IOTA Based Anomaly Detection Machine learning in Mobile Sensing
In this proposed method, iMCS can detect and prevent fake sensing activities of mobile users using machine learningtechniques. Our iMCS solution uses behavioral analysis based on participants' reliability scores to detect variation inbehavior of users and introduces a new role in a distribu...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
European Alliance for Innovation (EAI)
2022-03-01
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| Series: | EAI Endorsed Transactions on Creative Technologies |
| Subjects: | |
| Online Access: | https://eudl.eu/pdf/10.4108/eai.11-1-2022.172814 |
| _version_ | 1830230011717615616 |
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| author | Muhammad Akhtar Tao Feng |
| author_facet | Muhammad Akhtar Tao Feng |
| author_sort | Muhammad Akhtar |
| collection | DOAJ |
| description | In this proposed method, iMCS can detect and prevent fake sensing activities of mobile users using machine learningtechniques. Our iMCS solution uses behavioral analysis based on participants' reliability scores to detect variation inbehavior of users and introduces a new role in a distributed system of MCS architecture to validate the collected data. Toevaluate the incentive based on the participant's sensory data and data quality, to properly distribute profit among theparticipants, we employ the Shapley Value approach. The evaluation results demonstrate that our method is effective inboth quality estimations and incentive sharing. |
| first_indexed | 2024-12-18T10:46:34Z |
| format | Article |
| id | doaj.art-343d268c80114ccc90c929ac1c3ac508 |
| institution | Directory Open Access Journal |
| issn | 2409-9708 |
| language | English |
| last_indexed | 2024-12-18T10:46:34Z |
| publishDate | 2022-03-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Creative Technologies |
| spelling | doaj.art-343d268c80114ccc90c929ac1c3ac5082022-12-21T21:10:32ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Creative Technologies2409-97082022-03-0193010.4108/eai.11-1-2022.172814IOTA Based Anomaly Detection Machine learning in Mobile SensingMuhammad Akhtar0Tao Feng1School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, ChinaSchool of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, ChinaIn this proposed method, iMCS can detect and prevent fake sensing activities of mobile users using machine learningtechniques. Our iMCS solution uses behavioral analysis based on participants' reliability scores to detect variation inbehavior of users and introduces a new role in a distributed system of MCS architecture to validate the collected data. Toevaluate the incentive based on the participant's sensory data and data quality, to properly distribute profit among theparticipants, we employ the Shapley Value approach. The evaluation results demonstrate that our method is effective inboth quality estimations and incentive sharing.https://eudl.eu/pdf/10.4108/eai.11-1-2022.172814machine learningdeep learningdeep neural networkanomaly detection |
| spellingShingle | Muhammad Akhtar Tao Feng IOTA Based Anomaly Detection Machine learning in Mobile Sensing EAI Endorsed Transactions on Creative Technologies machine learning deep learning deep neural network anomaly detection |
| title | IOTA Based Anomaly Detection Machine learning in Mobile Sensing |
| title_full | IOTA Based Anomaly Detection Machine learning in Mobile Sensing |
| title_fullStr | IOTA Based Anomaly Detection Machine learning in Mobile Sensing |
| title_full_unstemmed | IOTA Based Anomaly Detection Machine learning in Mobile Sensing |
| title_short | IOTA Based Anomaly Detection Machine learning in Mobile Sensing |
| title_sort | iota based anomaly detection machine learning in mobile sensing |
| topic | machine learning deep learning deep neural network anomaly detection |
| url | https://eudl.eu/pdf/10.4108/eai.11-1-2022.172814 |
| work_keys_str_mv | AT muhammadakhtar iotabasedanomalydetectionmachinelearninginmobilesensing AT taofeng iotabasedanomalydetectionmachinelearninginmobilesensing |