A Mutation Particle Swarm Optimization Method for Task Scheduling in Seismic Edge Networks

The large-scale microtremor profiling method (LSMPM) operates under a “distributed data-collection and centralized data-recovery” framework, which has led to significant delays in retrieving shear wave velocity structural information, diminished detection efficacy, and the absence of prompt local computation and processing mechanisms for real-time imaging. Effectively harnessing the computational capabilities of acquisition nodes to facilitate wireless, multinode, low-latency collaborative computing remains a significant challenge. We present a task delay optimization and scheduling algorithm based on modified particle swarm optimization (MPSO) to achieve real-time imaging of shear wave velocity structures at the edge of the sensor network. It enhances the mutation process of the standard particle swarm optimization (PSO), utilizing the delay improvement coefficient as a metric to optimize the delay within computing task queues, thereby avoiding local convergence, augmenting global search capabilities, and consequently reducing the maximum completion time <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\textrm {Makespan})$ </tex-math></inline-formula> of tasks. The MPSO algorithm has undergone extensive simulation testing within the CloudSim environment, and the results indicate that, relative to the PSO task scheduling algorithm, the proposed MPSO task scheduling algorithm reduces the standard deviation of the Makespan by approximately 90%, and when the number of edge servers ranges from 10 to 30, the delay improvement coefficient of the proposed MPSO algorithm can surpass 60%, which underscores the superiority of the MPSO in terms of delay optimization efficacy and algorithmic stability.