Machine learning in IoT security: Current solutions and future challenges
The future Internet of Things (IoT) will have a deep economical, commercial and social
impact on our lives. The participating nodes in IoT networks are usually resource …
impact on our lives. The participating nodes in IoT networks are usually resource …
Machine learning for resource management in cellular and IoT networks: Potentials, current solutions, and open challenges
Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart
devices connected to the Internet. In the wake of disruptive IoT with a huge amount and …
devices connected to the Internet. In the wake of disruptive IoT with a huge amount and …
A learning-based incentive mechanism for federated learning
Internet of Things (IoT) generates large amounts of data at the network edge. Machine
learning models are often built on these data, to enable the detection, classification, and …
learning models are often built on these data, to enable the detection, classification, and …
A tutorial on calibration measurements and calibration models for clinical prediction models
Our primary objective is to provide the clinical informatics community with an introductory
tutorial on calibration measurements and calibration models for predictive models using …
tutorial on calibration measurements and calibration models for predictive models using …
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 …
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 …
Machine learning meets communication networks: Current trends and future challenges
The growing network density and unprecedented increase in network traffic, caused by the
massively expanding number of connected devices and online services, require intelligent …
massively expanding number of connected devices and online services, require intelligent …
Machine learning-enabled internet of things (iot): Data, applications, and industry perspective
Machine learning (ML) allows the Internet of Things (IoT) to gain hidden insights from the
treasure trove of sensed data and be truly ubiquitous without explicitly looking for knowledge …
treasure trove of sensed data and be truly ubiquitous without explicitly looking for knowledge …
Machine learning and the Internet of Things security: Solutions and open challenges
Abstract Internet of Things (IoT) is a pervasively-used technology for the last few years. IoT
technologies are also responsible for intensifying various everyday smart applications …
technologies are also responsible for intensifying various everyday smart applications …
Fastdeepiot: Towards understanding and optimizing neural network execution time on mobile and embedded devices
Deep neural networks show great potential as solutions to many sensing application
problems, but their excessive resource demand slows down execution time, pausing a …
problems, but their excessive resource demand slows down execution time, pausing a …