A Distributed Co-Evolutionary Optimization Method With Motif for Large-Scale IoT Robustness

Abstract

Fast-advancing mobile communication technologies have increased the scale of the Internet of Things (IoT) dramatically. However, this poses a tough challenge to the robustness of IoT networks when the network scale is large. In this paper, we present DAC-Motif, a distributed co-evolutionary method for optimizing network robustness based on network motifs. Unlike centralized evolutionary optimization approaches, DAC-Motif uses the technique of Divide-And-Conquer (DAC) to divide the large-scale IoT topology into partitions and then merge the self-evolving partitions into a global robust topology. This approach leverages both distributed computing and asynchronous communication mechanisms to mitigate premature convergence and reduce time complexity for large-scale IoT topologies. In our evaluation, DAC-Motif achieves three to four orders of magnitude shorter running time and over 10% robustness improvement compared to other centralized evolutionary algorithms under a scale of around 5,000 IoT devices.