Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/855
Title: Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey
Authors: Sezer, Ömer Berat
Doğdu, Erdoğan
Özbayoğlu, Ahmet Murat
Keywords: machine learning in IoT
data management and analytics
context awareness
Big data in Internet of Things (IoT)
Issue Date: Feb-2018
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Source: Sezer, O. B., Dogdu, E., & Ozbayoglu, A. M. (2018). Context-aware computing, learning, and big data in internet of things: a survey. IEEE Internet of Things Journal, 5(1), 1-27.
Abstract: Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Meanwhile, with this revolutionary transformation, researchers, implementers, deployers, and users are faced with many challenges. IoT is a complicated, crowded, and complex field; there are various types of devices, protocols, communication channels, architectures, middleware, and more. Standardization efforts are plenty, and this chaos will continue for quite some time. What is clear, on the other hand, is that IoT deployments are increasing with accelerating speed, and this trend will not stop in the near future. As the field grows in numbers and heterogeneity, "intelligence" becomes a focal point in IoT. Since data now becomes "big data," understanding, learning, and reasoning with big data is paramount for the future success of IoT. One of the major problems in the path to intelligent IoT is understanding "context," or making sense of the environment, situation, or status using data from sensors, and then acting accordingly in autonomous ways. This is called "context-aware computing," and it now requires both sensing and, increasingly, learning, as IoT systems get more data and better learning from this big data. In this survey, we review the field, first, from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context-aware computing studies. Finally, we review learning and big data studies related to IoT. We also identify the open issues and provide an insight for future study areas for IoT researchers.
URI: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8110603
https://hdl.handle.net/20.500.11851/855
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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