Research On Task Assignment Methods Of Mobile Crowdsensing Based On Edge Interaction Assistance

Mobile crowdsensing(MCS)uses mobile devices carried by the crowd to collect data to obtain better environmental perception.The traditional MCS system is based on a cloudbased centralized architecture.In this architecture,the gap between terminal devices and the cloud is large.With the rapid growth in the number of mobile devices and wearable devices used,the scale of MCS is also increasing,which will cause great delays and network congestion in task allocation.In order to solve the above problems,this paper introduces edge computing into MCS,and proposes an MCS framework based on edge interaction assistance.It uses edge nodes to disperse the computing load of the MCS platform.Through the information interaction between edge nodes,the edge nodes can obtain enough information to improve the task distribution performance.On the basis of this framework,this subject proposes different task allocation methods for the problems existing in two typical perception scenarios.The specific research content is as follows:(1)In order to solve the problem of fierce competition for resources caused by frequent task requests in hotspots,this paper proposes a two-phase allocation scheme.In the first phase,in order to reduce resource waste and computational pressure,a task selection algorithm based on cosine similarity is designed,taking into account the phenomenon of repeated data or repeated releases between tasks,and judging whether the task is executed based on the similarity between task information.In the second phase,in order to reduce the cost and delay caused by resource competition,the task allocation algorithm based on INSGA was designed.On the basis of the NSGA-Ⅱ algorithm,the probability selection operator based on the logistics distribution and the adaptive crossover and mutation operators are introduced to obtain a better pareto frontier.Through experiments to analyze the performance of the proposed method,it is found that the method significantly improves the task completion rate,and reduces the task cost and time delay.(2)Aiming at the lack of resources of participants in sparse areas,this paper proposes a threestage allocation scheme.In the first stage,a distance-based area partitioning algorithm is designed.The algorithm considers the relevance of the distance between the task and the user,and divides the area where the task and the user are located.In the second stage,a path planning algorithm based on a hybrid ant colony is designed.In the case of considering various time constraints,the hybrid ant colony algorithm is used to allocate tasks in the divided areas.In the third stage,in order to make full use of users’ time resources,a greedybased multi-user cooperation algorithm is designed,while weighing the requirements of time constraints and space-time coverage.The performance of this scheme is analyzed through experiments,and compared with the two baseline algorithms,it is found that this scheme significantly improves the task completion rate while ensuring the coverage of time and space.