Deep Learning in Industrial Internet of Things: Potentials, Challenges, and Emerging Applications

Ruhul Amin Khalil; Nasir Saeed; Yasaman Moradi Fard; Tareq Y. Al-Naffouri; Mohamed-Slim Alouini

Home/Archive/arXiv

arXiv · 2008.06701

Deep Learning in Industrial Internet of Things: Potentials, Challenges, and Emerging Applications

Ruhul Amin Khalil·Nasir Saeed·Yasaman Moradi Fard·Tareq Y. Al-Naffouri·Mohamed-Slim Alouini

View original source ↗Read source document ↗Export metadata ↓

Abstract

The recent advancements in the Internet of Things (IoT) are giving rise to the proliferation of interconnected devices, enabling various smart applications. These enormous number of IoT devices generates a large capacity of data that further require intelligent data analysis and processing methods, such as Deep Learning (DL). Notably, the DL algorithms, when applied in the Industrial Internet of Things (IIoT), can enable various applications such as smart assembling, smart manufacturing, efficient networking, and accident detection-and-prevention. Therefore, motivated by these numerous applications; in this paper, we present the key potentials of DL in IIoT. First, we review various DL techniques, including convolutional neural networks, auto-encoders, and recurrent neural networks and there use in different industries. Then, we outline numerous use cases of DL for IIoT systems, including smart manufacturing, smart metering, smart agriculture, etc. Moreover, we categorize several research challenges regarding the effective design and appropriate implementation of DL-IIoT. Finally, we present several future research directions to inspire and motivate further research in this area.

Explore related subjects

eess.SP

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Ruhul Amin Khalil, Nasir Saeed, Yasaman Moradi Fard, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini. 2020-08-15. Deep Learning in Industrial Internet of Things: Potentials, Challenges, and Emerging Applications. https://arxiv.org/abs/2008.06701

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Adaptive Polynomial Chaos Expansion for Uncertainty Quantification of Radio Wave Propagation over Irregular Terrains

Accurate modeling of radio wave propagation over irregular terrains is crucial for designing reliable wireless communication systems in such environments, yet uncertainties in the antenna configuration are not quantified within deterministic models. This paper develops a fixed number of samples adaptive polynomial chaos expansion (APCE) method for uncertainty quantification (UQ) of radio wave propagation over realistic irregular terrains, where the model evaluations are obtained using a two-way parabolic wave equation (PWE) method. The proposed APCE method is designed to construct a compact and stable PCE model from a prescribed and limited set of simulations. The polynomial basis is enriched using an anisotropic basis extension algorithm driven by variance contributions, while validation behavior and the available sample size are used to control basis growth. The convergence analysis shows decreasing validation errors and improved robustness as the sampling budget increases, with lower trial-to-trial variability than the baseline adaptive PCE method, which uses the same variance-driven basis extension strategy. For two realistic terrain profiles, the proposed method accurately predicts the mean and the 5-95 percentile range of the path loss, and improves the estimation of the standard deviation compared with standard and sparse PCE, using only 30 PWE simulations. APCE outperforms standard and sparse PCE, with the largest gains observed for the 5th and 95th percentile estimates, and its construction time is close to that of standard PCE and much lower than that of sparse PCE based on least angle regression. As the sample size increases, APCE maintains low errors with reduced trial-to-trial variability.

eess.SP↗

arXiv2026

Overlap-Summation-Based Pulse Shaping Transceiver for Affine Frequency Division Multiplexing

Affine frequency division multiplexing (AFDM) has recently emerged as a promising waveform for doubly-selective channels. A direct-windowing-based pulse shaping transceiver (PS-AFDM) was proposed to suppress the Doppler sidelobes, thus improving the accuracy of channel estimation. We observe that, when the path delays and Doppler shifts are randomly distributed, the legacy PS-AFDM scheme significantly increases the condition number of the effective channel matrix. The resulting ill-conditioning degrades the numerical stability of channel equalization in noise and consequently increases the BER. To address this issue, this letter applies the existing weighted overlap-summation (WOLA) transceiver to AFDM and proposes a novel channel-aware (CA) receive shaping window design, which simultaneously achieves accurate channel estimation and robust equalization performance, at the cost of additional prefix overhead and receive-window calculation. The resulting scheme is termed CAWOLA-AFDM. Compared with the legacy WOLA scheme, which employs a fixed receive window, the proposed CAWOLA design exploits the channel estimates of slowly varying power gains and Doppler shifts to design a channel-tailored receive shaping window in closed form, thereby further enhancing channel-estimation accuracy while maintaining the channel condition number when a Nyquist prototype window is adopted. The proposed CAWOLA receive-window design aims to produce an effective AFDM channel with more compact support in the DAFT domain. The source code for the simulations is provided at https://github.com/SANIS-HITSZ/Waveform_AFDM.

eess.SP↗

arXiv2026

Parametric Channel Estimation with Hardware Impaired Hybrid Beamformers: Sensing, Communications, and Power Efficiency Tradeoffs

Due to high power consumption and hardware costs of fully digital arrays, hybrid beamformers are often considered as a more economic alternative. Furthermore, using high resolution analog to digital converters (ADCs) can also have prohibitive power consumption, which leads to lower resolution converters being considered for radio frequency (RF) front end design. The finite quantization resolution as well as the nonlinearities caused by the power amplifiers (PAs) and low noise amplifiers (LNAs) can have a substantial impact on system performance. While widely studied for communications, the impact of hardware impairments on sensing performance is considerably less explored. In this work, we study the interplay between hybrid beamforming architectures, hardware impairments, and sensing and communications performance. Additionally, we define the concept of double-isotropy for pilot-combiner pairs, formalizing the notion of a perfectly energy-fair beam sweep. The multiple start (MS) space alternating generalized expectation maximization algorithm (SAGE) is also introduced, aimed at addressing the optimization issues arising from parametric channel estimation (PCE) in hybrid beamformed systems. We then provide a set of numerical results assessing the impacts of beamformer architecture and ADC resolution on PCE, sensing, and communications performance. The results show that medium resolution ADCs lead to the most power efficient configurations, with the best tradeoff between power consumption and performance for the majority of beamforming architectures. Additionally, fully digital beamforming architectures with high resolution converters can often be substituted for a hybrid beamformer setup with medium resolution converters without significant performance loss at a lower power consumption and overall hardware cost.

eess.SP↗