NDLI: Evaluating context information predictability for autonomic communication

Content Provider	IEEE Xplore Digital Library
Author	Musolesi, M. Mascolo, C.
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci., Univ. Coll. London (Musolesi, M.; Mascolo, C.)
Abstract	Delay tolerant and mobile ad hoc networks present considerable challenges to the development of protocols and systems. In particular, the challenge of being able to cope with their variability is an important one: sometimes the rate at which these systems change in terms of context (such as topology, collocation duration and availability and quality of the local resources) is very high and these changes are unpredictable. Knowledge of context could be used to improve the performance of such systems. For example, context information may be extremely useful to make routing decisions. Some recent approaches have successfully exploited context and prediction on future context condition to improve performance, for instance in terms of delivery ratio and delay. In this paper, we present a model of predictability of context information and the design of a generic component implementing it. The component can be used to decide if (or in which measure) context is predictable. The model is based on the analysis of the time series representing the context information. In order to show how the component can be used in practice, we describe its integration in our context-aware adaptive routing (CAR) protocol
Starting Page	5
Ending Page	499
File Size	192639
Page Count	495
File Format	PDF
ISBN	0769525938
DOI	10.1109/WOWMOM.2006.41
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Disruption tolerant networking Availability Protocols Network topology Predictive models Mobile communication Routing Delay Context modeling Mobile ad hoc networks
Content Type	Text
Resource Type	Article

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