Fetching the paper…
Reading the bibliography…
Reinforcement learning (RL) is capable of managing wireless, energy-harvesting IoT nodes by solving the problem of autonomous management in non-stationary, resource-constrained settings.
A. Ng, D. Harada, and S. Russell, “Policy invariance under reward transformations: Theory and application to reward shaping,” in ICML , 1999, vol. 99, pp. 278 —-287
1999
Earlier work this paper cites.
A. Kansal, J. Hsu, S. Zahedi, and M. B. Srivastava, “Power management in energy harvesting sensor networks,” ACM Transactions on Embedded Computing Systems , vol. 6, no. 4, pp. 32–es, 2007
2007
Earlier work this paper cites.
R. Chaoming Hsu, C. T. Liu, and W. M. Lee, “Reinforcement learning-based dynamic power management for energy harvesting wireless sensor network,” in International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems , vol. 5579 LNAI. Springer, 2009
2009
Earlier work this paper cites.
R. C. Hsu, C.-T. Liu, K.-C. Wang, and W.-M. Lee, “Qos-aware power management for energy harvesting wireless sensor network utilizing reinforcement learning,” in 2009 International Conference on Computational Science and Engineering , vol. 2. IEEE, 2009, pp. 537–542
2009
Earlier work this paper cites.
C. T. Liu and R. C. Hsu, “Dynamic power management utilizing reinforcement learning with fuzzy reward for energy harvesting wireless sensor nodes,” IECON Proceedings (Industrial Electronics Conference) , pp. 2365–2369, 2011
2011
Earlier work this paper cites.
R. C. Hsu, C. T. Liu, and H. L. Wang, “A reinforcement learning-based ToD provisioning dynamic power management for sustainable operation of energy harvesting wireless sensor node,” IEEE Transactions on Emerging Topics in Computing , vol. 2, no. 2, pp. 181–191, 2014
2014
Earlier work this paper cites.
R. C. Hsu, T. H. Lin, S. M. Chen, and C. T. Liu, “Dynamic energy management of energy harvesting wireless sensor nodes using fuzzy inference system with reinforcement learning,” Proceeding - 2015 IEEE International Conference on Industrial Informatics, INDIN 2015 , pp. 116–120, 2015
2015
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al. , “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, p. 529, 2015
2015
Cited alongside, same era.
2016
Cited alongside, same era.
G. M. Dias, M. Nurchis, and B. Bellalta, “Adapting sampling interval of sensor networks using on-line reinforcement learning,” in 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT) . IEEE, 2016, pp. 460–465
2016
Cited alongside, same era.
S. Shresthamali, M. Kondo, and H. Nakamura, “Adaptive Power Management in Solar Energy Harvesting Sensor Node Using Reinforcement Learning,” vol. 16, no. 5s, pp. 1–21, 2017
2017
Cited alongside, same era.
F. A. Kraemer, D. Ammar, A. E. Braten, N. Tamkittikhun, and D. Palma, “Solar energy prediction for constrained IoT nodes based on public weather forecasts,” in the Seventh International Conference . New York, New York, USA: ACM Press, 2017, pp. 1–8
2017
Later among the works it cites.
F. Fraternali, B. Balaji, and R. Gupta, “Scaling configuration of energy harvesting sensors with reinforcement learning,” in Proceedings of the 6th International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems . ACM, 2018, pp. 7–13
2018
Later among the works it cites.
F. A. Aoudia, M. Gautier, and O. Berder, “RLMan: an Energy Manager Based on Reinforcement Learning for Energy Harvesting Wireless Sensor Networks,” IEEE Transactions on Green Communications and Networking , vol. 2, no. 2, pp. 1–1, 2018
2018
Later among the works it cites.
A. E. Braten and F. A. Kraemer, “Towards Cognitive IoT: Autonomous Prediction Model Selection for Solar-Powered Nodes,” in 2018 IEEE International Congress on Internet of Things (ICIOT) . Seattle, USA: IEEE, 2018, pp. 118–125
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
A. Ortiz, H. Al-Shatri, X. Li, T. Weber, and A. Klein, “Reinforcement Learning for Energy Harvesting Decode-and-Forward Two-Hop Communications,” IEEE Transactions on Green Communications and Networking , vol. 1, no. 3, pp. 309–319, 2017
2017
Cited alongside, same era.
2018
Later among the works it cites.
Y. Rioual, Y. L. Moullec, J. Laurent, M. I. Khan, and J.-p. Diguet, “Reward Function Evaluation in a Reinforcement Learning Approach for Energy Management,” in 2018 16th Biennial Baltic Electronics Conference (BEC) . IEEE, 2018, pp. 1–4
2018
Later among the works it cites.
“uTensor project,” https://github.com/utensor, accessed: 2019-02-28
2019
Closest in time.