Adaptive self-deployment algorithms for mobile wireless substitution networks

In case of a disaster, the communication infrastructure may be partially or totally destroyed, or scarce because of the high data traffic. Even so, it is necessary to provide connectivity between the rescue teams and the command center. Therefore, temporary communication solutions are crucial until the infrastructure is restored. In this thesis, we focus on the deployment of a communication solution in such a context called substitution networks. Thus, we propose a self-deployment algorithm to allow mobile routers that compose a substitution network to be spread out to cover the target area. Our algorithm monitors the network conditions to decide whether the router should move or not, adjusting its position based on one-hop information by means of active measurement, i.e., probe packets. Such probe packets allow the algorithm to monitor the channel and its eventual changes over time. If the probe transmission rate is high enough, the insights obtained will be accurate, however, the overhead will increase proportionally consuming network resources. Hence, we propose to use surrogate data obtained by means of an autoregressive estimator to reduce the overhead without impacting our deployment algorithm. We show by simulation the efficiency of both algorithms and their performance in terms of deployment time, delay, jitter, and throughput.

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Source https://theses.hal.science/tel-00918017
Author Miranda, Karen
Maintainer CCSD
Last Updated May 7, 2026, 20:01 (UTC)
Created May 7, 2026, 20:01 (UTC)
Identifier tel-00918017
Language en
Rights https://about.hal.science/hal-authorisation-v1/
contributor Self-organizing Future Ubiquitous Network (FUN) ; Centre Inria de l'Université de Lille ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
creator Miranda, Karen
date 2013-12-10T00:00:00
harvest_object_id 2ec08e83-5990-4b17-b827-ad36e845ca40
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2025-10-07T00:00:00
set_spec type:THESE