بازیابی احتمالاتی از داده های حساس ناقص در IoT
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بازیابی احتمالاتی از داده های حساس ناقص در IoT

عنوان فارسی مقاله: بازیابی احتمالاتی از داده های حساس ناقص در IoT
عنوان انگلیسی مقاله: Probabilistic Recovery of Incomplete Sensed Data in IoT
مجله/کنفرانس: مجله IEEE اینترنت اشیا - IEEE Internet of Things Journal
رشته های تحصیلی مرتبط: مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: اینترنت و شبکه های گسترده
کلمات کلیدی فارسی: اینترنت اشیا (IoT)، بازیابی اطلاعات سنسورهای از دست رفته، عامل بندی ماتریسی احتمالی، داده های حساس عظیم
کلمات کلیدی انگلیسی: Internet of Things (IoT)، recovery of missing sensor data، probabilistic matrix factorization، massive sensed data
نمایه: JCR - Master Journal List - Scopus
شناسه دیجیتال (DOI): https://doi.org/10.1109/JIOT.2017.2730360
دانشگاه: Korea University - Sejong Metropolitan City - S. Korea and the Eindhoven University of Technology - Netherlands
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2017
ایمپکت فاکتور: 7/137 در سال 2017
شاخص H_index: 31 در سال 2019
شاخص SJR: 1/341 در سال 2017
شناسه ISSN: 2327-4662
شاخص Quartile (چارک): Q1 در سال 2017
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 10
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
کد محصول: E10757
فهرست انگلیسی مطالب

Abstract


I- Introduction


II- Related Work


III- Proposed Method


IV- Experimental Results


V- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


Reliable data delivery in the Internet of Things (IoT) is very important in order to provide IoT-based services with the required quality. However, IoT data delivery may not be successful for different reasons, such as connection errors, external attacks, or sensing errors. This results in data incompleteness, which decreases the performance of IoT applications. In particular, the recovery of missing data among the massive sensed data of the IoT is so important that it should be solved. In this paper, we propose a probabilistic method to recover missing (incomplete) data from IoT sensors by utilizing data from related sensors. The main idea of the proposed method is to perform probabilistic matrix factorization (PMF) within the preliminary assigned group of sensors. Unlike previous PMF approaches, the proposed model measures the similarity in data among neighboring sensors and splits them into different clusters with a K-means algorithm. Simulation results show that the proposed PMF model with clustering outperforms support vector machine (SVM) and deep neural network (DNN) algorithms in terms of accuracy and root mean square error. By using normalized datasets, PMF shows faster execution time than SVM, and almost the same execution time as the DNN method. This proposed incomplete data–recovery approach is a promising alternative to traditional DNN and SVM methods for IoT telemetry applications.


Introduction


UE to advancements in information technology, the Internet of Things (IoT) has been emerging as the next big thing in our daily lives. It is defined as a global network with an infrastructure that has self-configuring capabilities [1]. The IoT is an intelligent network that connects billions of things via the Internet by using a variety of communications technologies, such as conventional Long Term Evolution (LTE), Wi-Fi, ZigBee, wireless sensor networks (WSNs), Ethernet, as well as specially developed Internet Protocol Version 6 (IPv6) over low-power wireless personal area networks (6LoWPAN), the low-power wide area network from the LoRa Alliance (LoRaWAN), LTE machine type communications (LTE-MTC), narrowband IoT (NB-IoT), and many other communications technologies. Therefore, the IoT is rapidly transforming into a highly heterogeneous ecosystem that provides interoperability among different types of devices and communications technologies. The IoT achieves the goal of intelligent identification, location, tracking, monitoring, and managing of things [2]. It also creates additional value for a better life by sharing the information collected among different things, and it integrates and consolidates services at the edge using different IoT gateways. IoT implementation requires new solutions to integrate different physical objects (things) into a global IoT ecosystem so that all of them can be identified and recognized automatically. To achieve this, we need a reliable transmission medium to communicate among things, and an intelligent processing tool, such as cloud or fog computing, to generate additional value from IoT applications. According to recent analytics, we expect more than 100 billion IoT devices by 2025, whereas global financial revenue from the IoT will grow from US$3.9 trillion to US$11.1 trillion [3]. However, with its future implications, the IoT brings substantial challenges, such as security, privacy, and reliability, which need to be considered as well.

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