A Multi-Agent Reinforcement Learning-Based Optimized Routing for QoS in IoT

1. Boutaba, R., M. A. Salahuddin, N. Limam et al. A Comprehensive Survey on Machine Learning for Networking: Evolution, Applications and Research Opportunities. – J Internet Serv. Appl., Vol. 9, 2018, No 16. https://doi.org/10.1186/s13174-018-0087-210.1186/s13174-018-0087-2 Search in Google Scholar

2. Liang, X., I. Balasingham, S.-S. Byun. A Multi-Agent Reinforcement Learning Based Routing Protocol for Wireless Sensor Networks. – In: Proc. of 2008 IEEE International Symposium on Wireless Communication Systems, Reykjavik, 2008, pp. 552-557. DOI: 10.1109/ISWCS.2008.4726117.10.1109/ISWCS.2008.4726117 Search in Google Scholar

3. Sarasvathi, V., N. Ch. S. N. Iyengar, S. Saha. An Efficient Interference Aware Partially Overlapping Channel Assignment and Routing in Wireless Mesh Networks. – International Journal of Communication Networks and Information Security (IJCNIS), March 2014. Search in Google Scholar

4. Sarasvathi, V., N Ch. S. N. Iyengar. Centralized Rank-Based Channel Assignment for Multi-Radio Multi-Channel Wireless Mesh Networks. – Procedia Technology, Elsevier, Vol. 4, January 2012, pp. 182-186.10.1016/j.protcy.2012.05.027 Search in Google Scholar

5. Sarasvathi, V., N. Ch. S. N. Iyengar, S. Saha. QoS Guaranteed Intelligent Routing Using Hybrid PSO-GA in Wireless Mesh Networks. – Cybernetics and Information Technologies, Vol. 15, 2015, No 1, pp. 69-83.10.1515/cait-2015-0007 Search in Google Scholar

6. Sarasvathi, V., S. Saha, N. Ch. S. N. Iyengar, M. Koti. Coefficient of Restitution Based Cross Layer Interference Aware Routing Protocol in Wireless Mesh Networks. – International Journal of Communication Networks and Information Security (IJCNIS), Vol. 7, Novemer 2018, Issue 3. Search in Google Scholar

7. JerminJeaunita, T. C., V. Sarasvathi. Fault Tolerant Sensor Node Placement for IoT Based Large Scale Automated Greenhouse System. – International Journal of Computing and Digital Systems, UoB, Vol. 8, 2019, Issue 2. http://dx.doi.org/10.12785/ijcds/08021010.12785/ijcds/080210 Search in Google Scholar

8. RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks. https://tools.ietf.org/html/rfc6550. Search in Google Scholar

9. Thubert, P. Objective Function Zero for RPL. – RFC 6552, Vol. 33, 2012, pp. 3-8. Search in Google Scholar

10. Gnawali, O., P. Levis. The Minimum Rank with Hysteresis Objective Function. – RFC 6719 (Proposed Standard), Internet Engineering Task Force, September 2012. http://www.ietf.org/rfc/rfc6719.txt10.17487/rfc6719 Search in Google Scholar

11. Safaei, B., A. A. M. Salehi, A. M. H. Monazzah, A. Ejlali. Effects of RPL Objective Functions on the Primitive Characteristics of Mobile and Static IoT Infrastructures. – Microprocessors and Microsystems, Vol. 69, 2019, pp. 79-91. ISSN 0141-9331. https://doi.org/10.1016/j.micpro.2019.05.010.10.1016/j.micpro.2019.05.010 Search in Google Scholar

12. Ghaleb, B., A. Al-Dubai, E. Ekonomou, I. Wadhaj. A New Enhanced RPL Based Routing for Internet of Things. – In: Proc. of 2017 IEEE International Conference on Communications Workshops (ICC Workshops), 2017, pp. 595-600. DOI: 10.1109/ICCW.2017.7962723.10.1109/ICCW.2017.7962723 Search in Google Scholar

13. Mateo Sanguino, T. J., E. Navarro Lozano, M. Sánchez Alcántara. Intelligent Agent-Based Assessment of a Resilient Multi-Hop Routing Protocol for Dynamic WSN. – Wireless Pers. Commun., 2020. https://doi.org/10.1007/s11277-020-07136-110.1007/s11277-020-07136-1 Search in Google Scholar

14. Rocha, V., A. A. F. Brandao. A Scalable Multi-Agent Architecture for Monitoring IoT Devices. – Elsevier, Journal of Network and Computer Applications, 139, 2019, pp. 1-14. https://doi.org/10.1016/j.jnca.2019.04.01710.1016/j.jnca.2019.04.017 Search in Google Scholar

