Born This Way: A Self-Organizing Evolution Scheme With Motif for Internet of Things Robustness

Abstract

The span of Internet of Things (IoT) is expanding owing to numerous applications being linked to massive devices. Subsequently, node failures frequently occur because of malicious attacks, battery exhaustion, or other malfunctions. A reliable and robust network topology can alleviate the cascading collapse caused by local node failures. Existing optimization methods for fixed topologies enhance the robustness of the IoT topology by reconstructing the connections among the devices. However, the application of existing algorithms requires global topology optimizations or local adjustments when new nodes are added, which leads to high computational complexity. To address this problem, based on neuroevolution and network motifs, this study proposes an evolutionary algorithm to generate a robust IoT topology called “Born This Way: a self-organizing evolution scheme with Motif” (BTW-Motif). Using novel mutation and crossover operators, BTW-Motif generates an IoT topology with intrinsic robustness when new nodes are added. We design an adaptive edge density control mechanism to avoid an increase in energy consumption resulting from redundant connections. Specifically, BTW-Motif innovatively introduces network motif as a guide structure which has been proven to have a positive effect on the network robustness. Experiments indicate that BTW-Motif can effectively produce a robust topology. With different network sizes and edge densities, BTW-Motif can generate more robust topologies compared with the existing topology optimization algorithms. And the time consumption for the large-scale topology to achieve similar robustness is reduced by 50%.