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• W3028457518 startingPage "107327" @default.
• W3028457518 abstract "Existing research on cascading failures of wireless sensor networks (WSNs) fails to take into account the role of the sink node on network load distribution, and rarely involves how to improve network robustness, so it has obvious limitations. To this end, this paper presents a sink-oriented cascading model for WSNs. On this basis, a memetic algorithm MA-TOSCA is proposed to help WSNs resist cascading failures via topology optimization, in which the local search operator is designed based on a new network balancing metric “sink-oriented betweenness entropy”. Moreover, we apply network statistics to identify the correlation between typical network properties and network robustness. Extensive simulations have shown that the proposed model can properly characterize the cascading process of WSNs and MA-TOSCA can find more robust topology with less time compared to existing algorithms. In addition, we discover that a network with the “onion-grid topology structure” is highly robust; the network communication efficiency, the modularity and the clustering coefficient is positively related to network robustness and the average shortest path length is negatively related to network robustness." @default.
• W3028457518 created "2020-05-29" @default.
• W3028457518 creator A5035734476 @default.
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• W3028457518 date "2020-08-01" @default.
• W3028457518 modified "2023-10-02" @default.
• W3028457518 title "Topology optimization against cascading failures on wireless sensor networks using a memetic algorithm" @default.
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• W3028457518 doi "https://doi.org/10.1016/j.comnet.2020.107327" @default.
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