Reputation Check Algorithm Based on Mobile Security Agent Route Planning
To address the problem of not being able to determine the worker validation of route planning algorithms in the worker self-se-lection 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 a-gents.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 by11％ on the basis of ensuring the quality of sampling.
spatial crowdsourcingclustering algorithmroute planningreputation systemworker self-selection task model
万方数据
维普
