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Abstract
Although mobile end-user devices are getting more
and more powerful, they still suffer from limited processing
capabilities and battery capacities. To address this problem, the
augmentation of mobile devices with resources from surrounding
devices or with cloud-based resources has gained popularity in
the recent years. Existing solutions that follow this approach and
offload computationally intensive tasks already yield great results
for specific use cases. Unfortunately, most of these solutions are
tailored to specific operating systems or programming languages,
and do not support the flexible usage of resources. Furthermore,
existing solutions implicitly assume external resources to be
trustworthy, which is usually not the case in practice. To overcome
these limitations, we introduce a secure and flexible resource-
discovery solution for mobile augmentation systems especially tar-
geting the pervasive-computing paradigm. This solution enables
a dynamic use of external resources and assures that security-
critical computations are offloaded to trusted resources only. We
demonstrate the feasibility and applicability of our proposed
solution by means of a proof-of-concept implementation.The
potential of this implementation to improve performance and save
energy at the same time is evaluated by means of two resource-
intensive applications. Obtained evaluation results show that the
proposed solution significantly speeds up applications on mobile
devices and reduces their power consumption by 50%. Thus,
the proposed solution enables fast, secure, and energy-saving
execution of complex mobile applications.
and more powerful, they still suffer from limited processing
capabilities and battery capacities. To address this problem, the
augmentation of mobile devices with resources from surrounding
devices or with cloud-based resources has gained popularity in
the recent years. Existing solutions that follow this approach and
offload computationally intensive tasks already yield great results
for specific use cases. Unfortunately, most of these solutions are
tailored to specific operating systems or programming languages,
and do not support the flexible usage of resources. Furthermore,
existing solutions implicitly assume external resources to be
trustworthy, which is usually not the case in practice. To overcome
these limitations, we introduce a secure and flexible resource-
discovery solution for mobile augmentation systems especially tar-
geting the pervasive-computing paradigm. This solution enables
a dynamic use of external resources and assures that security-
critical computations are offloaded to trusted resources only. We
demonstrate the feasibility and applicability of our proposed
solution by means of a proof-of-concept implementation.The
potential of this implementation to improve performance and save
energy at the same time is evaluated by means of two resource-
intensive applications. Obtained evaluation results show that the
proposed solution significantly speeds up applications on mobile
devices and reduces their power consumption by 50%. Thus,
the proposed solution enables fast, secure, and energy-saving
execution of complex mobile applications.
| Originalsprache | englisch |
|---|---|
| Titel | 2016 4th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering |
| ISBN (elektronisch) | 978-1-5090-1754-6 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - Apr. 2016 |
| Veranstaltung | IEEE Mobile Cloud - Oxford, Großbritannien / Vereinigtes Königreich Dauer: 29 März 2016 → 1 Apr. 2016 |
Konferenz
| Konferenz | IEEE Mobile Cloud |
|---|---|
| Land/Gebiet | Großbritannien / Vereinigtes Königreich |
| Ort | Oxford |
| Zeitraum | 29/03/16 → 1/04/16 |
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A-SIT - Zentrum für sichere Informationstechnologie Austria
Stranacher, K. (Teilnehmer (Co-Investigator)), Dominikus, S. (Teilnehmer (Co-Investigator)), Leitold, H. (Teilnehmer (Co-Investigator)), Marsalek, A. (Teilnehmer (Co-Investigator)), Teufl, P. (Teilnehmer (Co-Investigator)), Bauer, W. (Teilnehmer (Co-Investigator)), Aigner, M. J. (Teilnehmer (Co-Investigator)), Rössler, T. (Teilnehmer (Co-Investigator)), Neuherz, E. (Teilnehmer (Co-Investigator)), Dietrich, K. (Teilnehmer (Co-Investigator)), Zefferer, T. (Teilnehmer (Co-Investigator)), Mangard, S. (Teilnehmer (Co-Investigator)), Payer, U. (Teilnehmer (Co-Investigator)), Orthacker, C. (Teilnehmer (Co-Investigator)), Lipp, P. (Teilnehmer (Co-Investigator)), Reiter, A. (Teilnehmer (Co-Investigator)), Knall, T. (Teilnehmer (Co-Investigator)), Bratko, H. (Teilnehmer (Co-Investigator)), Bonato, M. (Teilnehmer (Co-Investigator)), Suzic, B. (Teilnehmer (Co-Investigator)), Zwattendorfer, B. (Teilnehmer (Co-Investigator)), Kreuzhuber, S. (Teilnehmer (Co-Investigator)), Oswald, M. E. (Teilnehmer (Co-Investigator)), Tauber, A. (Teilnehmer (Co-Investigator)), Posch, R. (Projektleiter (Principal Investigator)), Bratko, D. (Teilnehmer (Co-Investigator)), Feichtner, J. (Teilnehmer (Co-Investigator)), Ivkovic, M. (Teilnehmer (Co-Investigator)), Reimair, F. (Teilnehmer (Co-Investigator)), Wolkerstorfer, J. (Teilnehmer (Co-Investigator)) & Scheibelhofer, K. (Teilnehmer (Co-Investigator))
21/05/99 → 31/12/24
Projekt: Arbeitsgebiet