Mobile Big Data: Concepts and Features Mobile Big Data Analytics Using Deep Learning and Apache Spark

By Mohammad Abu Alsheikh, Dusist Niyato, Shaowei Lin, Hwee-Pink Tan, Zhu Han

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Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “In [220], the capabilities of DL models in mobile big data analytics were investigated using a distributed learning framework that executes an iterative MapReduce task on several parallel Spark workers.”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “The authors of [41] and [42] focused their attention on the benefit of deep learning in wireless TABLE I THE TOPICS OF SURVEY PAPERS ON DIFFERENT ML PARADIGMS IN WIRELESS NETWORKS”
Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Toward 6G (2021, background): “• There exist various DL models to learn useful patterns from unlabeled data, which is the most common type of data in today’s mobile networks [300], in an unsupervised”
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, methodology): “Finally, the rest of the papers (indicated by Other in Figure 9) where based on models such as Autoencoders [71], [123], Inception Neural Networks (INN), or the other frameworks [105] for a total of 7 papers with an average accuracy”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “learning framework for this purpose [17].”
Mobile Edge Computing: A Survey (2018, background): “Big data analytics is a process of extracting meaningful information from raw data which could be helpful for marketing and targeted advertising, customer relations, business intelligence, context-aware computing, health care, etc. [59], [60].”
Deep Learning for RFID-Based Activity Recognition (2016, unknown): “Earlier research showed that deep learning can be applied to data from mobile phone sensors or an accelerometer for recognition of person’s simple physical activities [20,21].”
Smart Cities: A Survey on Data Management, Security, and Enabling Technologies (2017, methodology): “[231] use deep learning and a framework over Apache Spark for mobile big data analytics.”
Deep Learning for Anomaly Detection: A Survey (2019, background): “1 Viaene et al. [2005] VAE Section 11.”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, unknown): “, data can be generated from many sources, such as mobile users and IoT devices, and in different types) [234].”
A Survey on Blockchain for Big Data: Approaches, Opportunities, and Future Directions (2020, background): “It is reasonable since data can be generated by malfunctioning or uncalibrated IoT devices, untrusted devices, and can be transmitted to the data center via fading and dynamic wireless environments [56].”
Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing (2018, methodology): “[13] proposed a framework to combine deep learning algorithms and Apache”
Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey (2018, methodology): “Alsheikh et al. [149] proposed analysis of mobile (human activity recognition) data using a deep learning neural network.”