  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.
