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Unmanned aerial vehicles (UAVs) empowered with artificial intelligence (AI) have become a new paradigm for on-demand and intelligent marine monitoring. To enable diverse AI applications, numerous computation-intensive tasks (e.g., image recognition, video processing, path planning, etc.) that cannot be locally executed by UAVs need to be timely and effectively offloaded. Multiple unmanned surface vehicles (USVs) integrated into a USV fleet is appealingly advocated to provide abundant computation resources for computation tasks. In this paper, we propose an energy-efficient USV fleets-assisted collaborative computation offloading scheme for smart maritime services. Specifically, we first propose a collaborative computation offloading framework, where UAVs act as the requesters of computation offloading services, and USV fleets are the helpers. Then, the first-price sealed reverse auction with reserve price is utilized to incentivize USV fleets to assist in executing computation tasks of UAVs, where the reserve price guarantees the satisfied benefits of UAVs. Afterwards, to minimize the energy consumption of executing tasks within the USV fleet under the delay constraint, the joint allocation optimization scheme for computation subtasks and computation capacities is proposed based on the Block Coordinate Descent (BCD) and Alternating Direction Method of Multipliers (ADMM). Simulation results demonstrate that the proposed scheme improves the expected revenue and participation degree of the USV fleet and reduces the overall energy consumption of computation offloading compared to conventional schemes.
Zeng et al. (Tue,) studied this question.