Journal
PROCEEDINGS OF THE IEEE
Volume 98, Issue 11, Pages 1847-1864Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JPROC.2010.2052531
Keywords
Consensus protocols; distributed algorithms; distributed processing; gossip protocols; graph theory; information networks distributed averaging; network topology; peer to peer computing; protocols; random topologies; topology design; wireless sensor networks
Categories
Funding
- National Science Foundation (NSF) [CCF1011903]
- Air Force Office of Sponsored Research (AFOSR) [FA95501010291]
- U.S. Office of Naval Research (ONR) [MURI N000140710747]
- Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN 341596-2007]
- Information Technology and Complex Systems (MITACS)
- Fonds Quebecois de la Recherche sur la Nature et les Technologies (FQRNT) [2009-NC-126057]
- NSF [CCF-0729074]
- Direct For Computer & Info Scie & Enginr [0848256] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1018509] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations [0848256] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1531050] Funding Source: National Science Foundation
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Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This paper presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.
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