ct 18: e1

Research Article

IOTA Based Anomaly Detection Machine learning in Mobile Sensing

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  • @ARTICLE{10.4108/eai.11-1-2022.172814,
    author={Muhammad Shoaib Akhtar and Tao Feng},
    title={IOTA Based Anomaly Detection Machine learning in Mobile Sensing},
    journal={EAI Endorsed Transactions on Creative Technologies: Online First},
    volume={},
    number={},
    publisher={EAI},
    journal_a={CT},
    year={2022},
    month={1},
    keywords={machine learning, deep learning, deep neural network, anomaly detection},
    doi={10.4108/eai.11-1-2022.172814}
}
  • Muhammad Shoaib Akhtar
    Tao Feng
    Year: 2022
    IOTA Based Anomaly Detection Machine learning in Mobile Sensing
    CT
    EAI
    DOI: 10.4108/eai.11-1-2022.172814
Muhammad Shoaib Akhtar1,*, Tao Feng1
  • 1: School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China
*Contact email: 13.cs.194@gmail.com

Abstract

In this proposed method, iMCS can detect and prevent fake sensing activities of mobile users using machine learning techniques. Our iMCS solution uses behavioral analysis based on participants' reliability scores to detect variation in behavior of users and introduces a new role in a distributed system of MCS architecture to validate the collected data. To evaluate the incentive based on the participant's sensory data and data quality, to properly distribute profit among the participants, we employ the Shapley Value approach. The evaluation results demonstrate that our method is effective in both quality estimations and incentive sharing.

Keywords
machine learning, deep learning, deep neural network, anomaly detection
Received
2021-08-20
Accepted
2022-01-05
Published
2022-01-11
Publisher
EAI
http://dx.doi.org/10.4108/eai.11-1-2022.172814

Copyright © 2022 Muhammad Shoaib Akhtar et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.