An autoregressive estimator for overhead reduction in Substitution Networks
Abstract
A substitution network is a temporary network
that self-deploys to dynamically replace a portion of a damaged
infrastructure by means of a fleet of mobile routers. Some
efficient solutions deploy robots based on active measurements.
A robot/node in the network may use active link monitoring to
assess the link quality towards its neighbors through the use
of probe packets. Such probe packets are sent periodically at a
given rate, and so, the accuracy of the measurements depends on
the number and the frequency of exchanged packets. However,
exchanging probe packets is energy and bandwidth consuming,
thus active monitoring is considered as a costly mechanism.
Even so, active link monitoring is a technique widely used on
many network protocols. In this paper, we focus on an adaptive
positioning algorithm (APOLO) to self-deploy a network. APOLO
is based on active monitoring to gather essential information from
nodes. Therefore, we show how autoregressive estimation may
be used to reduce the overhead caused by the active measuring
technique. Moreover, it is possible to use surrogate data rather
than real data to feed APOLO without impacting its performance.
Origin : Files produced by the author(s)
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