Research Article

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

Table 1

Comparative analysis.

ReferencesTechniqueDatasetOutcomeEfficiency

Yang et al. [26]Data quality-aware truth estimation and surplus sharing method for Mobile crowd sensingReal-time data for mobile sensingQuality estimation, mobile crowd sensing89%
Arafeh et al. [27]Blockchain-based architectureMCC datasetDetection of fake sensing in Mobile crowd sensing92%
Kucuk et al. [28]Design with IoT technologiesIoT-based dataCrowd sensing aware disaster80%
Mrazovic [29]Crowd sensing-driven route optimization algorithmsSelf-createdSmart urban mobility93%
Haseeb et al. [30]Crowd sensing IOT basedReal-time data for mobile sensingDetection of fake sensing in mobile crowd sensing91%
Kianoush et al. [31]Blockchain-based fake detectionSelf-createdDetection of fake sensing in mobile crowd sensing87%
Owoh and Singh [32]Deep learning-based fake sensingReal-time data for mobile sensingDetection of fake sensing in mobile crowd sensing85%
Ali Al-Muqarm and Rabee [33]Cloud computing/edge computingCloud-based datasetDetection of fake sensing in mobile crowd sensing82%
Zhou et al. [34]Wifi-based route optimization and mobility crowd sensingWifi-based data collectionDetection of fake sensing in mobile crowd sensing83%
Louta et al. [35]Blockchain/federated learningReal-time data for mobile sensingDetection of fake sensing in mobile crowd sensing88%
Sisi and Souri [36]BlockchainReal-time data for mobile sensingQuality estimation, mobile crowd sensing90%
Reddy et al. [22]Machine learning, support vector machineData for mobile sensingQuality estimation, mobile crowd sensing96.4%
Feng et al. [24]Machine learning, random forestsData for mobile sensingQuality estimation, mobile crowd sensing87.5%