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

TL;DR

Addresses latency-sensitive offloading in dynamic Emergency UAV Networks with heterogeneous onboard resources. The authors propose an Extended Finite State Machine Space-time Graph (EFSMSG) to model per-UAV resources and state transitions, and map a task DAG $\Phi=(\Psi,\Gamma)$ to the EFSMSG via FPC; optimization is performed with Constraint Selection Adaptive Binary PSO (CSABPSO) to minimize latency $T(\mathbf{X})$. Key contributions include the EFSMSG framework, the FPC scheduling with cross-slot computation and communication, and the CSABPSO algorithm, plus extensive simulations showing substantial latency reductions against cloud and local processing, and against baseline load-balancing schemes. The results demonstrate the method's practical value for rapid emergency response by coordinating heterogeneous UAV swarms.

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.

Figures (10)

• Figure 1: The architecture of EUN.
• Figure 2: Flight trajectory model of EUN.
• Figure 3: Example of FSM model of UAV.
• Figure 4: Example of EFSM model of EUN.
• Figure 5: Model of EFSMSG.