Deep Learning for IoT Big Data and Streaming Analytics: A Survey

By M. Mohammadi, Ala Al-Fuqaha, Sameh Sorour, Mohsen Guizani. 2017

1,196 citations recorded by the source corpus.

In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.

Subfields: Artificial Intelligence, Building and Construction, Computer Networks and Communications, ML Systems & Hardware

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Citations in the corpus

Federated Learning With Differential Privacy: Algorithms and Performance Analysis (2019, methodology): “When integrating AI into a variety of IoT applications, distributed machine learning (ML) is preferred for many data processing tasks by defining parametrized functions from inputs to outputs as compositions of basic building blocks [4], [5].”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, background): “The smartphone-centric networks of yesteryears are gradually morphing into an Internet of Things (IoT) system [1]–[3] that integrates a heterogeneous mix of wireless-enabled devices ranging from smartphones, to drones, connected vehicles, wearables, sensors, and virtual reality devices.”
Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions (2018, background): “Furthermore, depending on the objective of how a learning architecture will be utilized, DL architectures can be broadly categorized into [35], [166]: generative, discriminative and hybrid.”
Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, methodology): “2018 [16] IoT data analytics through DL N/A In-depth review of ML- and DL-based solutions in IoT”
Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, methodology): “2018 [48] IoT data analytics through DL N/A In-depth review of ML- and DL-based security solutions in IoT”
Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning (2022, background): “Therefore, a great number of major corporations that are building such datasets are discouraged from sharing it publicly [22].”
A Lite Distributed Semantic Communication System for Internet of Things (2020, background): “Although some IoT devices have certain capability to process simple DL models, the limited memory, computing, and battery capability still prevent from wide applications of DL [4].”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, methodology): “e Learning Tools Key Applications FNN RNN DNN ESN SNN DA RL UAV VR CC SON Multi-RAT IoT PL CR DT [3] p p p [32] p p [50] p [51] p p p p [52] p p p [53] p p p p [54] p p p [63] p [55] p p [56] p p p p [57] p p p p p p [58] p p p p p p [59] p p p p p [60] p p p p p p p p [61] p p p p p p p p p [62] p ”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “Therefore, the trade-off between model complexity and accuracy should be carefully considered [66].”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “techniques such as ML and DL have been widely adopted in IoT for enabling intelligent IoT systems [39] thanks to their ability to discover knowledge of IoT data and get insights for realizing different smart applications, e.”
Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT (2021, background): “[1], [2], and it is frequently subjected to correspondingly rising abnormalities and cyberattacks [3], [4].”
Deep Learning With Edge Computing: A Review (2019, neutral): “[61].”
Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, background): “, streaming data analysis [55], mobile multimedia processing [56], manufacturing inspection [57], and health monitoring.”
The Future of Healthcare Internet of Things: A Survey of Emerging Technologies (2020, neutral): “DL is a star among ML algorithms that is implemented for data analytics [162].”
State-of-the-art in artificial neural network applications: A survey (2018, methodology): “The DL techniques require raw image data only [109, 110, 111, 112].”
Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Toward 6G (2021, background): “, text, audio, video, sensory data), variability (constantly changing), veracity (level of confidence or trust in the data), and value (valuable information and business insights out of it) [5].”