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REFIT: Robustness Enhancement Against Cascading Failure in IoT Networks
Univ Tehran, Sch Elect & Comp Engn, Coll Engn, Tehran, Iran..ORCID iD: 0000-0002-1480-7820
Univ Tehran, Sch Elect & Comp Engn, Coll Engn, Tehran, Iran..
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-5590-0784
2021 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 9, p. 40768-40782Article in journal (Refereed) Published
Abstract [en]

There has been tremendous growth in the Internet of Things (IoT) technologies, and many new applications have emerged. However, cascading failure as one of the major issues in such constrained networks have been neglected. In this paper, we apply an effective clustering approach dubbed as REFIT to enhance network topology robustness via nodes' residual energy. The REFIT protocol divides the network processes into two stages, (i) set-up state and (ii) steady state. The Cluster Head (CH) selection method determines the supreme set of CHs that balances load distribution. The routing method is developed with the modified Particle Swarm Optimization (PSO) algorithm and the objective function to find the supreme set of Relay Nodes (RNs). These complete methods are combined into a set-up state to construct an optimal routing tree that links these CHs to the sink via RNs. In steady state, we model the routing tree to Conditional Directed Acyclic Graph (C-DAG) infrastructure that leads to shortcut routes. Simulation results on MATLAB Simulink have demonstrated that compared with the state-of-the-art works, REFIT can significantly promote network robustness against cascading failure.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2021. Vol. 9, p. 40768-40782
Keywords [en]
Power system protection, Power system faults, Robustness, Routing, Internet of Things, Wireless sensor networks, Network topology, IoT, cascading failure, clustering, particle swarm optimization, fault tolerance
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-53792DOI: 10.1109/ACCESS.2021.3065293ISI: 000631191100001Scopus ID: 2-s2.0-85102620249OAI: oai:DiVA.org:mdh-53792DiVA, id: diva2:1541582
Available from: 2021-04-01 Created: 2021-04-01 Last updated: 2021-06-21Bibliographically approved

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Fotouhi, Hossein

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