TY - GEN
T1 - LTEOC
T2 - 11th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2020
AU - Batta, Mohamed Sofiane
AU - Aliouat, Zibouda
AU - Mabed, Hakim
AU - Harous, Saad
N1 - Funding Information:
ACKNOWLEDGMENT This research work is supported by UAEU/UPAR Grant 31T102-UPAR -1-2017.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/28
Y1 - 2020/10/28
N2 - Mobile traffic is expected to grow by 30% by 2024, saving energy is a crucial necessity even for battery-powered devices such as smartphones for the sake of green computing. Clustering techniques were introduced to conserve the energy of network devices. However, proposed energy optimization techniques do not yield optimal battery life, they mostly consider devices with non-rechargeable batteries and deal with limited energy without considering battery aging (short-term vision). To this end, we focus on the long-term energy optimization and we introduce a dynamic clustering technique that take into consideration the state of health of devices batteries and their degradation level. The proposed scheme efficiently manages the energy resource to enhance the battery behavior which extends the network lifespan in the long term.Simulations results show that the proposed approach out-performs similar works available in the current literature. The batteries life cycle and the network lifetime are improved by 38% and 47% respectively. The average number of generated clusters is reduced by 39%.
AB - Mobile traffic is expected to grow by 30% by 2024, saving energy is a crucial necessity even for battery-powered devices such as smartphones for the sake of green computing. Clustering techniques were introduced to conserve the energy of network devices. However, proposed energy optimization techniques do not yield optimal battery life, they mostly consider devices with non-rechargeable batteries and deal with limited energy without considering battery aging (short-term vision). To this end, we focus on the long-term energy optimization and we introduce a dynamic clustering technique that take into consideration the state of health of devices batteries and their degradation level. The proposed scheme efficiently manages the energy resource to enhance the battery behavior which extends the network lifespan in the long term.Simulations results show that the proposed approach out-performs similar works available in the current literature. The batteries life cycle and the network lifetime are improved by 38% and 47% respectively. The average number of generated clusters is reduced by 39%.
KW - Energy-aware Protocols
KW - IoT
KW - Multi-hop Clustering
KW - Network Connectivity
KW - Rechargeable Battery Lifetime
KW - WSN
UR - http://www.scopus.com/inward/record.url?scp=85099769597&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099769597&partnerID=8YFLogxK
U2 - 10.1109/UEMCON51285.2020.9298030
DO - 10.1109/UEMCON51285.2020.9298030
M3 - Conference contribution
AN - SCOPUS:85099769597
T3 - 2020 11th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2020
SP - 8
EP - 14
BT - 2020 11th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2020
A2 - Paul, Rajashree
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 October 2020 through 31 October 2020
ER -