Deep learning in mobile and wireless networking: A survey
The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …
services pose unprecedented demands on mobile and wireless networking infrastructure …
Machine learning/artificial intelligence for sensor data fusion–opportunities and challenges
During Fusion 2019 Conference (https://www. fusion2019. org/program. html), leading
experts presented ideas on the historical, contemporary, and future coordination of artificial …
experts presented ideas on the historical, contemporary, and future coordination of artificial …
Coedge: Cooperative dnn inference with adaptive workload partitioning over heterogeneous edge devices
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 …
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
With recent advances, neural networks have become a crucial building block in intelligent
IoT systems and sensing applications. However, the excessive computational demand …
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
Deep neural network (DNN) models typically have many hyperparameters that can be
configured to achieve optimal performance on a particular dataset. Practitioners usually tune …
configured to achieve optimal performance on a particular dataset. Practitioners usually tune …
A survey on deep neural network compression: Challenges, overview, and solutions
Deep Neural Network (DNN) has gained unprecedented performance due to its automated
feature extraction capability. This high order performance leads to significant incorporation …
feature extraction capability. This high order performance leads to significant incorporation …
Deep-learning-based pedestrian inertial navigation: Methods, data set, and on-device inference
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 …
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
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 …
upcoming years to improve the assistance systems. The IoT devices utilize several sensor …
Transforming large-size to lightweight deep neural networks for IoT applications
Deep Neural Networks (DNNs) have gained unprecedented popularity due to their high-
order performance and automated feature extraction capability. This has encouraged …
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 …
on mobile and wearable devices, they provide performance improvements compared to …