
was held during September 17–18, 2015, in Patras, Greece, within the ALGO annual
event.
Originally focused solely on sensor networks, ALGOSENSORS now covers more
broadly algorithmic issues arising in all wireless networks of computational entities,
including sensor networks, sensor-actuator networks, and systems of autonomous
mobile robots. In particular, it focuses on the design and analysis of discrete and
distributed algorithms, on models of computation and complexity, on experimental
analysis, in the context of wireless networks, sensor networks, and robotic networks
andonallfoundationalandalgorithmicaspectsoftheresearchintheseareas.Thisyear
papers were solicited into three tracks: Distributed and Mobile, Experiments and
Applications, and Wireless and Geometry.
In response to the call for papers, 30 submissions were received, out of which 16
papers were accepted after a rigorous reviewing process by the (joint) Program
Committee, which involved at least three reviewers for each accepted paper. This
volumecontainsthetechnicalpapersaswellasaninvitedpaperofthekeynotetalkby
Thomas Kesselheim (Max Planck Institute for Informatics).
We would like to thank all Program Committee members, as well as the external
reviewers,fortheirfundamentalcontributioninselectingthebestpapersresultingina
strongprogram.WewouldalsoliketowarmlythanktheALGO/ESA2015organizers
forkindlyacceptingtoco-locateALGOSENSORSwithsomeoftheleadingeventson
algorithms in Europe. Furthermore, we would like to thank the local ALGO Organi-
zation Committee for their help regarding various administrative tasks, especially the
Local Chair, Christos Zaroliagis. Last but not least, we would like thank the Publicity
Chair, Klaus-Tycho Foerster, the Web Chair, Laura Peer, and the Steering Committee
Chair, Sotiris Nikoletseas, for their help in ensuring a successful ALGOSENSORS
2015.
October 2015 Prosenjit Bose
Leszek Antoni Gąsieniec
Kay Römer
Roger Wattenhofer
Organization
Program Committee
Prosenjit Bose Carleton University, Canada (Chair Track Wireless
and Geometry)
LeszekAntoniGąsieniec University of Liverpool, UK (Chair Track Distributed
and Mobile)
Kay Römer TU Graz, Austria (Chair Track Experiments
and Applications)
Roger Wattenhofer ETH Zurich, Switzerland (General Chair)
Carlo Boano TU Graz, Austria
Nicolas Bonichon University of Bordeaux, France
Paz Carmi Ben-Gurion University, Israel
Jérémie Chalopin CNRS and Aix-Marseille Université, France
Jean-Lou De Carufel Carleton University, Canada
Stephane Durocher Manitoba University, Canada
Anna Foerster University of Bremen, Germany
Martin Gairing University of Liverpool, UK
Konstantinos Georgiou University of Waterloo, Canada
Tomasz Jurdzinski Wroclaw University, Poland
Matias Korman National Institute of Informatics, Japan
Olaf Landsiedel Chalmers University of Technology, Sweden
Andreas Loukas TU Berlin, Germany
George Mertzios University of Durham, UK
Luca Mottola Politecnico di Milano, Italy
Merav Parter MIT, USA
Ljubomir Perkovic DePaul University, USA
Andreas Reinhardt TU Clausthal, Germany
Olga Saukh ETH Zurich, Switzerland
Laura Peer ETH Zurich, Switzerland (Web Chair)
Klaus-Tycho Foerster ETH Zurich, Switzerland (Publicity Chair)
Steering Committee
Sotiris Nikoletseas University of Patras and CTI, Greece (Chair)
Josep Diaz U.P. Catalunya, Spain
Magnus M. Halldorsson Reykjavik University, Iceland
Bhaskar Krishnamachari University of Southern California, USA
P.R. Kumar Texas A&M University, USA
VIII Organization
Jose Rolim University of Geneva, Switzerland
Paul Spirakis University of Patras and CTI, Greece
Adam Wolisz T.U. Berlin, Germany
Additional Reviewers
Agathangelou, Chrysovalandis Kranakis, Evangelos
Avin, Chen Labourel, Arnaud
Czyzowicz, Jurek Lemiesz, Jakub
Das, Shantanu Martin, Russell
Deligkas, Argyrios Michail, Othon
Dieudonne, Yoann Navarra, Alfredo
Durmus, Yunus Raptopoulos, Christoforos
Emek, Yuval Roeloffzen, Marcel
Garncarek, Paweł Shalom, Mordechai
Gawrychowski, Pawel Tixeuil, Sebastien
Godard, Emmanuel Valicov, Petru
Jeż, Łukasz van Renssen, André
Klasing, Ralf Wong, Prudence W.H.
Korman, Amos Yogev, Eylon
Korzeniowski, Miroslaw Young, Adam
Kosowski, Adrian
Online Packing Beyond the Worst Case
(Invited Paper)
Thomas Kesselheim
Max-Planck-Institut für Informatik andSaarlandUniversity,
Saarbrücken, Germany
[e-mail]
For a number of online optimization problems, standard worst-case competitive anal-
ysisisverypessimisticorevenpointless.Sometimes,evenatrivialalgorithmmightbe
considered optimal because an adversary would be able to trick any algorithm.
