طرح تشخیص داده پرت برای شبکه های حسگر بی سیم
ترجمه نشده

طرح تشخیص داده پرت برای شبکه های حسگر بی سیم

عنوان فارسی مقاله: DODS: یک طرح تشخیص توزیع شده داده پرت برای شبکه های حسگر بی سیم
عنوان انگلیسی مقاله: DODS: A Distributed Outlier Detection Scheme for Wireless Sensor Networks
مجله/کنفرانس: شبکه های کامپیوتری - Computer Networks
رشته های تحصیلی مرتبط: کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم ها و محاسبات، معماری سیستم های کامپیوتری
کلمات کلیدی فارسی: شبکه های حسگر بی سیم، تشخیص داده پرت، طبقه بندی Bayes
کلمات کلیدی انگلیسی: Wireless sensor networks، Outlier detection، Bayes classifier
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.comnet.2019.06.014
دانشگاه: Department of Computer Science, University of Batna 2, Algeria
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4/205 در سال 2018
شاخص H_index: 119 در سال 2019
شاخص SJR: 0/592 در سال 2018
شناسه ISSN: 1389-1286
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 9
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E12753
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Related work


3- Distributed outlier detection scheme


4- Performance evaluation


5- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


In many wireless sensor network (WSN) applications, where a plethora of nodes are deployed to sense physical phenomena, erroneous measurements could be generated mainly due to the presence of harsh environments and/or to the depletion of a sensor’s battery. The measurements that significantly deviate from a normal behavior of sensed data are considered as outliers. To address the problem of detecting these outliers in wireless sensor networks, we propose a new algorithm, called Distributed Outlier Detection Scheme (DODS), in which multiple sensed data types are considered and where outliers are detected locally by each node, using a set of classifiers, so that neither information about neighbors is needed to be known by other nodes nor a communication is required among them. These characteristics allow the proposed scheme to be scalable and efficient in terms of both energy consumption and communication cost. The functionalities of the proposed scheme have been validated through extensive simulations using real sensed data obtained from Intel-Berkeley Research Lab. The obtained results demonstrate the efficiency of the proposed scheme in comparison to the surveyed algorithms.


Introduction


The advances in the fields of transistors and semiconductor devices have led to the deployment of wireless sensor networks (WSNs). A wireless sensor network (WSN) is a self-organized network that consists of a large number of low-cost and low-powered sensor devices, which can be deployed in a field, in the air, in vehicles, on bodies, underwater, and inside buildings. These small sensing devices can cooperatively monitor real world physical or environmental conditions, such as temperature, pollution, pressure, light, voltage, humidity and motion. They are also considered as particular networks which are widely used in commercial and industrial areas, for example, transportation tracking, environmental and habitat monitoring, healthcare, etc. Moreover, in a military applications, WSNs can be used for target tracking and battlefield surveillance. In many of these applications, the data sensed by nodes are often unreliable. The quality of the data is affected by multiple noises and errors, missing values, duplicated data, or inconsistent data [1], without forgetting the low performance of nodes in terms of energy, computational and memory capabilities. These issues generally lead into having the generated data unreliable and inaccurate. One of the most sources that influence the quality of sensed data are outliers. We can define outliers as those measurements that significantly deviate from the normal pattern of the sensed data [1]. It means that the sensed data should be in coherence with a pattern which represents the reality of the sensed data. Therefore, it is clear that outlier detection is a crucial task in WSNs as it improves the quality of data, the security of the system, and maximizes the lifetime of the network.

  • اشتراک گذاری در

دیدگاه خود را بنویسید:

تاکنون دیدگاهی برای این نوشته ارسال نشده است

طرح تشخیص داده پرت برای شبکه های حسگر بی سیم
نوشته های مرتبط
مقالات جدید
لوگوی رسانه های برخط

logo-samandehi

پیوندها