Research On Task Allocation Optimization Based On Time And Energy In Mobile Crowd Sensing

Mobile crowd sensing(MCS)is an emerging research field,and it’s comes from crowd sourcing.Based on the Internet of Things and wireless sensor network,mobile intelligent devices are utilized to perceive,compute,and store functions,and users using perceptual devices or devices that can independently obtain perceptual data are regarded as participants of the system.Through the participants’ mobile devices to form an interactive and participatory perception network,and the perception task is released to individuals or groups in the network to complete,so as to help professionals or the public to collect data,analyze information and share knowledge.Mobile crowd sensing aims to accomplish a large number of perceptual tasks with a certain quality at a lower cost.It has a broad application space and prospects in smart city,intelligent transportation,environmental monitoring and other aspects.Task assignment algorithm,is the core problem of mobile crowd sensing,it is one of the hotspots in this field.This paper studies the factors affecting the two aspects of mobile crowd sensing and proposes a solution based on the existing solutions.The main work and contribution of this paper are as follows.1.A task classification method based on LDA is proposed to reduce system energy consumption.According to different perception attribute in the system,the application of the LDA model,with the help of a large number of training set to form the LDA complete classifier,The new task is classified by LDA classifier to confirm the perceptual ability required to complete the task.The function of the perceptual device of the known participants in the system is detected,and the task is assigned according to the requirements of the participants’ ability value.This method ensure that participants can complete the task,avoid the energy loss caused by the failure of the task due to the lack of sensory device function,and reduce the unnecessary energy consumption in task allocation.2.An optimization method based on queuing theory and simulated annealing algorithm is proposed in the complex case of system time.Based on the background of this paper,the main factors affecting the time in the system are analyzed,and the optimization method of the task waiting time is described in the form of data and charts.Time optimization is divided into two parts in task allocation,for single task allocation,it is confirmed whether the participants receiving the task have enough time to complete the existing task and appropriate participants are selected.Route planning for multitasking participants to ensure the shortest time to complete the task.3.To solve the task allocation problem in battlefield environment,a scalable mobile crowd sensing model is constructed.Build mathematical models aimed at energy consumption and time optimization to solve path planning problems in specific scenarios.According to the characteristics of the model,a hybrid optimization algorithm based on LDA and simulated annealing is proposed.The experimental simulation results prove the feasibility and superiority of the algorithm in solving path problems,the research results enrich the related research on the application of mobile crowd sensing in battlefield environment.