DocumentCode
1761761
Title
Estimating the Cardinality of a Mobile Peer-to-Peer Network
Author
Shiping Chen ; Yan Qiao ; Shigang Chen ; Jianfeng Li
Author_Institution
Network Inf. Center & Sch. of Opt.-Electr. & Comput. Eng., Univ. of Shanghai for Sci. & Technol., Shanghai, China
Volume
31
Issue
9
fYear
2013
fDate
41518
Firstpage
359
Lastpage
368
Abstract
Collecting information from mobile peer-to-peer (P2P) networks has important civilian and military applications. One problem is to determine the cardinality, i.e., the number of nodes, in a large mobile system. In a stationary wireless network, it can be trivially solved through a flooding-based query. However, the problem becomes much more challenging for mobile P2P networks whose topologies are constantly changing. In this paper, we present two novel statistical methods, called the circled random walk and the tokened random walk, to address this interesting problem. The circled random walk is simpler to implement and works well in networks of high mobility, whereas the tokened random walk works well with high or low mobility. These methods provide cardinality estimation by involving only a small subset of the nodes. They make tradeoff between overhead and estimation accuracy. The estimation error can be made arbitrarily small at the expense of larger overhead.
Keywords
mobile radio; peer-to-peer computing; query processing; telecommunication network topology; cardinality estimation; circled random walk; civilian applications; estimation error; flooding-based query; military applications; mobile P2P networks; mobile peer-to-peer network; stationary wireless network; statistical methods; tokened random walk; Accuracy; Estimation; Mobile communication; Mobile computing; Peer-to-peer computing; Probes; Wireless communication; Mobile P2P networks; cardinality estimation; random walk;
fLanguage
English
Journal_Title
Selected Areas in Communications, IEEE Journal on
Publisher
ieee
ISSN
0733-8716
Type
jour
DOI
10.1109/JSAC.2013.SUP.0513032
Filename
6585895
Link To Document