Abstract: In order to solve the problem of trust safety and inefficient perception task execution in the existing mobile crowdsen-sing incentive model,this paper proposes a safe efficient and decentralized model for mobile crowdsensing incentive.Employing blockchain to decentralize user management,the model completes the interaction and chain transaction among task publisher,participant and miner,realizes task publishing,participant selection,data quality evaluation and payment through PCSC(participant control smart contract) and TCSC(task control smart contract).In the process of participant selection,this paper proposes a “task-participants” matching mechanism based on BP neural network,which refers the time-location attribute of participants in historical data respectively to find out the most suitable participants for the current task.Then an adaptive reputation updating mechanism is suggested,that is,“giving reward and reputation incentive to the winner,giving reputation compensation to the non-winner who is willing to participate,and giving reputation punishment to the continuous non-participants who are suitable for the current task”.Security analysis and experimental results show that the proposed incentive model is safe,efficient and decentra-lized,since it not only can significantly improve the task completion rate,perceived data quality,participants’ benefits and user participation on the international open benchmark Brightkite dataset,but also can work on blockchain due to the efficiency of PCSC and TCSC using Solidity.

Key words: Mobile crowdsensing, Back propagation neural network, Participant selection strategy, Blockchain, Reputation mana-gement