15. Mittal, M., S. Srinivasan, M. Rani, O. P. Vyas. Type-2 Fuzzy Ontology-Based Multi-Agents’ System for Wireless Sensor Network. – In: Proc. of IEEE Region 10 Conference (TENCON’17), Penang, 2017, pp. 2864-2869. DOI: 10.1109/TENCON.2017.8228350.10.1109/TENCON.2017.8228350 Search in Google Scholar

16. Liang, X., I. Balasingham, S.-S. Byun. A Multi-Agent Reinforcement Learning Based Routing Protocol for Wireless Sensor Networks. – In: Proc. of 2008 IEEE International Symposium on Wireless Communication Systems, Reykjavik, 2008, pp. 552-557. DOI: 10.1109/ISWCS.2008.4726117.10.1109/ISWCS.2008.4726117 Search in Google Scholar

17. Rudek, R., L. Koszalka, I. Pozniak-Koszalka. Introduction to Multi-Agent Modified Q-Learning Routing for Computer Networks. – In: Proc. of Advanced Industrial Conference on Telecommunications/Service Assurance with Partial and Intermittent Resources Conference/e-Learning on Telecommunications Workshop (AICT/SAPIR/ELETE’05), Lisbon, Portugal, 2005, pp. 408-413. DOI: 10.1109/AICT.2005.53.10.1109/AICT.2005.53 Search in Google Scholar

18. Busoniu, L., R. Babuška, B. de Schutter. Multi-Agent Reinforcement Learning: An Overview. – In: D. Srinivasan, L. C. Jain, Eds. Innovations in Multi-Agent Systems and Applications – 1. Studies in Computational Intelligence. Vol. 310. Berlin, Heidelberg, Springer, 2010, pp. 183-221. https://doi.org/10.1007/978-3-642-14435-6_710.1007/978-3-642-14435-6_7 Search in Google Scholar

19. Liu, M., S. Xu, S. Sun. An Agent-Assisted QoS-Based Routing Algorithm for Wireless Sensor Networks. – Journal of Network and Computer Applications, Elsevier, January 2012. https://doi.org/10.1016/j.jnca.2011.03.03110.1016/j.jnca.2011.03.031 Search in Google Scholar

20. Bendjima, M., M. Feham. Multi-Agent System for a Reliable Routing in WSN. – In: Proc. of 2015 Science and Information Conference (SAI’15), London, 2015, pp. 1412-1419. DOI: 10.1109/SAI.2015.7237331.10.1109/SAI.2015.7237331 Search in Google Scholar

21. Silva, M. A. L., S. R. de Souza, M. J. F. Souza, A. L. C. Bazzan. A Reinforcement Learning-Based Multi-Agent Framework Applied for Solving Routing and Scheduling Problems. – Elsevier, Expert Systems with Applications, Vol. 131, 2019, pp. 148-171. https://doi.org/10.1016/j.eswa.2019.04.05610.1016/j.eswa.2019.04.056 Search in Google Scholar

22. Belagali, R., A. M. Anusha, P. Sangulagi. Energy-Efficient Secure Routing and Aggregation in Military Sensor Network Using Multi-Agent Approach. – In: Proc. of International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Davangere, 2015, pp. 286-292. DOI: 10.1109/ICATCCT.2015.7456897.10.1109/ICATCCT.2015.7456897 Search in Google Scholar

23. Sutagundar, A. V., S. S. Manvi. Fish Bone Structure-Based Data Aggregation and Routing in Wireless Sensor Network: Multi-Agent Based Approach. – Telecommun. Syst., Vol. 56, 2014, pp. 493-508. https://doi.org/10.1007/s11235-013-9769-z10.1007/s11235-013-9769-z Search in Google Scholar

24. Mammeri, Z. Reinforcement Learning Based Routing in Networks: Review and Classification of Approaches. – IEEE Access, Vol. 7, 2019, pp. 55916-55950. DOI: 10.1109/ACCESS.2019.2913776.10.1109/ACCESS.2019.2913776 Search in Google Scholar

25. You, X., X. Li, Y. Xu, H. Feng, J. Zhao, H. Yan. Toward Packet Routing with Fully-Distributed Multi-Agent Deep Reinforcement Learning. – Journal of LATEX Class Files, Vol. 14, August 2015, No 8, arXiv:1905.03494v2. Search in Google Scholar

26. Wang, F., R. Feng, H. Chen. Dynamic Routing Algorithm with Q-Learning for Internet of Things with Delayed Estimator. – In: IOP Conf. Series: Earth and Environmental Science. Vol. 234. 2019. DOI:10.1088/1755-1315/234/1/012048.10.1088/1755-1315/234/1/012048 Search in Google Scholar

27. Singh, K., J. Kaur. Machine Learning Based Link Cost Estimation for Routing Optimization in Wireless Sensor Networks. – Advances in Wireless and Mobile Communications, Vol. 10, 2017, No 1, pp. 39-49. ISSN 0973-6972. Search in Google Scholar