基于模糊逻辑信誉的众包任务分配算法

Fuzzy-Logic-Reputation-based Crowdsourcing Task Allocation Algorithm

摘要:

针对海洋物联网中水下节点多跳传输能耗高、链路稳定性差及大规模任务传输效率低等问题，提出一种基于模糊逻辑信誉的众包任务分配算法。该方法构建众包式协同传输框架，综合船舶历史履约率、数据完整性和用户反馈计算综合信誉值，并结合任务规模感知与梯度增强转发实现动态任务分配。仿真结果表明，该算法在任务完成率、传输稳定性和能量损耗控制方面均优于对比方法，尤其在低船舶密度和大任务场景下表现出更好的适应能力与鲁棒性。在相同船舶密度下，平均任务完成率达87.0%，较3种对比算法分别提高8.3%、9.9%和25.1%，平均能量损耗分别降低28.8%、39.9%和71.7%。结果表明，该方法能够有效降低水下节点传输负担，提高海洋数据外送效率。

Abstract:

To address the problems of high energy consumption, poor link stability, and low transmission efficiency for large-scale tasks caused by multi-hop transmission of underwater nodes in the Ocean Internet of Things (OIoT), a crowdsourcing task allocation algorithm based on fuzzy logic reputation is proposed. First, a crowdsourcing-based collaborative transmission framework for OIoT is constructed. The comprehensive reputation value of ships is calculated by jointly considering historical fulfillment rate, data integrity, and user feedback. On this basis, a reputation-driven ship selection mechanism is designed. Then, dynamic task allocation in complex marine environments is achieved by integrating task-scale awareness and gradient-enhanced forwarding. Simulation results show that the proposed algorithm outperforms the baseline algorithms in task completion rate, transmission stability, and energy consumption control, especially under low ship-density and large-task scenarios, where it exhibits better adaptability and robustness. Under the same ship density, the average task completion rate reaches 87.0%, which is 8.3%, 9.9%, and 25.1% higher than that of the other three baseline algorithms, respectively, while the average energy consumption is reduced by 28.8%, 39.9%, and 71.7%, respectively. These results indicate that the proposed algorithm can effectively reduce the transmission burden of underwater nodes and improve the efficiency of ocean data offloading.