Deep learning in mobile and wireless networking: A survey

C Zhang, P Patras, H Haddadi - IEEE Communications surveys …, 2019 - ieeexplore.ieee.org
The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …

Machine learning/artificial intelligence for sensor data fusion–opportunities and challenges

E Blasch, T Pham, CY Chong, W Koch… - IEEE Aerospace and …, 2021 - ieeexplore.ieee.org
During Fusion 2019 Conference (https://www. fusion2019. org/program. html), leading
experts presented ideas on the historical, contemporary, and future coordination of artificial …

Coedge: Cooperative dnn inference with adaptive workload partitioning over heterogeneous edge devices

L Zeng, X Chen, Z Zhou, L Yang… - IEEE/ACM Transactions …, 2020 - ieeexplore.ieee.org
Recent advances in artificial intelligence have driven increasing intelligent applications at
the network edge, such as smart home, smart factory, and smart city. To deploy …

Deep compressive offloading: Speeding up neural network inference by trading edge computation for network latency

S Yao, J Li, D Liu, T Wang, S Liu, H Shao… - Proceedings of the 18th …, 2020 - dl.acm.org
With recent advances, neural networks have become a crucial building block in intelligent
IoT systems and sensing applications. However, the excessive computational demand …

An empirical study of the impact of hyperparameter tuning and model optimization on the performance properties of deep neural networks

L Liao, H Li, W Shang, L Ma - ACM Transactions on Software …, 2022 - dl.acm.org
Deep neural network (DNN) models typically have many hyperparameters that can be
configured to achieve optimal performance on a particular dataset. Practitioners usually tune …

A survey on deep neural network compression: Challenges, overview, and solutions

R Mishra, HP Gupta, T Dutta - arXiv preprint arXiv:2010.03954, 2020 - arxiv.org
Deep Neural Network (DNN) has gained unprecedented performance due to its automated
feature extraction capability. This high order performance leads to significant incorporation …

Deep-learning-based pedestrian inertial navigation: Methods, data set, and on-device inference

C Chen, P Zhao, CX Lu, W Wang… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Modern inertial measurements units (IMUs) are small, cheap, energy efficient, and widely
employed in smart devices and mobile robots. Exploiting inertial data for accurate and …

Analyzing patient health information based on IoT sensor with AI for improving patient assistance in the future direction

H Fouad, AS Hassanein, AM Soliman, H Al-Feel - Measurement, 2020 - Elsevier
Abstract Internet of Things (IoT) and Artificial Intelligence (AI) play a vital role in the
upcoming years to improve the assistance systems. The IoT devices utilize several sensor …

Transforming large-size to lightweight deep neural networks for IoT applications

R Mishra, H Gupta - ACM Computing Surveys, 2023 - dl.acm.org
Deep Neural Networks (DNNs) have gained unprecedented popularity due to their high-
order performance and automated feature extraction capability. This has encouraged …

On-device deep learning for mobile and wearable sensing applications: A review

OD Incel, SÖ Bursa - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
Although running deep-learning (DL) algorithms is challenging due to resource constraints
on mobile and wearable devices, they provide performance improvements compared to …