Distribution Patterns for Mobile Internet Applications

Roland Wagner, Franz Gruber, Werner Hartmann

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

After the enormous success ot the internet and mobile networks, the next upcoming boost for information technology will be the combination of both. But developing applications for this domain is challenging, because first, most mobile devices provide only small memory and processor footprints, prohibiting resource intensive code at client side and second, mobile networks offer only limited bandwidth, and the prohability to connection losses is relatively high compared to wired networks. Selecting the appropriate software architecture in terms of distributing the functionality of the system between server and client device is crucial. Application distribution patterns, known from conventional system development, are analysedfor their applicability for the mobile environment. After the more abstract analysis of the patterns, the IP multimedia subsystem (IMS) which is part of the current specification of 3G mobile networks is introduced and its support for different application distribution patterns is examined.
Original languageEnglish
Title of host publicationHandbook of Research on Mobile Multimedia
Editors Ismail Khalil Ibrahim, Johannes Kepler University Linz, Austria
PublisherIdea Group Reference, Hershey - London - Melbourne - Singapore
Pages507-520
Number of pages14
ISBN (Print)1-59140-866-0
Publication statusPublished - 2006

Fields of science

  • 102001 Artificial intelligence
  • 102006 Computer supported cooperative work (CSCW)
  • 102010 Database systems
  • 102014 Information design
  • 102015 Information systems
  • 102016 IT security
  • 102028 Knowledge engineering
  • 102019 Machine learning
  • 102022 Software development
  • 102025 Distributed systems
  • 502007 E-commerce
  • 505002 Data protection
  • 506002 E-government
  • 509018 Knowledge management
  • 202007 Computer integrated manufacturing (CIM)
  • 102033 Data mining
  • 102035 Data science

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