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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2025, Vol. 59 Issue (9): 1814-1825    DOI: 10.3785/j.issn.1008-973X.2025.09.005
    
Decentralized indoor positioning crowdsourcing data quality control method
Xuejun ZHANG(),Junxin KUANG,Chengze LI,Mei LI,Bin ZHANG,Xiaohong JIA
School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
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Abstract  

Existing blockchain-based crowdsourcing methods for fingerprint data lack effective utilization of fingerprint distribution characteristics in data quality control consensus and incentive mechanism reward distribution, affecting data collection quality. To address this issue, a decentralized indoor positioning crowdsourcing data quality control method considering fingerprint distribution features was proposed. A data quality control consensus algorithm was designed based on fingerprint distribution characteristics. Characteristic parameters were estimated by retrieving historical data within identical label classes. User-submitted fingerprint vectors were chained only when their weighted mean square error with historical mean vectors was below a given threshold. Duplicate data were rejected to resist replay attacks. To protect user identity privacy, data were anonymously uploaded with the block ownership verified using the Schnorr protocol. An appropriate incentive function was determined according to the data quality errors between user-submitted and on-chain fingerprint distributions. Experiments on three fingerprint datasets (UJIIndoorLoc, MALL and WiFi-RSS) demonstrated that compared with the original datasets, fingerprint data filtered by the proposed method improved training accuracy of indoor positioning models by approximately 10, 3 and 10 percentage points respectively, effectively enhancing fingerprint data acquisition quality.

Key words： indoor positioning      received signal strength (RSS) fingerprint      blockchain      crowdsourcing collection      data quality     
Received: 31 July 2024      Published: 25 August 2025
CLC:  TP 3-05  
Fund:  国家自然科学基金资助项目（61762058，62366029）；甘肃省重点研发计划资助项目（25YFFA089）；甘肃省教育厅产业支撑基金资助项目（2022CYZC-38）；高校教师创新基金项目（2024A-039）.
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Xuejun ZHANG
Junxin KUANG
Chengze LI
Mei LI
Bin ZHANG
Xiaohong JIA
Cite this article:

Xuejun ZHANG,Junxin KUANG,Chengze LI,Mei LI,Bin ZHANG,Xiaohong JIA. Decentralized indoor positioning crowdsourcing data quality control method. Journal of ZheJiang University (Engineering Science), 2025, 59(9): 1814-1825.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.09.005     OR     https://www.zjujournals.com/eng/Y2025/V59/I9/1814


去中心化的室内定位众包数据质量控制方法

现有基于区块链的众包指纹收集方法在数据质量控制共识、激励机制报酬分配方面缺乏对指纹数据本身分布特征的有效利用，从而影响数据收集质量. 为此提出考虑指纹数据分布特征的去中心化的室内定位众包数据质量控制方法. 根据指纹数据分布特征设计数据质量控制共识算法，通过检索相同标签类内的历史数据估算指纹数据分布的特征参数，当用户提交的指纹数据向量与历史均值向量的带权均方差小于给定阈值时，将用户数据上链，若链上已有该数据则不上链以抵御重放攻击. 为了保护用户身份隐私，将用户数据匿名上传并利用Schnorr协议验证区块所有权. 根据用户提交的指纹分布与链上指纹分布的数据质量误差来确定合适的激励函数. 在UJIIndoorLoc、MALL、WiFi-RSS这3个指纹数据集上的实验表明，与原始数据集相比，通过本研究方法筛选的指纹数据使室内定位模型的训练精度分别提升约10、3、10个百分点，有效提高了指纹数据采集的质量.


