Things that blink: computationally augmented name tags
IBM Systems Journal
Meme tags and community mirrors: moving from conferences to collaboration
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
XMill: an efficient compressor for XML data
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Allia: alliance-based service discovery for ad-hoc environments
WMC '02 Proceedings of the 2nd international workshop on Mobile commerce
XPRESS: a queriable compression for XML data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
XGRIND: A Query-Friendly XML Compressor
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Service Rings - A Semantic Overlay for Service Discovery in Ad hoc Networks
DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
Scalable Service Discovery for MANET
PERCOM '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
A sensor network for social dynamics
Proceedings of the 5th international conference on Information processing in sensor networks
IEEE Communications Magazine
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In this paper, we describe a packet data size minimization method designed specifically for advertising and discovery in ubiquitous networks. The minimization is effective for achieving superior discovery performance characteristics such as discovery time and power consumption. The proposed method for data packet size minimization is based on indexing of advertisement text. In the method, dictionaries and indexed data are stored separately, i.e. dictionaries are stored on a server and indexed data is stored on ubiquitous wireless devices, and the same dictionaries are shared among all users. We evaluate an average packet data size and dictionary size for three indexing methods: regular indexing, category indexing and attribute indexing; and show that these methods achieve data packet sizes which are about two and three times smaller than raw data packets and zipped packet data sizes respectively. Also, we show that category indexing allows users to be less dependent on the infrastructure.