Communication-Aware Task Planning for Heterogeneous UUV Swarms in the Internet of Underwater Things

The Internet of Underwater Things (IoUT) extends distributed sensing to marine environments, where unmanned underwater vehicles (UUVs) serve as mobile nodes for flexible underwater operations. This article investigates the heterogeneous UUV multi-type task planning problem (HUMTTP) for a collaborative system comprising autonomous underwater vehicles (AUVs), underwater gliders (UGs), and bionic manta-ray underwater vehicles (BMUVs). Existing methods generally prioritize execution efficiency but often overlook platform heterogeneity and acoustic communication limitations. To address these issues, this article proposes a hierarchical task planning framework called REASOM-UCLNS. In the task allocation layer, the reward-and energy-aware self-organizing map (REASOM) algorithm incorporates a novel neuron-based reward estimation strategy into winner selection. By integrating the estimated platform-specific task rewards with current-aware energy evaluation, REASOM assesses the execution suitability of heterogeneous UUVs and facilitates the efficient allocation of multi-type tasks. In the route planning layer, the underwater cooperative large neighborhood search (UCLNS) algorithm converts the generated task sets into feasible routes for individual UUVs. UCLNS develops two constraint-specific operators to restore route feasibility when communication connectivity or energy balance violations occur. By integrating targeted constraint handling into the destroy-repair process, UCLNS improves route quality while satisfying operational constraints. Numerical results demonstrate the superior performance of the proposed framework. Finally, a lake experiment involving four heterogeneous AUVs further confirms its practical applicability.