TY - GEN
T1 - ONSRA
T2 - 2021 IEEE International Conference on Communications, ICC 2021
AU - Abdellatif, Alaa Awad
AU - Allahham, Mhd Saria
AU - Mohamed, Amr
AU - Erbad, Aiman
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - The rapid production of mobile and wearable devices along with the wireless applications boom is continuing to evolve everyday. This motivates network operators to integrate and exploit wireless spectrum across multiple radio access networks to cope with such intensive demand, while improving quality of service. However, it is crucial to develop innovative network selection techniques that consider heterogeneous networks characteristics, while meeting applications' quality requirements. Thus, this paper develops an optimal network selection with resource allocation scheme over heterogeneous networks that aims to optimize the latency, cost, and energy consumption, while accounting for data compression at the edge. Indeed, our framework could significantly enhance the performance of wireless healthcare systems by enabling data transfer from patients edge nodes to the cloud in cost-effective and energy-efficient manner, while maintaining strict Quality of Service (QoS) requirements of health applications. Our simulation results depict that our solution significantly outperforms state-of- the-art techniques in terms of energy consumption, latency, and cost.
AB - The rapid production of mobile and wearable devices along with the wireless applications boom is continuing to evolve everyday. This motivates network operators to integrate and exploit wireless spectrum across multiple radio access networks to cope with such intensive demand, while improving quality of service. However, it is crucial to develop innovative network selection techniques that consider heterogeneous networks characteristics, while meeting applications' quality requirements. Thus, this paper develops an optimal network selection with resource allocation scheme over heterogeneous networks that aims to optimize the latency, cost, and energy consumption, while accounting for data compression at the edge. Indeed, our framework could significantly enhance the performance of wireless healthcare systems by enabling data transfer from patients edge nodes to the cloud in cost-effective and energy-efficient manner, while maintaining strict Quality of Service (QoS) requirements of health applications. Our simulation results depict that our solution significantly outperforms state-of- the-art techniques in terms of energy consumption, latency, and cost.
KW - Heterogeneous networks
KW - edge computing
KW - internet of mobile things
KW - multi-RAT architecture
KW - wireless healthcare systems
UR - http://www.scopus.com/inward/record.url?scp=85115668771&partnerID=8YFLogxK
U2 - 10.1109/ICC42927.2021.9500548
DO - 10.1109/ICC42927.2021.9500548
M3 - Conference contribution
AN - SCOPUS:85115668771
T3 - IEEE International Conference on Communications
BT - ICC 2021 - IEEE International Conference on Communications, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 June 2021 through 23 June 2021
ER -