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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2024, Vol. 58 Issue (6): 1091-1106    DOI: 10.3785/j.issn.1008-973X.2024.06.001
    
Survey of mobile crowdsensing data processing based on blockchain
Zihao SHAO1(),Ru HUO1,2,*(),Zhihao WANG1,Dong NI1,Renchao XIE1,3
1. Purple Mountain Laboratories, Nanjing 211111, China
2. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
3. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
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Abstract  

A comprehensive evaluation and categorization of blockchain-based mobile crowdsensing (MCS) data processing was conducted, in order to address the wide participation of users, the flexible mobility of collection devices, and the complexity of communication environment in mobile crowdsensing data processing. Firstly, the developments of MCS and blockchain were reviewed, and the challenges of MCS data processing and the characteristics of blockchain were introduced. Secondly, a blockchain-based mobile crowdsensing architecture (BMCA) was designed to achieve decentralized data management, data security assurance, precise data quality evaluation, and enhanced credibility of incentives. Then, existing data processing techniques were sorted from privacy-preserving, data quality evaluation, and incentive mechanism. Finally, the current problems and challenges in resource consumption control, precise data analysis, full-cycle and differentiated privacy-preserving, and integrated mode application of blockchain-based MCS data processing research were discussed, and the potential future research direction was pointed out.

Key words： mobile crowdsensing      blockchain      privacy-preserving      data quality evaluation      incentive mechanism     
Received: 05 February 2024      Published: 25 May 2024
CLC:  TP 393  
Fund:  国家重点研发计划资助项目（2023YFB2704200）.
Corresponding Authors: Ru HUO     E-mail: shaozihao@pmlabs.com.cn;huoru1988@126.com
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Zihao SHAO
Ru HUO
Zhihao WANG
Dong NI
Renchao XIE
Cite this article:

Zihao SHAO,Ru HUO,Zhihao WANG,Dong NI,Renchao XIE. Survey of mobile crowdsensing data processing based on blockchain. Journal of ZheJiang University (Engineering Science), 2024, 58(6): 1091-1106.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2024.06.001     OR     https://www.zjujournals.com/eng/Y2024/V58/I6/1091


基于区块链的移动群智感知数据处理研究综述

针对移动群智感知（MCS）数据处理的用户广泛参与性、采集设备灵活移动性与通信环境复杂性的特点，对基于区块链的移动群智感知数据处理进行评估. 回顾移动群智感知与区块链的发展历程，总结移动群智感知数据处理面临的挑战与区块链技术的特点；设计基于区块链的移动群智感知体系结构（BMCA），实现数据去中心化管理、数据安全保障、数据质量精准评估与激励可信性增强；从隐私保护、数据质量评估、激励机制3个维度，对比分析现有的数据处理关键技术研究工作；探讨基于区块链的移动群智感知数据处理研究在资源消耗控制、数据精准分析、全周期与差异化隐私保护、融合模式应用等方面存在的问题及未来可能的发展方向.