An interesting way to avoid these pathological effects is to slightly reduce the
power of the adversary by introducing stochastic components. For example, the
adversarymightstilldefinetheinstancebutnottheorderinwhichitispresentedtothe
algorithm. This order is drawn uniformly at random from all possible permutations.
We consider online packing problems and show that this small transition beyond
worst-case analysis can have a big impact. We focus on the online independent-set
problem in graph classes motivated by wireless networks and on online packing LPs,
which among other applications also play a big role in web advertising.
1 Online Independent-Set Problems
Intheonlineindependent-setproblem,agraphisrevealedtothealgorithmstepwise.In
each step, one node is revealed including its edges to previously arrived nodes. This
way,manyonlineproblemsinthedomainsofschedulingandadmissioncontrolcanbe
captured.Forexample,thegraphmightrepresentwirelessinterferencesandthetaskis
to select a maximum non-interfering set of transmitters.
A very simple way to model wireless interference is by a disk graph: Each trans-
mittercoversacertaincircularareaintheplane.Theinterferenceconstraintrequiresthe
areas of notwotransmitters tobe intersecting.Although NPhard, theindependent-set
problemindiskgraphscanbeapproximatedverywell.Averysimplegreedyalgorithm
isaconstant-factorapproximation,andthereisevenaPTAS[2].Moregenerally,many
graph classes of relevance for practical applications, particularly wireless interference,
induce a bounded inductive independence number q [4, 6]. The greedy algorithm has
an approximation ratio of q, thus giving a constant-factor approximation in all these
examples.
Inatraditional(worst-case)competitiveanalysisoftheonlineproblem,onewould
have an adversary choosing the instance (i.e., the graph) and the order in which the
input is presented. The performance of an algorithm is measured in terms of its
competitive ratio jALGj, where ALG is the set of vertices chosen by the algorithm and
jOPTj
OPT is a maximum independent set in the graph. Unfortunately, even in disk graphs,
X T. Kesselheim
thisapproachcanonlyyielddevastatingresults,indicatingthatnoalgorithmcanbeany
better than the trivial algorithm, which only selected the first vertex of the input.
In [3], we present a stochastic analysis of this problem. Instead of focusing on a
particular stochastic input model, we introduce a generic sampling approach that
enables us to devise online algorithms achieving performance guarantees for a variety
of input models. In particular, we cover the random-order model, in which the
adversary still chooses the graph but cannot determine the order in which the graph is
presented to the algorithm. Instead, the order is drawn uniformly at random from all
possible permutation after the adversary’s choice.
We present an online algorithm for maximum independent set achieving a com-
petitiveratioofOð1=q2Þintherandom-ordermodelandanumberoffurtherstochastic
online models. We prove that this result can be extended towards maximum-weight
independent set by losing only a factor of Oð1=lognÞ in the competitive ratio with
n denoting the (expected) number of nodes. This upper bound is complemented by a
lower bound of Xðlog2logn=lognÞ, showing that our approach achieves nearly the
optimal competitive ratio. In addition, we present various extensions of our approach
e.g. towards admission control in wireless networks modeled by SINR graphs.
2 Online Packing LPs
InonlinepackingLPs,thecolumnsofapackingLParepresentedtothealgorithmone
after the other. The corresponding variables have to be set irrevocably at the arrival
of the corresponding column. Again, we assume that the instance is chosen by an
adversarybuttheorder inwhichcolumnsarepresentedisdrawnuniformlyatrandom
from all permutations.
In [5], we present a -competitive online algorithm. Here d de-
notesthecolumnsparsity,i.e.,themaximumnumberofresourcesthatoccurinasingle
column, and B denotes the capacity ratio B, i.e., the ratio between the capacity of a
resource and the maximum demand for this resource. In other words, we achieve a
ð1(cid:2)(cid:2)Þ-approximationifthecapacityratiosatisfiesB¼XðlogdÞ,whichisbestpossible
(cid:2)2
for any (randomized) online algorithms [1].
Our result improves exponentially on previous work with respect to the capacity
ratio.IncontrasttoexistingresultsonpackingLPproblems,ouralgorithmdoesnotuse
dualpricestoguidetheallocationofresourcesovertime.Instead,thealgorithmsimply
solves, for each request, a scaled version of the partially known primal program and
randomlyroundstheobtainedfractionalsolutiontoobtainanintegralallocationforthis
request.WeshowthatthissimplealgorithmictechniqueisnotrestrictedtopackingLPs
with large capacity ratio of order XðlogdÞ, but it also yields close-to-optimal com-
petitiveratiosifthecapacityratioisboundedbyaconstant.Inparticular,weprovean
upper bound on the competitive ratio of Xðd(cid:2)1=ðB(cid:2)1ÞÞ, for any B(cid:3)2.