Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey

By Omer Berat Sezer, Erdogan Dogdu, A. Ozbayoglu. IEEE Internet of Things Journal, 2018

386 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/44454bc0c23fb3d515540eb64c8636de727abe85

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

Anomaly Detection for IoT Time-Series Data: A Survey (2020, background): “Temporal Context: As the majority of IoT data is generated in the form of time-series data [36] (whether sampled at set intervals or via irregular sampling) there is some implication of temporal correlation between observations—that is the reading at time t is in some-way related to the…”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “To obtain insights from these data, researchers [39-44] have discussed different methods of integrating big data analytical methods with IoT design.”
Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions (2018, methodology): “Moreover, the recent article [43] provided an overview of existing context-aware computing studies along with the learning and big data related works in the direction of intelligent IoT systems.”
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, background): “In [21], different learning techniques, which are suitable for Internet of Things (IoT), are presented, taking into account the unique characteristics of IoT, including resource constraints and strict quality-of-service requirements, and studies on learning for IoT are also reviewed in [22].”
Digital Twin: Enabling Technologies, Challenges and Open Research (2020, background): “These are categorised below into topics of potential importance for this project [67], [68].”