دانلود مقاله ISI انگلیسی شماره 76953
عنوان فارسی مقاله

تشخیص ناهنجاری داده آنلاین و تطبیقی برای سیستم های حسگر بی سیم

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
76953 2014 14 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Adaptive and online data anomaly detection for wireless sensor systems
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Knowledge-Based Systems, Volume 60, April 2014, Pages 44–57

کلمات کلیدی
شبکه های حسگر بی سیم؛ طبقه بندی مؤلفه های اصلی یک کلاس؛ تشخیص ناهنجاری داده ها؛ تشخیص ناهنجاری تطبیقی؛ تجزیه و تحلیل داده های حسگر؛ کیفیت داده های حسگر
پیش نمایش مقاله
پیش نمایش مقاله تشخیص ناهنجاری داده آنلاین و تطبیقی برای سیستم های حسگر بی سیم

چکیده انگلیسی

Wireless sensor networks (WSNs) are increasingly used as platforms for collecting data from unattended environments and monitoring important events in phenomena. However, sensor data is affected by anomalies that occur due to various reasons, such as, node software or hardware failures, reading errors, unusual events, and malicious attacks. Therefore, effective, efficient, and real time detection of anomalous measurement is required to guarantee the quality of data collected by these networks. In this paper, two efficient and effective anomaly detection models PCCAD and APCCAD are proposed for static and dynamic environments, respectively. Both models utilize the One-Class Principal Component Classifier (OCPCC) to measure the dissimilarity between sensor measurements in the feature space. The proposed APCCAD model incorporates an incremental learning method that is able to track the dynamic normal changes of data streams in the monitored environment. The efficiency and effectiveness of the proposed models are demonstrated using real life datasets collected by real sensor network projects. Experimental results show that the proposed models have advantages over existing models in terms of efficient utilization of sensor limited resources. The results further reveal that the proposed models achieve better detection effectiveness in terms of high detection accuracy with low false alarms especially for dynamic environmental data streams compared to some existing models.

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پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.