Collaborative Scheduling of Time-dependent UAVs,Vehicles and Workers for Crowdsensing in Disaster Response

TL;DR

This work tackles post-disaster sensing by addressing online scheduling of time-dependent UAVs, vehicles, and workers. It introduces HoCs-MPQ, which models collaboration and conflict as a weighted undirected graph and solves a maximum weight independent set with iterative local search accelerated by multi-priority queues to enable real-time decisions. The approach is NP-hard in general, but the method achieves substantial improvements in task completion and online decision latency, outperforming several baselines by large margins while keeping per-decision times under 3 seconds. The results on real and simulated data demonstrate strong robustness to varying environmental factors and agent configurations, highlighting practical relevance for rapid, scalable disaster response.

Abstract

Frequent natural disasters cause significant losses to human society, and timely, efficient collection of post-disaster environmental information is the foundation for effective rescue operations. Due to the extreme complexity of post-disaster environments, existing sensing technologies such as mobile crowdsensing suffer from weak environmental adaptability, insufficient professional sensing capabilities, and poor practicality of sensing solutions. Therefore, this paper explores a heterogeneous multi-agent online collaborative scheduling algorithm, HoCs-MPQ, to achieve efficient collection of post-disaster environmental information. HoCs-MPQ models collaboration and conflict relationships among multiple elements through weighted undirected graph construction, and iteratively solves the maximum weight independent set based on multi-priority queues, ultimately achieving collaborative sensing scheduling of time-dependent UA Vs, vehicles, and workers. Specifically, (1) HoCs-MPQ constructs weighted undirected graph nodes based on collaborative relationships among multiple elements and quantifies their weights, then models the weighted undirected graph based on conflict relationships between nodes; (2) HoCs-MPQ solves the maximum weight independent set based on iterated local search, and accelerates the solution process using multi-priority queues. Finally, we conducted detailed experiments based on extensive real-world and simulated data. The experiments show that, compared to baseline methods (e.g., HoCs-GREEDY, HoCs-K-WTA, HoCs-MADL, and HoCs-MARL), HoCs-MPQ improves task completion rates by an average of 54.13%, 23.82%, 14.12%, and 12.89% respectively, with computation time for single online autonomous scheduling decisions not exceeding 3 seconds.

Figures (16)

• Figure 1: Collaboration Among Time-Dependent UAVs, Vehicles, and Workers.
• Figure 2: Framework of HoCs-MPQ.
• Figure 3: Types of weighted undirected graph nodes.
• Figure 4: Example of weighted undirected graph generation.
• Figure 5: Example framework diagram of the HoCs-MPQ algorithm flow.