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 PDF
Preview 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
Prosenjit Bose Leszek Antoni Gasieniec Kay Römer Roger Wattenhofer (Eds.) 6 Algorithms 3 5 9 S for Sensor Systems C N L 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 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen Editorial Board David Hutchison Lancaster University, Lancaster, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Friedemann Mattern ETH Zurich, Zürich, Switzerland John C. Mitchell Stanford University, Stanford, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel C. Pandu Rangan 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 More information about this series at http://www.springer.com/series/7411 ą Prosenjit Bose Leszek Antoni G sieniec (cid:129) ö Kay R mer Roger Wattenhofer (Eds.) (cid:129) 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 LibraryofCongressControlNumber:2015958836 LNCSSublibrary:SL5–ComputerCommunicationNetworksandTelecommunications ©SpringerInternationalPublishingSwitzerland2015 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartofthe material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodologynow knownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbookare believedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsortheeditors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissionsthatmayhavebeenmade. Printedonacid-freepaper ThisSpringerimprintispublishedbySpringerNature TheregisteredcompanyisSpringerInternationalPublishingAGSwitzerland 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 [email protected] 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.
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