TY - GEN
T1 - MPC for Online Power Control in Energy Harvesting Sensor Networks
AU - Al-Tous, Hanan
AU - Barhumi, Imad
N1 - Funding Information:
This research work has been supported by UAE University-UPAR grant number 31N202.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/20
Y1 - 2018/7/20
N2 - In this paper, model-predictive-control (MPC) framework is proposed for an online power control and data scheduling of energy-harvesting (EH) multi-hop wireless-sensor-networks (WSNs). The network consists of M sensor nodes transmitting their sensed information to a sink node through multi-hop transmission. Each sensor node has a battery of limited capacity to save the harvested energy and a buffer of limited size to store both the sensed and relayed data. The dynamic resource allocation problem is formulated using the MPC framework aiming to efficiently utilize the available harvested energy and transmit the data of all sensor nodes. The solution of the proposed MPC framework is compared with an offline solution, which is obtained assuming prior knowledge of the sensed data and harvested energy. Simulation results demonstrate the merits of the proposed approach.
AB - In this paper, model-predictive-control (MPC) framework is proposed for an online power control and data scheduling of energy-harvesting (EH) multi-hop wireless-sensor-networks (WSNs). The network consists of M sensor nodes transmitting their sensed information to a sink node through multi-hop transmission. Each sensor node has a battery of limited capacity to save the harvested energy and a buffer of limited size to store both the sensed and relayed data. The dynamic resource allocation problem is formulated using the MPC framework aiming to efficiently utilize the available harvested energy and transmit the data of all sensor nodes. The solution of the proposed MPC framework is compared with an offline solution, which is obtained assuming prior knowledge of the sensed data and harvested energy. Simulation results demonstrate the merits of the proposed approach.
KW - Wireless sensor network
KW - energy harvesting
KW - model predictive control
KW - offline control
UR - http://www.scopus.com/inward/record.url?scp=85050974403&partnerID=8YFLogxK
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U2 - 10.1109/VTCSpring.2018.8417663
DO - 10.1109/VTCSpring.2018.8417663
M3 - Conference contribution
AN - SCOPUS:85050974403
T3 - IEEE Vehicular Technology Conference
SP - 1
EP - 5
BT - 2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 87th IEEE Vehicular Technology Conference, VTC Spring 2018
Y2 - 3 June 2018 through 6 June 2018
ER -