IOTA-Based Mobile Crowd Sensing: Detection of Fake Sensing Using Logit-Boosted Machine Learning Algorithms

Table 1

Comparative analysis.

References	Technique	Dataset	Outcome	Efficiency
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%