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AuthorChkirbene Z.
AuthorAbdellatif A.A.
AuthorMohamed A.
AuthorErbad A.
AuthorGuizani M.
Available date2022-04-21T08:58:20Z
Publication Date2022
Publication NameIEEE Transactions on Network Science and Engineering
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/TNSE.2021.3058037
URIhttp://hdl.handle.net/10576/30049
AbstractSmart 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, reliability, latency, and limited networks resources are among the many challenges hindering the realization of smart health systems. Thus, in this paper, we leverage the dense heterogeneous network (HetNet) architecture over 5 G network to enhance network capacity and provide seamless connectivity for smart health systems. However, network selection in HetNets is still a challenging problem that needs to be addressed. Inspired by the success of Deep Reinforcement Learning (DRL) in solving complicated control problems, we present a novel DRL model for solving the network selection problem with the aim of optimizing medical data delivery over heterogeneous health systems. Specifically, we formulate an optimization model that integrates the network selection problem with adaptive compression, at the network edge, to minimize the transmission energy consumption and latency, while meeting diverse applications' Quality of service (QoS) requirements. Our experimental results show that the proposed DRL-based model could minimize the energy consumption and latency compared to the greedy techniques, while meeting different users' demands in high dynamics environments. 2013 IEEE.
Languageen
PublisherIEEE Computer Society
SubjectDeep learning
Energy utilization
Heterogeneous networks
Learning systems
Medical information systems
Medical problems
Network architecture
Quality of service
Reinforcement learning
Diverse applications
Energy consumption and cost
Heterogeneous Network (HetNet)
Integrating information
Optimization modeling
Qualityof-service requirement (QoS)
Seamless connectivity
Transmission energy consumption
5G mobile communication systems
TitleDeep Reinforcement Learning for Network Selection over Heterogeneous Health Systems
TypeArticle
Pagination258-270
Issue Number1
Volume Number9
dc.accessType Abstract Only


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