Utilize este identificador para referenciar este registo: http://hdl.handle.net/11144/3923
Título: Source Localization and Network Topology Discovery in Infection Networks
Autor: Hao, He
Silvestre, Daniel
Silvestre, Carlos
Palavras-chave: computer networks
telecommunication network topology
high-degree nodes
source localization
network topology discovery
infection networks
identification problem
source identification
Observers
Standards
Optimization;
Topology
Mathematical model
Observability
Network topology
Data: Jul-2018
Editora: IEEE
Citação: H. Hao, D. Silvestre and C. Silvestre, "Source Localization and Network Topology Discovery in Infection Networks," 2018 37th Chinese Control Conference (CCC), Wuhan, 2018, pp. 1915-1920. doi: 10.23919/ChiCC.2018.8482274 keywords: {computer networks;telecommunication network topology;high-degree nodes;source localization;network topology discovery;infection networks;identification problem;source identification;Mathematical model;Observability;Network topology;Observers;Standards;Optimization;Topology}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8482274&isnumber=8482271
Resumo: Determining the network topology is typically a challenging problem due to the number of nodes and connection between them. Complexity is added whenever this identification problem relies solely on a subset of the outputs of some dynamical system or distributed algorithm running on those nodes. In this paper, we focus on both the source identification and network topology discovery problems in the context of infection networks where a subset of the nodes are elected as observers. The solution consists in writing the binary constraints associated with the problem. Convex relaxations are also proposed and investigated through simulations where a pattern emerges that placing observers in high-degree nodes increases the accuracy of the method.
Arbitragem científica: yes
URI: http://hdl.handle.net/11144/3923
DOI: 10.23919/ChiCC.2018.8482274
ISSN: 1934-1768
Aparece nas colecções:AUTONOMA TECHLAB - Artigos/Papers

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