1 |
W. Zhang, Y. Liu, G. Han, Y. Feng, and Y. Zhao, "An energy efficient and QoS aware routing algorithm based on data classification for industrial wireless sensor networks," IEEE Access, vol. 6, pp. 46495-46504, 2018.
DOI
|
2 |
I.Y. Kim, and O.L. De Weck, "Adaptive weighted sum method for multiobjective optimization: a new method for Pareto front generation," Structural and Multidisciplinary Optimization, vol. 31, no. 2, pp. 105-116, 2006.
DOI
|
3 |
D. Jiang, P. Zhang, L. Zhihan, and H. Song, "Energy-efficient multi-constraint routing algorithm with load balancing for smart city applications," IEEE Internet of Things Journal, vol. 3, no. 6, pp. 1437-1447, 2016.
DOI
|
4 |
J.Y. Ji, W.J. Yu, Y.J. Gong, and J. Zhang, "Multiobjective optimization with ϵ-constrained method for solving real-parameter constrained optimization problems," Information Sciences, vol. 467, pp. 15-34, 2018.
DOI
|
5 |
C. Liu, X. Xu, and D. Hu. "Multiobjective reinforcement learning: A comprehensive overview," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 3, pp. 385-398, 2014.
DOI
|
6 |
J. A. Boyan and M. L. Littman, "Packet routing in dynamically changing networks: A reinforcement learning approach," Advances in Neural Information Processing Systems, pp. 671-678, 1994.
|
7 |
P. Gawlowicz and A. Zubow, "Ns-3 meets openai gym: The playground for machine learning in networking research," In Proceeding of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp. 113-120, 2019.
|
8 |
H. Mostafaei, "Energy-efficient algorithm for reliable routing of wireless sensor networks," IEEE Transactions on Industrial Electronics, vol. 66, no. 7, pp. 5567-5575, 2018.
DOI
|
9 |
J.G. Castro, L.S. Dario, and M.P. Jose, "Dynamic lexicographic approach for heuristic multi-objective optimization," In Proceedings of the Workshop on Intelligent Metaheuristics for Logistic Planning, Seville-Spain, pp. 153-163, 2009.
|
10 |
Z. Fei, B. Li, Y. Shaoshi, C. Xing, H. Chen, and L. Hanzo, "A survey of multi-objective optimization in wireless sensor networks: Metrics, algorithms, and open problems," IEEE Communications Surveys & Tutorials, vol. 19, no. 1, pp. 550-586, 2017.
DOI
|
11 |
M. Alloghani, D, Al-Jumeily, J. Mustafina, A. Hussain, and A.J. Aljaaf, "A systematic review on supervised and unsupervised machine learning algorithms for data science," Supervised and Unsupervised Learning for Data Science, pp. 3-21, 2019.
|
12 |
C. J. Watkins and D. Peter, "Q-learning," Machine Learning, vol. 8, no. 3, pp. 279-292, 1992.
DOI
|
13 |
W. Guo, C. Yan, and T. Lu. "Optimizing the lifetime of wireless sensor networks via reinforcement-learning-based routing," International Journal of Distributed Sensor Networks, vol. 15, no. 2, 2019.
|
14 |
Z. Liu and I. Elhanany, "RL-MAC: A QoS-aware reinforcement learning based MAC protocol for wireless sensor networks," in Proceeding of the 2006 IEEE International Conference on Networking, Sensing and Control, pp. 768-773, 2006.
|
15 |
A. Bildea, "Link quality in wireless sensor networks," Ph.D. dissertation, Universite de Grenoble, 2013.
|
16 |
D. Kandris, C. Nakas, D. Vomvas, and G. Koulouras, "Applications of wireless sensor networks: an up-to-date survey," Applied System Innovation, vol. 3, no. 1, pp. 14-38, 2020.
DOI
|
17 |
M. Ndiaye, G. P. Hancke, and A. M. Abu-Mahfouz, "Software defined networking for improved wireless sensor network manage- ment: A survey," Sensors, vol. 17, no. 5 pp. 1031-1063, 2017.
DOI
|
18 |
Z. Mammeri, "Reinforcement learning based routing in networks: Review and classification of approaches," IEEE Access, vol. 7, pp. 55916-55950, 2019.
DOI
|
19 |
X. Tang, X. Wang, R. Cattley, F. Gu, and A.D. Ball, "Energy harvesting technologies for achieving self-powered wireless sensor networks in machine condition monitoring: a review," Sensors, vol. 18, no. 12, pp. 4113-4152 ,2018.
DOI
|
20 |
R. Elhabyan, W. Shi, and M. Hilaire, "Coverage protocols for wireless sensor networks: review and future directions," Journal of Communication and Networks, vol. 21, no. 1, pp. 45-60, 2019.
DOI
|