دانلود مقاله ISI انگلیسی شماره 76884
عنوان فارسی مقاله

تشخیص ناهنجاری های فضایی در شبکه های حسگر با استفاده از اطلاعات محلی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
76884 2017 16 صفحه PDF سفارش دهید 14940 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Spatial anomaly detection in sensor networks using neighborhood information
منبع

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

Journal : Information Fusion, Volume 33, January 2017, Pages 41–56

کلمات کلیدی
تشخیص ناهنجاری؛ همجوشی سنسور - شبکه های حسگر؛ WSN همکاری
پیش نمایش مقاله
پیش نمایش مقاله تشخیص ناهنجاری های فضایی در شبکه های حسگر با استفاده از اطلاعات محلی

چکیده انگلیسی

The field of wireless sensor networks (WSNs), embedded systems with sensing and networking capability, has now matured after a decade-long research effort and technological advances in electronics and networked systems. An important remaining challenge now is to extract meaningful information from the ever-increasing amount of sensor data collected by WSNs. In particular, there is strong interest in algorithms capable of automatic detection of patterns, events or other out-of-the order, anomalous system behavior. Data anomalies may indicate states of the system that require further analysis or prompt actions. Traditionally, anomaly detection techniques are executed in a central processing facility, which requires the collection of all measurement data at a central location, an obvious limitation for WSNs due to the high data communication costs involved. In this paper we explore the extent by which one may depart from this classical centralized paradigm, looking at decentralized anomaly detection based on unsupervised machine learning. Our aim is to detect anomalies at the sensor nodes, as opposed to centrally, to reduce energy and spectrum consumption. We study the information gain coming from aggregate neighborhood data, in comparison to performing simple, in-node anomaly detection. We evaluate the effects of neighborhood size and spatio-temporal correlation on the performance of our new neighborhood-based approach using a range of real-world network deployments and datasets. We find the conditions that make neighborhood data fusion advantageous, identifying also the cases in which this approach does not lead to detectable improvements. Improvements are linked to the diffusive properties of data (spatio-temporal correlations) but also to the type of sensors, anomalies and network topological features. Overall, when a dataset stems from a similar mixture of diffusive processes precision tends to benefit, particularly in terms of recall. Our work paves the way towards understanding how distributed data fusion methods may help managing the complexity of wireless sensor networks, for instance in massive Internet of Things scenarios.

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