IOTA Based Anomaly Detection Machine learning in Mobile Sensing

Authors

DOI:

https://doi.org/10.4108/eai.11-1-2022.172814

Keywords:

machine learning, deep learning, deep neural network, anomaly detection

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.

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Published

11-01-2022

How to Cite

1.
Shoaib Akhtar M, Feng T. IOTA Based Anomaly Detection Machine learning in Mobile Sensing. EAI Endorsed Trans Creat Tech [Internet]. 2022 Jan. 11 [cited 2024 Apr. 18];9(30):e1. Available from: https://publications.eai.eu/index.php/ct/article/view/1404