Finite State Machines-Based Path-Following Collaborative Computing Strategy for Emergency UAV Swarms

TL;DR

This paper introduces a finite state machine-based strategy for optimizing task offloading in emergency UAV swarms, effectively reducing delay in dynamic disaster environments.

Contribution

It proposes a novel EFSMSG model and a CSABPSO algorithm to improve task scheduling and minimize latency in UAV swarms during emergencies.

Findings

Reduces task processing delay significantly.

Effectively handles dynamic UAV environments.

Improves rescue operation efficiency.

Abstract

Offloading services to UAV swarms for delay-sensitive tasks in Emergency UAV Networks (EUN) can greatly enhance rescue efficiency. Most task-offloading strategies assumed that UAVs were location-fixed and capable of handling all tasks. However, in complex disaster environments, UAV locations often change dynamically, and the heterogeneity of on-board resources presents a significant challenge in optimizing task scheduling in EUN to minimize latency. To address these problems, a Finite state machines-based Path-following Collaborative computation strategy (FPC) for emergency UAV swarms is proposed. First, an Extended Finite State Machine Space-time Graph (EFSMSG) model is constructed to accurately characterize on-board resources and state transitions while shielding the EUN dynamic characteristic. Based on the EFSMSG, a mathematical model is formulated for the FPC strategy to minimize task processing delay while facilitating computation during transmission. Finally, the Constraint Selection Adaptive Binary Particle Swarm Optimization (CSABPSO) algorithm is proposed for the solution. Simulation results demonstrate that the proposed FPC strategy effectively reduces task processing delay, meeting the requirements of delay-sensitive tasks in emergency situations.