크라우드센싱 시스템에서 머신러닝을이용한 이상데이터 탐지
Anomaly Data Detection Using Machine Learning in Crowdsensing System
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초록
최근, 별도의 센서를 설치하지 않고 센서가 포함된 사용자의 기기로부터 제공되는 실시간 센싱 데이터를 가지고 새로운 센싱 서비스를 제공하는 크라우드센싱(Crowdsensing) 시스템이 주목받고 있다. 크라우드센싱 시스템에서는 사용자의 조작실수나 통신 문제로 인해 의미 없는 데이터가 제공되거나 보상을 얻기 위해 거짓 데이터를 제공할 수 있어 해당 이상 데이터의탐지 및 제거가 크라우드센싱 서비스의 질을 결정짓는다. 이러한 이상데이터를 탐지하기 위해 제안되었던 방법들은 크라우드센싱의 빠른 변화 환경에 효율적이지 않다. 본 논문은 머신러닝 기술을 활용하여 지속적이고 빠르게 변화하는 센싱 데이터의 특징을 추출하고 적절한 알고리즘을 통해 모델링하여 이상데이터를 탐지하는 방법을 제안한다. 지도학습의 딥러닝 이진분류 모델과 비지도학습의 오토인코더 모델을 사용하여 제안 시스템의 성능 및 실현 가능성을 보인다.
Abstract
Recently, a crowdsensing system that provides a new sensing service with real-time sensing data provided from auser’s device including a sensor without installing a separate sensor has attracted attention. In the crowdsensingsystem, meaningless data may be provided due to a user’s operation error or communication problem, or false datamay be provided to obtain compensation. Therefore, the detection and removal of the abnormal data determines thequality of the crowdsensing service. The proposed methods in the past to detect these anomalies are not efficient forthe fast-changing environment of crowdsensing. This paper proposes an anomaly data detection method by extractingthe characteristics of continuously and rapidly changing sensing data environment by using machine learningtechnology and modeling it with an appropriate algorithm. We show the performance and feasibility of the proposedsystem using deep learning binary classification model of supervised learning and autoencoder model of unsupervisedlearning.
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