<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Paulo Jesus</style></author><author><style face="normal" font="default" size="100%">Carlos Baquero Moreno</style></author><author><style face="normal" font="default" size="100%">Paulo Sérgio Almeida</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fault-Tolerant Aggregation by Flow Updating</style></title><secondary-title><style face="normal" font="default" size="100%">Distributed Applications and Interoperable Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-02164-0_6</style></url></web-urls><related-urls><url><style face="normal" font="default" size="100%">https://haslab.uminho.pt/sites/default/files/cbm/files/flow_updating.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></publisher><pub-location><style face="normal" font="default" size="100%">Lisbon, Portugal</style></pub-location><volume><style face="normal" font="default" size="100%">5523</style></volume><pages><style face="normal" font="default" size="100%">73–86</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Data aggregation plays an important role in the design of scalable systems, allowing the determination of meaningful system-wide properties to direct the execution of distributed applications. In the particular case of wireless sensor networks, data collection is often only practicable if aggregation is performed. Several aggregation algorithms have been proposed in the last few years, exhibiting different properties in terms of accuracy, speed and communication tradeoffs. Nonetheless, existing approaches are found lacking in terms of fault tolerance. In this paper, we introduce a novel fault-tolerant averaging based data aggregation algorithm. It tolerates substantial message loss (link failures), while competing algorithms in the same class can be affected by a single lost message. The algorithm is based on manipulating flows (in the graph theoretical sense), that are updated using idempotent messages, providing it with unique robustness capabilities. Furthermore, evaluation results obtained by comparing it with other averaging approaches have revealed that it outperforms them in terms of time and message complexity.&lt;/p&gt;
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