TY - GEN
T1 - Energy-Aware Edge-Cloud Computation Offloading for Smart Connected Health
AU - Materwala, Huned
AU - Ismail, Leila
N1 - Funding Information:
ACKNOWLEDGMENT This research was funded by the National Water and Energy Center of the United Arab Emirates University (Grant 31R215).
Publisher Copyright:
© 2021 IEEE.
PY - 2021/8
Y1 - 2021/8
N2 - Smart connected healthcare is an emerging technology in the context of smart cities. The connected network aims to provide efficient and effective remote patient care. In such scenarios, edge and clouds come into the picture to offload computation from sensors and mobile devices, monitoring patient's health conditions, having limited processing capabilities to the edge and/or the cloud. The problem of optimizing the energy consumption of the edge and cloud servers in this offloading scenario is crucial. However, existing research efforts focus on sensors or cloud energy optimization. They do not consider the edge and cloud as part of the offloading strategy. In this paper, we address this void by proposing a novel energy-aware offloading algorithm for smart connected healthcare, which optimizes the energy of the edge-cloud computing platform for compute-intensive applications. The experimental results show that our proposed algorithm is a promising approach to energy savings.
AB - Smart connected healthcare is an emerging technology in the context of smart cities. The connected network aims to provide efficient and effective remote patient care. In such scenarios, edge and clouds come into the picture to offload computation from sensors and mobile devices, monitoring patient's health conditions, having limited processing capabilities to the edge and/or the cloud. The problem of optimizing the energy consumption of the edge and cloud servers in this offloading scenario is crucial. However, existing research efforts focus on sensors or cloud energy optimization. They do not consider the edge and cloud as part of the offloading strategy. In this paper, we address this void by proposing a novel energy-aware offloading algorithm for smart connected healthcare, which optimizes the energy of the edge-cloud computing platform for compute-intensive applications. The experimental results show that our proposed algorithm is a promising approach to energy savings.
KW - Cloud computing
KW - Computation offloading
KW - Edge computing
KW - Energy-efficiency
KW - Queuing theory
KW - Smart connected healthcare
UR - http://www.scopus.com/inward/record.url?scp=85119658120&partnerID=8YFLogxK
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U2 - 10.1109/FiCloud49777.2021.00028
DO - 10.1109/FiCloud49777.2021.00028
M3 - Conference contribution
AN - SCOPUS:85119658120
T3 - Proceedings - 2021 International Conference on Future Internet of Things and Cloud, FiCloud 2021
SP - 144
EP - 150
BT - Proceedings - 2021 International Conference on Future Internet of Things and Cloud, FiCloud 2021
A2 - Younas, Muhammad
A2 - Awan, Irfan
A2 - Unal, Perin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Future Internet of Things and Cloud, FiCloud 2021
Y2 - 23 August 2021 through 25 August 2021
ER -
