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Real-Time Traffic Management in Sensor Networks

Authors: Kyriakos Karenos; Vana Kalogeraki;

Real-Time Traffic Management in Sensor Networks

Abstract

In this work we propose a traffic management mechanism to support real-time flows in highly unpredictable sensor network environments. The mechanism is based on a joint traffic regulation and end-to-end scheduling approach; a traffic regulation component adjusts the incoming packet rate to implicitly control the channel load and intelligently rejects packets that are more likely to miss their deadline while a laxity-based scheduling component projects the packets? per-hop delay and compensates for network endto- end delays. Thus, high success ratios without severely degrading the fidelity are achieved. Our mechanism attempts to maintain accuracy in a resource-efficient manner even under extremely unstable network conditions where delays are difficult to model and compute. Furthermore, the adoption of a component-based approach allows for substantial independence from both the MAC and routing layers. We thoroughly evaluate our mechanism and demonstrate its accuracy and performance merits.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
28
Top 10%
Top 10%
Top 10%
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