Please use this identifier to cite or link to this item: http://hdl.handle.net/11144/3923
Title: Source Localization and Network Topology Discovery in Infection Networks
Authors: Hao, He
Silvestre, Daniel
Silvestre, Carlos
Keywords: 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
Issue Date: Jul-2018
Publisher: IEEE
Citation: 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
Abstract: 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.
Peer reviewed: yes
URI: http://hdl.handle.net/11144/3923
metadata.dc.identifier.doi: 10.23919/ChiCC.2018.8482274
ISSN: 1934-1768
Appears in Collections:AUTONOMA TECHLAB - Artigos/Papers

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