面向单任务质量保障的移动群智感知任务分配

在移动群智感知中，现有的任务分配方法大多关注平台的整体感知质量，未充分考虑任务对工人、预算等资源的竞争，无法有效保障大规模任务分配场景下每个任务的感知质量，从而导致平台资源利用率降低。针对该问题，提出一种面向单任务质量保障的任务分配方法。为高效利用平台预算，考虑任务的难度和位置以及工人的设备能耗和理性因素，设计平台的激励成本。为保障每个任务的感知质量，考虑任务间的资源竞争情况并设计2种衡量指标，分别是从任务的角度根据差异化感知质量需求设计任务覆盖效率，以及从工人的角度基于最大熵原理设计工人利用效率，将这2种衡量指标相结合作为平台的系统效用，在平台资源有限的情况下以平台系统效用最大化为优化目标，提出一种融合交叉和变异操作的天牛群（BSO）算法。实验结果表明，与PSO、GA等基线方法相比，BSO算法的系统效用最大值平均提升13.51%，寻优速度平均提高40.61%，利用该算法获取的具有最大系统效用的任务分配方案可以有效保障每个任务的感知质量。

Task Allocation Towards Individual Task Quality Assurance in Mobile Crowd Sensing

In Mobile Crowd Sensing(MCS), most existing task allocation methods focus on the overall sensing quality of the platform and do not fully consider task competition for workers, budgets and other resources.This cannot effectively guarantee the sensing quality of each task in a large-scale task allocation scenario, resulting in a reduction of resource utilization of the platform.To solve this problem, a task allocation method for individual task quality assurance is proposed.To efficiently use the platform budget, the incentive cost of the platform is designed by considering the difficulty and location of tasks and the energy consumption and rationality of worker equipment.To ensure the sensing quality of each task, the resource competition between tasks is considered and two measurement indicators are implemented:the task coverage efficiency according to the differentiated perceived quality requirements from the perspective of tasks and the worker utilization efficiency based on the maximum entropy principle from the perspective of workers.These two measurement indicators are combined as the system utility of the platform.In the case of limited platform resources, a Beetle Swarm Optimization(BSO) algorithm that integrates crossover and mutation operations is proposed for maximizing the system utility of the platform.The experiments show that the maximum system utility and optimization speed of the BSO algorithm increases by 13.51% and 40.61% on average, respectively, compared with Particle Swarm Optimization(PSO), Genetic Algorithm(GA), and other baseline methods.The task allocation scheme with the maximum system utility obtained by this algorithm can effectively ensure the sensing quality of each task.