关键词： 室内定位,  接收信号强度（RSS）指纹,  区块链,  众包收集,  数据质量 
Fig.1 Structure of block header and block body
Fig.2 Framework of decentralized indoor positioning RSS fingerprint data quality control
Fig.3 Fingerprint data distribution error
层输出形式参数个数
Conv2d_1(Conv2D)(None,1,53,32)192
Conv2d_2(Conv2D)(None,1,53,32)10304
max_pooling2d_1(MaxPooling2D)(None,1,24,64)0
dropout_1 (Dropout)(None,1,13,64)0
flatten_1 (Flatten)(None,832)0
dense_1 (Dense)(None,128)106624
dropout_2 (Dropout)(None,128)0
dense_2 (Dense)(None,10)1290
Tab.1 Network parameters of CNN location model
数据集平均耗时/ms
PoWFQ-
winlen10		PoWFQ-
winlen15		PoWFQ-
winlen20
UJIIndoorLoc21.633333.398734.3033
WiFi-RSS20.990019.162828.4033
MALL18.530018.396721.7110
Tab.2 Average consensus time under different window sizes
滑动窗口大小数据集$ {a_{\rm{train}}} $$ {l_{\rm{train}}} $$ {a_{\rm{val}}} $$ {l_{\rm{val}}} $
PoWFQ-winlen10UJIIndoorLoc0.89190.34880.65632.1507
WiFi-RSS0.79180.65560.50582.0796
MALL0.90930.26170.89270.3614
PoWFQ-winlen15UJIIndoorLoc0.96640.07260.60003.1297
WiFi-RSS0.63971.09720.24054.3345
MALL0.72160.75920.69760.8146
PoWFQ-winlen20UJIIndoorLoc0.87400.40000.63332.1305
WiFi-RSS0.74310.77470.44323.5678
MALL0.70040.80390.71740.7660
Tab.3 Influence of PoWFQ with different window sizes on model performance
数据集PoWFQ-
winlen10		PoWFQ-
winlen15		PoWFQ-
winlen20
UJIIndoorLoc0.1363110.001960?0.145020
MALL0.1967420.0129950.013133
WiFi-RSS0.149871?0.099422?0.075516
Tab.4 Change of silhouette coefficient after PoWFQ screening data set with different window sizes
数据集UJIIndoorLocMALLWiFi-RSS
原始数据19937700018750
PoWFQ-winlen101638448005072
PoWFQ-winlen151183746534917
PoWFQ-winlen201971648746118
Tab.5 Number change of PoWFQ with different window sizes after screening different data sets
数据集ts/ms
PoWPoSDpoSPoDPoWFQ
UJIIndoorLoc1964.60009.72339.366713.616721.6333
MALL427.54008.60338.866710.053318.5300
WiFi-RSS345.02678.84338.990010.003320.9900
Tab.6 Average time of uploading 300 records by different consensus algorithms
共识算法数据隐私双花攻击数据质量
Pod√×√
Pow×√×
Pos×××
Dpos×××
PoWFQ√√√
Tab.7 Functional comparison of different consensus algorithms
Fig.4 Comparison of model training and testing accuracy on three datasets
共识算法数据集$ {a_{{{\mathrm{train}}}}} $$ {l_{{{\mathrm{train}}}}} $$ {a_{{{\mathrm{val}}}}} $$ {l_{{{\mathrm{val}}}}} $
PoWUJIIndoorLoc0.77100.74720.44483.7624
WiFi-RSS0.70560.98210.68871.1992
MALL0.87200.34580.90930.3323
PoSUJIIndoorLoc0.80050.66610.67001.4324
WiFi-RSS0.69990.99770.69621.2308
MALL0.88320.32640.90360.3490
DPoSUJIIndoorLoc0.79570.67990.64121.6682
WiFi-RSS0.67791.07180.65441.3273
MALL0.87160.37040.89570.3506
PoDUJIIndoorLoc0.84320.58760.35265.6292
WiFi-RSS0.30862.81000.07824.4087
MALL0.89000.34250.29623.2239
PoWFQUJIIndoorLoc0.89190.34880.65632.1507
WiFi-RSS0.79180.65560.50582.0796
MALL0.90930.26170.89270.3614
Tab.8 Effect of PoW, PoS, DpoS, PoD and PoWFQ on model performance
数据集原始数据PoWFQ筛选后PoD筛选后
UJIIndoorLoc0.1279070.1363110.019306
MALL0.1907220.1967420.036098
WiFi-RSS0.1434820.1498710.210153
Tab.9 Silhouette coefficient before and after screening different datasets
Fig.5 Reward factor of users in UJIIndoorLoc dataset
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