Topology optimization against cascading failures on wireless sensor networks using a memetic algorithm

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.