一种安全高效的去中心化移动群智感知激励模型  

Safe Efficient and Decentralized Model for Mobile Crowdsensing Incentive

作　　者：周玉莹 马苗[1] 申琪琪 任杰 张明瑞 杨波[1] ZHOU Yuying;MA Miao;SHEN Qiqi;REN Jie;ZHANG Mingrui;YANG Bo(School of Computer Science,Shaanxi Normal University,Xi’an 710119,China;Software Engineering Institute,East China Normal University,Shanghai 200062,China)

机构地区：[1]陕西师范大学计算机科学学院,西安710119 [2]华东师范大学软件工程学院,上海200062

出　　处：《计算机科学》2023年第S02期894-903,共10页Computer Science

基　　金：国家自然科学基金(U2001205,62377031);中央高校基本科研业务费专项资金(2021CSLY021,GK202007033);陕西省重点研发计划(2023-YBGY-241).

摘　　要：针对现有群智感知激励模型因依赖中央机构而存在的信任安全隐患问题和如何高效地完成感知任务问题,提出一种安全高效的去中心化群智感知激励模型。该模型利用区块链技术实现去中心化的用户管理,通过参与者控制智能合约PCSC和任务控制智能合约TCSC,完成任务发布者、参与者和矿工之间的交互和链上交易,实现任务发布、参与者优选和数据质量评估及报酬支付。在参与者优选环节,提出基于BP神经网络的“任务-参与者集合”匹配策略,即分别利用历史数据中参与者的时间和位置属性,找出最适合当前任务的参与者集合,并采用“对胜出者给予报酬、信誉双激励,对愿意参加的非胜出者给予信誉补偿,对适合当前任务而连续不参与者给予信誉惩罚”的自适应信誉更新机制。安全性分析和在国际公开基准数据集Brightkite上对所提模型在任务完成率、感知数据质量、参与者收益及用户参与度方面的高效性测试结果以及在区块链上用Solidity语言对PCSC和TCSC合约的有效性测试结果,均表明所提模型是一种安全高效的去中心化群智感知激励模型。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.

关 键 词：移动群智感知 BP神经网络 参与者优选策略 区块链 信誉管理 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]