TY - GEN
T1 - Fast and low-complexity reinforcement learning for delay-sensitive energy harvesting wireless visual sensing systems
AU - Toorchi, Niloofar
AU - Chakareski, Jacob
AU - Mastronarde, Nicholas
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/3
Y1 - 2016/8/3
N2 - In this paper, we consider an energy challenged remote sensor transmitting latency-sensitive imagery data over a time-varying channel. The sensor harvests energy from the environment and hence efficient energy consumption is of great importance. In this paper, we aim to find the optimal transmission scheduling and power management policies that maximize the available energy for future transmissions while meeting a queuing delay constraint. We formulate this problem as a Markov Decision Process (MDP) and propose a reinforcement learning (RL) algorithm to solve it online. Our experiments show that the proposed algorithm achieves comparable performance to a state-of-the-art RL algorithm, but at much lower complexity.
AB - In this paper, we consider an energy challenged remote sensor transmitting latency-sensitive imagery data over a time-varying channel. The sensor harvests energy from the environment and hence efficient energy consumption is of great importance. In this paper, we aim to find the optimal transmission scheduling and power management policies that maximize the available energy for future transmissions while meeting a queuing delay constraint. We formulate this problem as a Markov Decision Process (MDP) and propose a reinforcement learning (RL) algorithm to solve it online. Our experiments show that the proposed algorithm achieves comparable performance to a state-of-the-art RL algorithm, but at much lower complexity.
KW - Energy harvesting
KW - Latency-sensitive remote sensing
KW - Markov decision process
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85006713307&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006713307&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2016.7532669
DO - 10.1109/ICIP.2016.7532669
M3 - Conference contribution
AN - SCOPUS:85006713307
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1804
EP - 1808
BT - 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PB - IEEE Computer Society
T2 - 23rd IEEE International Conference on Image Processing, ICIP 2016
Y2 - 25 September 2016 through 28 September 2016
ER -