关键词： 移动群智感知,  区块链,  隐私安全保护,  数据质量评估,  激励机制 
Fig.1 Development of blockchain
Fig.2 Blockchain-based MCS architecture (BMCA)
分类文献方法优势不足
数据
上传		[18]PRVB同时保障用户与提供商的隐私指定的提供商存在隐私泄漏
[19]CrowdHB提升计算效率并实现位置隐私保护应用局限于位置数据保护
[20]差异化私有众包框架个性化用户位置与隐私保护计算开销较大，单阶段保护
[21]无证书签密方案确保模块可信性，减少计算资源消耗签密过程存在性能损失
[22]HBIA平衡用户隐私与计算开销未考虑用户的可信性
结果
匹配		[23]PPQC实现隐私保护并保障效率问题存储空间需求大
[24]FedCrowd实现隐私保护任务推荐保护信息类型单一
[25]PPNS降低时间消耗且保障准确性忽视了用户需求的差异性
[26]改进感知平台支持跨域加密任务匹配和安全授权撤销缺少全周期隐私保护
收益
分配		[27]FGCA隐藏历史交易，增强交易隐私保护历史交易难追溯，数据可用性不足
[28]SecBCS满足激励机制的安全和隐私需求依赖可信参与者
[29]BSIS利用许可区块链实现安全能源交付服务应用场景受限
[30]BCFR安全记录无人机中的数据交易记录的数据交易存储存在泄漏可能
多阶段[31]CrowdBLPS（上传+匹配）确保数据质量与用户隐私保护只能保护用户位置信息
[32]区块链+TEE（上传+分配）安全存储数据与精准奖励分配TEE构建仍需可信中心
[33]区块链+k-匿名（上传+分配）加密数据与奖励，实现安全报酬获取数据计算资源消耗大
[34]LPWS（上传+分配）有限任务预算下实现个性化保护计算效率有待提升
Tab.1 Privacy-preserving methods based on blockchain
分类文献方法优势不足
相似性匹配[35]MNM+QGE利用匹配度与聚类保障数据质量并获得合理报酬粗粒度评估指标分级存在误差
[36]Crowdauditing智能合约审计数据完整性并评估质量需要大量可审计人员计算资源消耗大
[37]GTD结合真值发现技术提升数据质量评估准确性与效率只能实现数值型数据评估
[38]CPchain语义相似度衡量需求匹配保证准确性存在恶意竞争用户的可能
[39]GSM+SenseChain+利用QoT和QoI衡量工人和任务偏好准确性依赖用户自身定义的偏好
[40]熵值+GRA-TOPSIS多维度制定评价指标体系并评估属性指标涵盖面较少
声誉
评估		[41]BC-CQAM利用可信机制评估结果可信性计算资源消耗大
[42]信誉链+感知链将数据与信誉评分分离，防止泄漏信誉准确性依赖确定型任务规模
[43]ML+QoI预测用户行为，提升数据质量增加了服务成本
[44]B-CSTM设计EPU计算个人数据质量信息可靠性不应局限于距离
[45]ValidatorRep构建细粒度信任验证评估方法优化问责机制用户初始化声誉并未讨论
[46]TEM量化车辆节点信任度，实现无偏见评估依赖评分者的专业性
异常
识别		[47]TWNS在异常数据中低成本选择可信节点信任值准确性依赖大规模数据
[48]Iroha许可区块链恶意节点识别，阻止虚假数据提供依赖可信验证节点
[49]共识算法+智能合约解决支付不公平、识别消极参与者、防止合作作弊依赖大规模验证者
[50]RC-chain实现数据交易与用户声誉评估应用拓展性有待提升
Tab.2 Data quality evaluation methods based on blockchain
分类文献方法优点缺点
用户为中心[30]BCFR激励高声誉无人机执行任务并防止数据共享免受内部攻击缺乏考虑个体理性
[51]区块链+Beta信誉模型确保数据安全性并鼓励高信誉用户参与应用场景具有局限性
[52]BC-MCSDT基于用户声誉的Stackelberg博弈，保障用户动态策略调整声誉频繁更新导致区块链存储困难
[53]PODV鼓励用户提供高质量数据并具有高吞吐量可扩展性低，数据分析困难
[54]RADT利用贪婪策略来确定赢家，智能合约确保交易可追溯缺失智能合约的计算性能分析
[55]PFPI竞价加密状态下实现用户激励计算复杂度高
[56]三阶段Stackelberg博弈对参与者分类实现差异化激励无法有效支持大规模应用
[57]GAIMMO鼓励矿工合作，保障用户收益需要大规模矿工参与
[58]混合激励模型鼓励用户提供高质量数据，防止搭便车评估属性固定，难以扩展
[59]分布式拍卖在保障安全情况下满足社会效益和个体理性不同智能合约部署存在延迟
发布方为中心[60]时间约束激励模型对任务分类并分别给出激励策略场景受限于两车协同
[61]RbatAlloc识别异常平台，确保机制透明缺乏考虑个体理性
[62]数字水印防止矿工冒名领取奖励引入的矿工收益并未进行考虑
[63]TensorBC考虑冗余赢家与参与者多属性矿工收益并未考虑
[64]DRR确保数据收集平衡性缺乏考虑用户学习能力
[65]BCDT防止参与用户操纵拍卖价格缺乏对发布方共谋的预防
[66]BCC利用可转移奖励的联盟博弈方法激励用户合作未将车辆进行分类
Tab.3 Incentive mechanism methods based on blockchain
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