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Articles

Dynamic adaptation of contention window boundaries using deep Q networks in UAV swarms

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Pages 167-174 | Received 10 Aug 2023, Accepted 08 Dec 2023, Published online: 21 Dec 2023

References

  • Macrorie R, Marvin S, While A. Robotics and automation in the city: a research agenda. Urban Geogr. 2021;42(2):197–217. doi: 10.1080/02723638.2019.1698868
  • Hentati AI, Fourati LC, Elgharbi E, et al. Simulation tools, environments and frameworks for UAVs and multi-UAV-based systems performance analysis (version 2.0). Int J Model Simul. 2023;43(4):474–490. doi: 10.1080/02286203.2022.2092257
  • Khan T, Ahmad N, Cao Y, et al. Certificate revocation in vehicular ad hoc networks techniques and protocols: a survey. Sci China Inf Sci. 2017;60:1–18. doi: 10.1007/s11432-017-9203-x
  • Yuan Z, Jin J, Sun L, et al. Ultra-reliable IoT communications with UAVs: a swarm use case. IEEE Commun Mag. 2018;56(12):90–96. doi: 10.1109/MCOM.2018.1800161
  • Arafat MY, Poudel S, Moh S. Medium access control protocols for flying ad hoc networks: a review. IEEE Sens J. 2020;21(4):4097–4121. doi: 10.1109/JSEN.7361
  • Cerquitelli T, Meo M, Curado M, et al. Machine learning empowered computer networks. Comput Netw. 2023; 230;109807. doi: 10.1016/j.comnet.2023.109807
  • Eiza MH, Cao Y, Xu L, editors. Toward sustainable and economic smart mobility: shaping the future of smart cities. World Scientific; 2020. Electronic reproduction. Singapore, ISBN 9781786347862.
  • Li B, Guo X, Zhang R, et al. Performance analysis and optimization for the MAC protocol in UAV-based IoT network. IEEE Trans Veh Technol. 2020;69(8):8925–8937. doi: 10.1109/TVT.25
  • Asaf K, Khan B, Kim GY. Wireless lan performance enhancement using double deep Q-Networks. Appl Sci. 2022;12(9):4145. doi: 10.3390/app12094145
  • Chitrashekharaiah Y, Srinidhi NN, Chouhan D, et al. Efficient Lifetime and Network Performance Improvement for Mobility of Nodes in IoT. In International Conference on Innovative Computing and Communications: Proceedings of ICICC 2021. Vol. 3, Springer Singapore; p. 421–430.
  • Abyaneh AHY, Hirzallah M, Krunz M. Intelligent-CW: AI-based framework for controlling contention window in WLANs. In: 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN); IEEE; 2019 Nov. p. 1–10.
  • Ali R, Zikria YB, Kim BS. Deep Reinforcement Learning Paradigm for Dense Wireless Networks in Smart Cities. In: Al-Turjman F, editor. Smart Cities Performability, Cognition, & Security. EAI/Springer Innovations in Communication and Computing. Springer, Cham. doi:10.1007/978-3-030-14718-1_3
  • Surjeet. 2022. “Optimal capacity utilization in mobile ad hoc network with adaptive Contention Window management scheme,” Research Square. doi:10.21203/rs.3.rs-1947426/v1
  • Wu Q, Shi S, Wan Z, et al. Towards V2I age-aware fairness access: a DQN based intelligent vehicular node training and test method. preprint, 2022. arXiv:2208.01283.
  • Lu S, Hu S, Duan Y, et al. Contention window adaptive adjustment strategy for fairness in multi-rate IEEE 802.11 network. In: 2022 6th International Conference on Communication and Information Systems (ICCIS); IEEE; 2022 Oct. p. 82–86.
  • Chen C, Li J, Balasubramaniam V, et al. Contention resolution in Wi-Fi 6-enabled internet of things based on deep learning. IEEE Internet Things J. 2020;8(7):5309–5320. doi: 10.1109/JIOT.2020.3037774
  • Zhang Q, Xue Y, Han Z, et al. Design and performance analysis of 3-D markov-chain-model-based fair spectrum-sharing access for IoT services. IEEE Internet Things J. 2022;9(17):15756–15770. doi: 10.1109/JIOT.2022.3150181
  • Ke CH, Astuti L. Applying deep reinforcement learning to improve throughput and reduce collision rate in IEEE 802.11 networks. KSII Trans Internet Inf Syst (TIIS). 2022;16(1):334–349.
  • Sheila de Cássia SJ, Ouameur MA, de Figueiredo FAP. Reinforcement learning-based Wi-Fi contention window optimization. J Commun Inf Syst. 2023;38:128–143.
  • Srivastava A, Prakash J. Future FANET with application and enabling techniques: anatomization and sustainability issues. Comput Sci Rev. 2021;39:100359. doi: 10.1016/j.cosrev.2020.100359
  • Aboueleneen N, Alwarafy A, Abdallah M. Deep reinforcement learning for internet of drones networks: issues and research directions. IEEE Open J Commun Soc. 2023;4:671–683. doi:10.1109/OJCOMS.2023.3251855

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