research unit 1

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Type of publication:Inproceedings
Entered by:
TitleNaming and Counting in Anonymous Unknown Dynamic Networks
Bibtex cite IDRACTI-RU1-2013-8
Booktitle 15th International Symposium on Stabilization, Safety, and Security of Distributed Systems (SSS)
Series Lecture Notes in Computer Science
Year published 2013
Month November
Volume 8255
Pages 281-295
Publisher Springer International Publishing Switzerland
Location Osaka, Japan
Keywords dynamic network,distributed computing,mobile computing,worst-case dynamicity,causal influence,naming,counting
In this work, we study the fundamental naming and counting problems (and some variations) in networks that are anonymous, unknown, and possibly dynamic. In counting, nodes must determine the size of the network n and in naming they must end up with unique identities. By anonymous we mean that all nodes begin from identical states apart possibly from a unique leader node and by unknown that nodes have no a priori knowledge of the network (apart from some minimal knowledge when necessary) including ignorance of n. Network dynamicity is modeled by the 1-interval connectivity model [KLO10], in which communication is synchronous and a (worst-case) adversary chooses the edges of every round subject to the condition that each instance is connected. We first focus on static networks with broadcast where we prove that, without a leader, counting is impossible to solve and that naming is impossible to solve even with a leader and even if nodes know n. These impossibilities carry over to dynamic networks as well. We also show that a unique leader suffices in order to solve counting in linear time. Then we focus on dynamic networks with broadcast. We conjecture that dynamicity renders nontrivial computation impossible. In view of this, we let the nodes know an upper bound on the maximum degree that will ever appear and show that in this case the nodes can obtain an upper bound on n. Finally, we replace broadcast with one-to-each, in which a node may send a different message to each of its neighbors. Interestingly, this natural variation is proved to be computationally equivalent to a full-knowledge model, in which unique names exist and the size of the network is known.
Michail, Othon
Chatzigiannakis, Ioannis
Spirakis, Paul
MCS-SSS13.pdf (main file)
Publication ID983