Deep learning based object detection for resource constrained devices: Systematic review, future trends and challenges ahead
Deep learning models are widely being employed for object detection due to their high
performance. However, the majority of applications that require object detection are …
performance. However, the majority of applications that require object detection are …
Advanced deep learning models for 6G: overview, opportunities and challenges
The advent of the sixth generation of mobile communications (6G) ushers in an era of
heightened demand for advanced network intelligence to tackle the challenges of an …
heightened demand for advanced network intelligence to tackle the challenges of an …
AI driven heterogeneous MEC system with UAV assistance for dynamic environment: Challenges and solutions
By taking full advantage of Computing, Communication and Caching (3C) resources at the
network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for …
network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for …
A systematic analysis of deep learning methods and potential attacks in internet-of-things surfaces
The usage of intelligent IoT devices is exponentially rising, and so the possibility of attacks in
the IoT surfaces. The deep leaning algorithms are competent for directing the sanctuary …
the IoT surfaces. The deep leaning algorithms are competent for directing the sanctuary …
Adaptive online decision method for initial congestion window in 5G mobile edge computing using deep reinforcement learning
Mobile edge computing provides users with low response time and avoids unnecessary
data transmission. Due to the deployment of 5G, the emerging edge systems can provide …
data transmission. Due to the deployment of 5G, the emerging edge systems can provide …
[HTML][HTML] New universal sustainability metrics to assess edge intelligence
N Lenherr, R Pawlitzek, B Michel - Sustainable Computing: Informatics and …, 2021 - Elsevier
The single recent focus on deep learning accuracy ignores economic, and environmental
cost. Progress towards Green AI is hindered by lack of universal metrics that equally reward …
cost. Progress towards Green AI is hindered by lack of universal metrics that equally reward …
Toward on-device ai and blockchain for 6g-enabled agricultural supply chain management
6G envisions artificial intelligence (AI) powered solutions for enhancing the quality of service
(QoS) in the network and to ensure optimal utilization of resources. In this work, we propose …
(QoS) in the network and to ensure optimal utilization of resources. In this work, we propose …
Software orchestrated and hardware accelerated artificial intelligence: toward low latency edge computing
Driven by the expeditious wireless evolution and growing complexity of Internet of Things
systems, edge intelligence has been widely recognized as a novel paradigm to enable …
systems, edge intelligence has been widely recognized as a novel paradigm to enable …
Hyper-noise interference privacy protection framework for intelligent medical data-centric networks
W Wu, H Zhang, VHC de Albuquerque, L Xu - IEEE Network, 2020 - ieeexplore.ieee.org
Convolutional neural network (CNN) has been widely used in the medical domain, but how
to protect privacy is still a challenge. In addition, traditional differential privacy protection …
to protect privacy is still a challenge. In addition, traditional differential privacy protection …
Evolving energy efficient convolutional neural networks
As deep neural networks have been deployed in more and more applications over the past
half decade and are finding their way into an ever increasing number of operational …
half decade and are finding their way into an ever increasing number of operational …