مقاله انگلیسی رایگان در مورد یک طرح تشخیص توزیع شده داده پرت برای شبکه های حسگر بی سیم – الزویر ۲۰۱۹

elsevier

 

مشخصات مقاله
ترجمه عنوان 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
انتشار مقاله سال ۲۰۱۹
تعداد صفحات مقاله انگلیسی ۹ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۴٫۲۰۵ در سال ۲۰۱۸
شاخص H_index ۱۱۹ در سال ۲۰۱۹
شاخص SJR ۰٫۵۹۲ در سال ۲۰۱۸
شناسه ISSN ۱۳۸۹-۱۲۸۶
شاخص Quartile (چارک) Q1 در سال ۲۰۱۸
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر دارد
رفرنس دارد
رشته های مرتبط کامپیوتر
گرایش های مرتبط مهندسی الگوریتم ها و محاسبات، معماری سیستم های کامپیوتری
نوع ارائه مقاله
ژورنال
مجله  شبکه های کامپیوتری – Computer Networks
دانشگاه Department of Computer Science, University of Batna 2, Algeria
کلمات کلیدی شبکه های حسگر بی سیم، تشخیص داده پرت، طبقه بندی Bayes
کلمات کلیدی انگلیسی Wireless sensor networks، Outlier detection، Bayes classifier
شناسه دیجیتال – doi
https://doi.org/10.1016/j.comnet.2019.06.014
کد محصول E12753
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فهرست مطالب مقاله:
Abstract

۱- Introduction

۲- Related work

۳- Distributed outlier detection scheme

۴- Performance evaluation

۵- Conclusion

References

 

بخشی از متن 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.

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