Leader election in ad hoc radio networks: A keen ear helps
- ,
- Andrzej Pelc
- University of Liverpool,
- Université du Québec en Outaouais
Related Event
Title
Event type
ConferenceDate
07/05/2009 - 07/12/2009Location
Abstract
We address the fundamental distributed problem of leader election in ad hoc radio networks modeled as undirected graphs. Nodes are stations having distinct integer labels, and each node knows only its own label and a polynomial upper bound on all labels. A signal from a transmitting node reaches all neighbors. What distinguishes radio networks from message-passing networks is that a message is received successfully by a node, if and only if, exactly one of its neighbors transmits in this round. If two neighbors of a node transmit simultaneously in a given round, none of the messages is heard by the receiving node. In this case we say that a collision occurred at this node. An important capability of nodes of a radio network is collision detection: the ability of nodes to distinguish a collision from the background noise occurring when no neighbor transmits. (This ability is the "keen ear" of the nodes.) Can collision detection speed up leader election in arbitrary radio networks? We give a positive answer to this question. More precisely, our main result is a deterministic leader election algorithm working in time O(n) in all n-node networks, if collision detection is available, while it is known that deterministic leader election requires time Ω(n logn), even for complete networks, if there is no collision detection. This is the first computational task whose execution for arbitrary radio networks is shown to be faster with collision detection than without it.
Publication Information
Output type
Original language
English (US)Pages from-to (Number of pages)
Pages 521-533 (13 pages)Publication milestones
- Published - 2009
Publication status
Edition
PART 2Publication series
- Publication series name: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print): 0302-9743
ISSN (Electronic): 1611-3349
Volume: 5556 LNCS
Number: PART 2
ISBN (Print)
3642029299, 9783642029295Publication IDs
- Scopus: 70449113277
