Research On Incentive Mechanism Algorithm In Mobile Crowd Sensing Network

In mobile crowd sensing network,the mutual cooperation of nodes is the premise of data forwarding.Since collecting data and forwarding data consumes the energy and bandwidth of nodes,selfish nodes will seek their own interests and ignore the interests of other nodes.In order to solve the negative impact of selfish nodes on mobile crowd sensing network,this paper proposes a novel incentive mechanism based on reputation and trust(RTM),which defines service quality factor,link reliability factor and time heat factor as pricing factors to comprehensively price and complete message forwarding,the combination of the two incentives will produce better incentives and improve the fairness of the mechanism.The experimental results show that the packet delivery rate,average delay and energy consumption are used as index parameters to prove the effectiveness of the incentive mechanism of reputation and trust.Compared with reputation-based incentive mechanism and trust-based incentive mechanism,RTM has a good performance in terms of effectiveness and feasibility.The location distribution of the participants will affect the timeliness of obtaining the information of the sensing tasks,so the participants in good positions can improve the completion rate of the tasks.Due to the individual differences in the capabilities of the participants,the completion quality of the tasks will be different,so participants with good reputation values can ensure the completion quality of the task.This paper further proposes a utility-based location distribution reverse auction incentive mechanism(ULDM),which takes the distance between the task sensing point and the worker as one of the criteria for screening the sensing task object,considering the role of budget constraints,worker reputation,and location characteristics in the incentive model,select the winner to complete the sensing task,submit the data information,complete the payment transaction,and then define the distance correlation and time correlation to calculate the utility value of the data collected by the winner,verify the practicability of the data,and finally judge whether the winner's reputation value increases or not.The experimental results show that with the packet delivery rate,average delay and energy consumption as the index parameters,ULDM improves the completion quality of the task,strengthens the incentive effect,and makes the winner receive more reasonable compensation.