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کیفیت پیوند برای شبکه های حسگر بی سیم

عنوان فارسی مقاله: روش پیش بینی کیفیت پیوند برای شبکه های حسگر بی سیم مبتنی بر XGBoost
عنوان انگلیسی مقاله: A Link Quality Prediction Method for Wireless Sensor Networks Based on XGBoost
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی فناوری اطلاعات، مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: شبکه های کامپیوتری، مهندسی الگوریتم و محاسبات
کلمات کلیدی فارسی: شبکه های حسگر بی سیم، پیش بینی کیفیت پیوند، XGBoost، الگوریتم فازی بهبود یافته
کلمات کلیدی انگلیسی: Wireless sensor networks, link quality prediction, XGBoost, improved fuzzy C-means algorithm
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2949612
دانشگاه: School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 13
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: ندارد
کد محصول: E13921
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I. Introduction


II. Related Work


III. Division of Link Quality Grade


IV. Link Quality Estimation


V. Link Quality Prediction


Authors


Figures


References

نمونه متن انگلیسی مقاله

Abstract


Link quality is an important factor for nodes selecting communication links in wireless sensor networks. Effective link quality prediction helps to select high quality links for communication, so as to improve stability of communication. We propose the improved fuzzy C-means clustering algorithm (SUBXBFCM) and use it to adaptively divide the link quality grades according to the packet reception rate. The Pearson correlation coefficient is employed to analyse the correlation between the hardware parameters and packet reception rate. The averages of the received signal strength indicator, link quality indicator and the signal to noise ratio are selected as the inputs of the link quality estimation model based on the XGBoost (XGB_LQE). The XGB_LQE is constructed to estimate the current link quality grade, which takes the classification advantages of XGBoost. Based on the estimated results of the XGB_LQE, the link quality prediction model (XGB_LQP) is constructed by using the XGBoost regression algorithm, which can predict the link quality grade at the next moment with historical link quality information. Experiment results in single-hop scenarios of square, laboratory, and grove show that the SUBXBFCM algorithm is effective at dividing the link quality grades compared with the normal division methods. Compared with link quality prediction methods based on the Support Vector Regression and 4C, XGB_LQP makes better predictions in single-hop wireless sensor networks.


Introduction


Wireless sensor networks (WSNs) are multi-hop selforganizing networks that are formed by wireless communication, and they consist of large numbers of inexpensive micro sensor nodes that are deployed in a monitoring area [1]. Their purposes are to collaboratively perceive, collect and process the information of the area and then send it to the observer. Sensors, perceptual objects, and observers form the three elements of a wireless sensor network. WSNs currently have broad application prospects in the fields of smart home, urban transportation and so on. In WSNs, nodes communicate by radio frequencies, and links have characteristics such as asymmetry, irregularity, and directionality [2], [3]. The WSN links are susceptible to multipath effect, loss and adjacent channel interference, which result in unreliable links, low channel quality and frequent topological changes [4]. The quality of the communication links has a significant impact on the performance of the wireless sensor network, such as the life cycle of the network, the throughput of the network and the reliability of transmissions [5]. Selecting a link with poor link quality for communications is likely to cause a large amount of packet loss. The retransmission due to packet loss recovery is the main cause of the increase of network energy consumption [6]. Effective link quality prediction helps to select good links for data transmissions, so as to improve network throughput and to maintain normal network operations and topological stability of networks, thereby improving the performance of wireless sensor networks [7]. It can be seen that the link quality prediction for WSNs is of great significance.

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