基于可信工人路径规划的声望检验算法

Reputation Check Algorithm Based on Mobile Security Agent Route Planning

针对工人自选择任务模式下无法确定路径规划算法工人可信度的问题,提出了基于可信工人路径规划的声望检验算法。首先,根据欧氏距离与区域限制对任务进行k均值聚类,缩小可信工人候选任务集,从而降低路径规划算法的时间复杂度;其次,提出了利用权值k对可信工人路径优化的算法,该算法综合考虑可检验工人数量、距离成本和截止时间计算任务权值,基于任务权值最大化寻找最佳插入位置来建立和更新可信工人的执行路径;最后,通过采用抽查检验的方式对可信工人与普通工人提交数据对比的结果来建立工人声望模型,该模型使用了可信性、不可信性和不确定性三个参数的期望值来描述工人的可靠性。在模拟数据集和gMission真实数据集上对声望检验算法进行实验,结果表明提出的声望校验算法数据采集质量提升13%,路径规划算法在保证抽查质量的基础上,可信工人的旅行成本降低11%。

To address the problem of not being able to determine the worker validation of route planning algorithms in the worker self-selection task model,a reputation check algorithm based on mobile security agents is proposed.Firstly,k-mean clustering of tasks based on Euclidean distance with region restriction narrows the set of mobile security agent candidate tasks,which reduces the time complexity of the route planning algorithm.Secondly,an algorithm for optimizing the route of mobile security agents by using the weight k is proposed.The algorithm comprehensively considers the number of checkable workers,distance cost and deadline to calculate the task weight,and finds the best insertion position based on the maximization of task weight to establish and update the execution route of mobile security agents.Finally,the worker reputation model is established by comparing the results of the data submitted by the mobile security agents and the ordinary workers by means of random inspection.The model uses the expectations of the three parameters of believability,disbelievability and uncertainty to describe the reliability of the workers.Experiments are conducted on the synthetic dataset and the gMission real dataset for the reputation check algorithm,which show that the data collection quality of the reputation check algorithm proposed is improved by 13%,and the route planning algorithm reduces the travel cost of mobile security agents by 11%on the basis of ensuring the quality of sampling.