Show simple item record

AuthorChkirbene, Zina
AuthorAbdellatif, Alaa Awad
AuthorMohamed, Amr
AuthorErbad, Aiman
AuthorGuizani, Mohsen
Available date2022-10-23T08:47:09Z
Publication Date2022-01-01
Publication NameIEEE Transactions on Network Science and Engineering
Identifierhttp://dx.doi.org/10.1109/TNSE.2021.3058037
CitationChkirbene, Z., Abdellatif, A. A., Mohamed, A., Erbad, A., & Guizani, M. (2021). Deep reinforcement learning for network selection over heterogeneous health systems. IEEE Transactions on Network Science and Engineering, 9(1), 258-270.‏
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85100859447&origin=inward
URIhttp://hdl.handle.net/10576/35283
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.
Languageen
PublisherIEEE Computer Society
SubjectAdaptive compression
Deep reinforcement learning
Heterogeneous networks
Remote monitoring
Smart health
TitleDeep Reinforcement Learning for Network Selection over Heterogeneous Health Systems
TypeArticle
Pagination258-270
Issue Number1
Volume Number9
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record