Research On Long-Term Incentive Strategy Of Crowdsensing Based On Payment Member Mechanism

With the rapid development of the Internet of Things and communication technology,the use of mobile intelligent devices is gradually spreading.In this trend,crowdsensing is widely used.Crowdsensing replaces the traditional sensor network and solves large-scale data collection problems by assigning sensor tasks to ubiquitous mobile users.In some environments that require long-term data,such as traffic information monitoring,noise monitoring,etc,long-term involvement of the user community is an essential factor in ensuring the efficiency of data collection tasks.However,users who repeatedly participate in the tasks will consume their equipment resources,leading to increased cost.The platform can not attract users’ long-term participation due to budget constraints.Thus,it is necessary to design appropriate incentives to improve platform utility and ensure the long-term participation of users in the task.Current studies are divided into monetary and non-monetary incentives,but there are still the following problems:(1)The traditional monetary incentive mechanism believes users always pursue utility maximization.To incentivize the participation of users,single-round incentives are optimized by increasing single-task payouts.Long-term incentives are only repeating the single optimal,which causes the dropping of platform profits.(2)Some non-monetary incentives use the user’s historical information to increase the probability of being selected by platform.However,the excitation effect is not as well as the results of monetary incentives,it’s unable to protect the persistence of users,leading to low retention rate.This thesis designs crowdsensing incentive mechanisms from platform utility and user retention rates to address the above issues.The pay member model in life can incentive consumers to keep long-term purchase while ensuring the profits of shopping malls.This thesis deeply analyzes the principle of the paid membership mechanism from the perspective of behavioral economics and introduces the reference dependence into the incentive mechanism to attract users to pay membership fee in the platform actively.It also introduces the sunk cost effect in behavioral economics to improve the retention rate of users.The main work is as follows:(1)To address the problem of low platform utility caused by maximizing user utility in traditional money-based incentive mechanisms,this thesis proposes a platform utility guarantee mechanism based on reference-dependent(PGRD).The high-profit tasks of membership and reference dependency are used to motivate users to actively submit membership fee to increase platform benefits.Finally,through comparison with the ESWM mechanism,the theoretical analysis and experimental results analysis show that the PGRD mechanism is better in ensureing platform utility.(2)To address low user retention rates,on the basis of the PGRD mechanism,this thesis proposes a long-term incentive guarantee mechanism based on sunk cost effect(LGSC).Setting sunk losses and reward gold for the members based on the sunk cost effects,and inspiring members to complete the task in the long-term.This strategy not only improves members status but also achieves long-term incentives.The final results show that the LGSC mechanism can improve the user retention rate.