Towards a Stable and Truthful Incentive Mechanism for Task Delegation in Hierarchical Crowdsensing
Haotian Wu, Jun Tao, Bin Xiao · 2020
In order to achieve the desired performance of crowdsensing, the incentive mechanism, which can stimulate the workers to serve the sensing tasks efficiently, is usually indispensable. Different from the existing research efforts of incentive mechanisms, we propose an incentive mechanism to facilitate the delegation of tasks among the workers in hierarchical crowdsensing. Considering the task converging at some skillful workers, which will degrade the system stability and unbalance the workload among the workers, we construct a Stable and Truthful Incentive Mechanism (STIM) to model and restrict the interactions between the requester and the workers. STIM mechanism comprises a queue control algorithm for the workers and an auction scheme with Multi-sEllers for the Divisible tAsks (MEDA), which exploits an optimal winning bids determination strategy and conducts a truthful payment algorithm. The soundness of the modeling and the accuracy of the analysis are verified through extensive simulations.