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Algorithms
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Deep Learning for Internet of Things
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Deep Learning for Internet of Things

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (20 August 2022) | Viewed by 7868

Special Issue Editor

Dr. Yijun Mo

E-Mail Website
Guest Editor
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: knowledge graph; graph computing; edge computing; network architecture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep learning for Internet of Things is a new research field that has been developing rapidly in recent years. Research has largely been focused on deep learning analysis algorithms and the framework of massive perceptual data for the Internet of Things, deep learning algorithms under resource constraints, and distributed federated learning frameworks. Deep learning for the Internet of Things is widely used in intelligent security, smart grids, the industrial Internet, remote diagnosis, etc. Model compression, task offloading, and resource scheduling for deep computation significantly impact training and reasoning efficiency, analysis, and processing accuracy, and these have thus become research hotspots.

We invite you to submit your latest high-quality research to a Special Issue entitled Deep Learning for the Internet of Things, which can involve theoretical algorithms or application systems. This Special Issue deals with, but is not limited to, the following topics:

  • Model compressing algorithms for deep learning
  • Machine learning, deep learning, and edge computing for IoT
  • Distributed AI computing
  • Applications of deep learning for IoT
  • Efficiency edge–cloud data orchestration
  • Energy-efficient processors for training and inference
  • Hardware for edge computing and machine learning

Dr. Yijun Mo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Deep Learning
  • Edge Computing
  • Model compressing
  • Distributed AI computing
  • Federal Learning
  • Task Orchestration
  • Task offloading resource scheduling

Published Papers (3 papers)

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Research

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18 pages, 1468 KiB  
Article
Inference Acceleration with Adaptive Distributed DNN Partition over Dynamic Video Stream
by Jin Cao, Bo Li, Mengni Fan and Huiyu Liu
Algorithms 2022, 15(7), 244; https://doi.org/10.3390/a15070244 - 13 Jul 2022
Cited by 1 | Viewed by 1787
Abstract
Deep neural network-based computer vision applications have exploded and are widely used in intelligent services for IoT devices. Due to the computationally intensive nature of DNNs, the deployment and execution of intelligent applications in smart scenarios face the challenge of limited device resources. [...] Read more.
Deep neural network-based computer vision applications have exploded and are widely used in intelligent services for IoT devices. Due to the computationally intensive nature of DNNs, the deployment and execution of intelligent applications in smart scenarios face the challenge of limited device resources. Existing job scheduling strategies are single-focused and have limited support for large-scale end-device scenarios. In this paper, we present ADDP, an adaptive distributed DNN partition method that supports video analysis on large-scale smart cameras. ADDP applies to the commonly used DNN models for computer vision and contains a feature-map layer partition module (FLP) supporting edge-to-end collaborative model partition and a feature-map size partition (FSP) module supporting multidevice parallel inference. Based on the inference delay minimization objective, FLP and FSP achieve a tradeoff between the arithmetic and communication resources of different devices. We validate ADDP on heterogeneous devices and show that both the FLP module and the FSP module outperform existing approaches and reduce single-frame response latency by 10–25% compared to the pure on-device processing. Full article
(This article belongs to the Special Issue Deep Learning for Internet of Things)
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25 pages, 1580 KiB  
Article
Learning-Based Online QoE Optimization in Multi-Agent Video Streaming
by Yimeng Wang, Mridul Agarwal, Tian Lan and Vaneet Aggarwal
Algorithms 2022, 15(7), 227; https://doi.org/10.3390/a15070227 - 28 Jun 2022
Cited by 3 | Viewed by 2006
Abstract
Video streaming has become a major usage scenario for the Internet. The growing popularity of new applications, such as 4K and 360-degree videos, mandates that network resources must be carefully apportioned among different users in order to achieve the optimal Quality of Experience [...] Read more.
Video streaming has become a major usage scenario for the Internet. The growing popularity of new applications, such as 4K and 360-degree videos, mandates that network resources must be carefully apportioned among different users in order to achieve the optimal Quality of Experience (QoE) and fairness objectives. This results in a challenging online optimization problem, as networks grow increasingly complex and the relevant QoE objectives are often nonlinear functions. Recently, data-driven approaches, deep Reinforcement Learning (RL) in particular, have been successfully applied to network optimization problems by modeling them as Markov decision processes. However, existing RL algorithms involving multiple agents fail to address nonlinear objective functions on different agents’ rewards. To this end, we leverage MAPG-finite, a policy gradient algorithm designed for multi-agent learning problems with nonlinear objectives. It allows us to optimize bandwidth distributions among multiple agents and to maximize QoE and fairness objectives on video streaming rewards. Implementing the proposed algorithm, we compare the MAPG-finite strategy with a number of baselines, including static, adaptive, and single-agent learning policies. The numerical results show that MAPG-finite significantly outperforms the baseline strategies with respect to different objective functions and in various settings, including both constant and adaptive bitrate videos. Specifically, our MAPG-finite algorithm maximizes QoE by 15.27% and maximizes fairness by 22.47% compared to the standard SARSA algorithm for a 2000 KB/s link. Full article
(This article belongs to the Special Issue Deep Learning for Internet of Things)
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Other

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38 pages, 1397 KiB  
Systematic Review
Integrated Industrial Reference Architecture for Smart Healthcare in Internet of Things: A Systematic Investigation
by Aswani Devi Aguru, Erukala Suresh Babu, Soumya Ranjan Nayak, Abhisek Sethy and Amit Verma
Algorithms 2022, 15(9), 309; https://doi.org/10.3390/a15090309 - 29 Aug 2022
Cited by 14 | Viewed by 3395
Abstract
Internet of Things (IoT) is one of the efflorescing technologies of recent years with splendid real-time applications in the fields of healthcare, agriculture, transportation, industry, and environmental monitoring. In addition to the dominant applications and services of IoT, many challenges exist. As there [...] Read more.
Internet of Things (IoT) is one of the efflorescing technologies of recent years with splendid real-time applications in the fields of healthcare, agriculture, transportation, industry, and environmental monitoring. In addition to the dominant applications and services of IoT, many challenges exist. As there is a lack of standardization for IoT technologies, the architecture emerged as the foremost challenge. The salient issues in designing an IoT architecture encompass connectivity, data handling, heterogeneity, privacy, scalability, and security. The standard IoT architectures are the ETSI IoT Standard, the ITU-T IoT Reference Model, IoT-A Reference Model, Intel’s IoT Architecture, the Three-Layer Architecture, Middle-Based Architecture, Service-Oriented Architecture, Five-Layer Architecture, and IWF Architecture. In this paper, we have reviewed these architectures and concluded that IWF Architecture is most suitable for the effortless development of IoT applications because of its immediacy and depth of insight in dealing with IoT data. We carried out this review concerning smart healthcare as it is among the major industries that have been leaders and forerunners in IoT technologies. Motivated by this, we designed the novel Smart Healthcare Reference Architecture (SHRA) based on IWF Architecture. Finally, present the significance of smart healthcare during the COVID-19 pandemic. We have synthesized our findings in a systematic way for addressing the research questions on IoT challenges. To the best of our knowledge, our paper is the first to provide an exhaustive investigation on IoT architectural challenges with a use case in a smart healthcare system. Full article
(This article belongs to the Special Issue Deep Learning for Internet of Things)
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