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References | Technique | Dataset | Outcome | Efficiency |
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Yang et al. [26] | Data quality-aware truth estimation and surplus sharing method for Mobile crowd sensing | Real-time data for mobile sensing | Quality estimation, mobile crowd sensing | 89% |
Arafeh et al. [27] | Blockchain-based architecture | MCC dataset | Detection of fake sensing in Mobile crowd sensing | 92% |
Kucuk et al. [28] | Design with IoT technologies | IoT-based data | Crowd sensing aware disaster | 80% |
Mrazovic [29] | Crowd sensing-driven route optimization algorithms | Self-created | Smart urban mobility | 93% |
Haseeb et al. [30] | Crowd sensing IOT based | Real-time data for mobile sensing | Detection of fake sensing in mobile crowd sensing | 91% |
Kianoush et al. [31] | Blockchain-based fake detection | Self-created | Detection of fake sensing in mobile crowd sensing | 87% |
Owoh and Singh [32] | Deep learning-based fake sensing | Real-time data for mobile sensing | Detection of fake sensing in mobile crowd sensing | 85% |
Ali Al-Muqarm and Rabee [33] | Cloud computing/edge computing | Cloud-based dataset | Detection of fake sensing in mobile crowd sensing | 82% |
Zhou et al. [34] | Wifi-based route optimization and mobility crowd sensing | Wifi-based data collection | Detection of fake sensing in mobile crowd sensing | 83% |
Louta et al. [35] | Blockchain/federated learning | Real-time data for mobile sensing | Detection of fake sensing in mobile crowd sensing | 88% |
Sisi and Souri [36] | Blockchain | Real-time data for mobile sensing | Quality estimation, mobile crowd sensing | 90% |
Reddy et al. [22] | Machine learning, support vector machine | Data for mobile sensing | Quality estimation, mobile crowd sensing | 96.4% |
Feng et al. [24] | Machine learning, random forests | Data for mobile sensing | Quality estimation, mobile crowd sensing | 87.5% |
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