Improving the performance of asynchronous communication in long-distance delay-sensitive networks through the development of context-aware architectures

Peoples, C., Parr, G. ORCID: https://orcid.org/0000-0002-9365-9132, Scotney, B., Moore, A. and Dini, P. (2006) Improving the performance of asynchronous communication in long-distance delay-sensitive networks through the development of context-aware architectures. In: International Conference on Autonomic and Autonomous Systems (2006). UNSPECIFIED, USA. ISBN 0-7695-2653-5

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Abstract

Context-awareness is inherent in anticipated interplanetary missions. Swarm technologies by D'Arrigo, P, Santandrea, S. (2005) use context-awareness in short-haul networks between components, and long-distance networks allow communication with Earth. However, the propagation delays limit real-time communications, deep space being an environment in which the speed of light becomes a restriction. Therefore, the development of a protocol stack which is adaptive to application requirements and external influences will help to maximise communication synchronicity. As part of a first year doctorate research programme, this paper correlates current stack functionalities with interplanetary application requirements. A redesigned stack proposes to resolve this misalignment. Context-awareness is incorporated, enabling intelligent protocol selection using application layer knowledge and environmental information, with particular attention given to transport protocols. The paper concludes by considering transport protocol characteristics when deployed beside a context-aware layer, with the long-term aim being the development of a transport protocol suitable for deployment in the state-of-the-art context-aware stack.

Item Type:	Book Section
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory Faculty of Science > Research Groups > Data Science and AI
Depositing User:	Pure Connector
Date Deposited:	24 Sep 2016 01:07
Last Modified:	10 Dec 2024 01:11
URI:	https://ueaeprints.uea.ac.uk/id/eprint/60546
DOI:	10.1109/ICAS.2006.27

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