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Now showing items 1-10 of 11
Compress or Interfere?
(
IEEE Computer Society
, 2019 , Conference Paper)
Rapid evolution of wireless medical devices and network technologies has fostered the growth of remote monitoring systems. Such new technologies enable monitoring patients' medical records anytime and anywhere without ...
A Deep Learning Approach for Vital Signs Compression and Energy Efficient Delivery in mhealth Systems
(
Institute of Electrical and Electronics Engineers Inc.
, 2018 , Article)
© 2013 IEEE. Due to the increasing number of chronic disease patients, continuous health monitoring has become the top priority for health-care providers and has posed a major stimulus for the development of scalable and ...
Multi-Agent Reinforcement Learning for Network Selection and Resource Allocation in Heterogeneous Multi-RAT Networks
(
Institute of Electrical and Electronics Engineers Inc.
, 2022 , Article)
The rapid production of mobile devices along with the wireless applications boom is continuing to evolve daily. This motivates the exploitation of wireless spectrum using multiple Radio Access Technologies (multi-RAT) and ...
Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence
(
Institute of Electrical and Electronics Engineers Inc.
, 2022 , Article)
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems and speech processing applications to robotics ...
Dynamic Network Slicing and Resource Allocation for 5G-and-Beyond Networks
(
Institute of Electrical and Electronics Engineers Inc.
, 2022 , Conference Paper)
5G networks are designed not only to transport data, but also to process them while supporting a vast number of services with different key Performance Indicators (KPIs). Network virtualization has emerged to enable this ...
Deep Reinforcement Learning for Network Selection over Heterogeneous Health Systems
(
IEEE Computer Society
, 2022 , Article)
Smart health systems improve our quality oflife by integrating diverse information and technologies into health and medical practices. Such technologies can significantly improve the existing health services. However, ...
Patient-Driven Network Selection in multi-RAT Health Systems Using Deep Reinforcement Learning
(
Institute of Electrical and Electronics Engineers Inc.
, 2021 , Conference Paper)
The recent pandemic along with the rapid increase in the number of patients that require continuous remote monitoring imposes several challenges to support the high quality of services (QoS) in remote health applications. ...
Distributed Multi-Objective Resource Optimization for Mobile-Health Systems
(
Hamad bin Khalifa University Press (HBKU Press)
, 2016 , Conference Paper)
Mobile-health (m-health) systems leverage wireless and mobile communication technologies to promote new ways to acquire, process, transport, and secure the raw and processed medical data. M-health systems provide the ...
RLENS: RL-based Energy-Efficient Network Selection Framework for IoMT
(
IEEE
, 2022 , Conference Paper)
With the emergence of smart health (s-health) applications and services, several requirements for quality have arisen to foresee and react instantaneously to emergency circumstances. Such requirements demand fast-acting ...
RL-Assisted Energy-Aware User-Edge Association for IoT-based Hierarchical Federated Learning
(2022 , Conference Paper)
The extremely heavy global reliance on IoT devices is causing enormous amounts of data to be gathered and shared in IoT networks. Such data need to efficiently be used in training and deploying of powerful artificially ...