TY - GEN
T1 - Distributed reinforcement learning algorithm for 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.
Funding Information:
has been supported by UAE University-UPAR grant
Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - In this paper, a distributed reinforcement-learning (RL) algorithm is proposed for power control and data scheduling in energy-harvesting (EH) multi-hop wireless-sensor-networks (WSNs). The WSN consists of M EH sensor nodes aiming to transmit their data to a sink node with minimum delay. 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 from neighboring nodes. A state-action-reward-state-action (SARSA) based distributed algorithm is proposed. The proposed distributed-SARSA (D-SARSA) algorithm adaptively changes the transmitted data and power control at each sensor node according to the state information such that the data of all sensor nodes are received at the sink node with minimum delay. Simulation results demonstrate the merits of the proposed algorithm.
AB - In this paper, a distributed reinforcement-learning (RL) algorithm is proposed for power control and data scheduling in energy-harvesting (EH) multi-hop wireless-sensor-networks (WSNs). The WSN consists of M EH sensor nodes aiming to transmit their data to a sink node with minimum delay. 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 from neighboring nodes. A state-action-reward-state-action (SARSA) based distributed algorithm is proposed. The proposed distributed-SARSA (D-SARSA) algorithm adaptively changes the transmitted data and power control at each sensor node according to the state information such that the data of all sensor nodes are received at the sink node with minimum delay. Simulation results demonstrate the merits of the proposed algorithm.
KW - SARSA
KW - Wireless sensor network
KW - energy harvesting
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85072334485&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072334485&partnerID=8YFLogxK
U2 - 10.1109/BlackSeaCom.2019.8812862
DO - 10.1109/BlackSeaCom.2019.8812862
M3 - Conference contribution
AN - SCOPUS:85072334485
T3 - 2019 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2019
BT - 2019 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2019
Y2 - 3 June 2019 through 6 June 2019
ER -