DocumentCode :
2556861
Title :
Disconnection prediction in mobile P2P networks using publish/subscribe
Author :
De Leoni, Massimiliano ; Mecella, Massimo ; Manfre, Paolo ; Franchi, Junio Valerio ; Graziano, Daniele
Author_Institution :
Dipt. di Inf. e Sist. - DIS, Sapienza Univ. di Roma, Rome, Italy
fYear :
2009
fDate :
12-14 Oct. 2009
Firstpage :
1
Lastpage :
8
Abstract :
In many pervasive scenarios (e.g., emergency management or health care), operators need to exchange data and information and collaborate in order to carry on a collaborative job, but communication features can be lacking on the spot. Therefore, mobile ad-hoc networks are valuable solutions to let them coordinate. While executing some activities, nodes can move in the area and, hence, disconnect from the others. In order operators to coordinate with each other, devices need to be continuously connected, although that is not guaranteed, due to the mobile nature of the network. In this paper we present a Bayesian approach to predict device disconnections in mobile ad-hoc networks, and validating experimental results that show the viability of the approach. This prediction feature is useful for an upper coordination layer, which can arrange appropriate actions to prevent disconnections from occurring or, at least, to mitigate the consequences.
Keywords :
ad hoc networks; belief networks; groupware; mobile computing; peer-to-peer computing; Bayesian approach; collaborative job; disconnection prediction; mobile P2P networks; mobile ad hoc networks; publish; subscribe; Ad hoc networks; Bayesian methods; Cameras; Collaboration; Disaster management; Global Positioning System; Mobile ad hoc networks; Mobile communication; Mobile computing; Peer to peer computing; Coordination; Disaster management; Disconnection prediction; Global Positioning System (GPS); Mobile P2P networks; Publish/subscribe Middleware;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Ultra Modern Telecommunications & Workshops, 2009. ICUMT '09. International Conference on
Conference_Location :
St. Petersburg
Print_ISBN :
978-1-4244-3942-3
Electronic_ISBN :
978-1-4244-3941-6
Type :
conf
DOI :
10.1109/ICUMT.2009.5345353
Filename :
5345353
Link To Document :
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