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Reinforcement Learning-Based Multidimensional Perception and Energy Awareness Optimized Link State Routing for Flying Ad-Hoc Networks

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Abstract

One of the uncrewed aerial vehicles (UAV) in a Flying Ad-hoc network (FANET) can link directly to the infrastructure. At the same time, the other UAVs in the system may have a multi-hop connection in which each node works as a relay and a data collection node. We may not have the support of traditional infrastructure-based networks when natural disasters such as floods or earthquakes strike. This is fatal because trapped people are challenging to find by search and rescue personnel. In such cases, an airborne network of small drones is valuable for giving quick and adequate coverage of the affected area and instant insights to rescue workers. At the same time, such networks face various challenges, and ongoing research and development show promise in making such technology more dependable and effective. This paper presents Multidimensional Perception and Energy Awareness Optimized Link State Routing (MPEAOLSR) for Flying Ad-hoc networks, which is based on Reinforcement Learning (RL). The protocol is a mobile wireless LAN-specific version of the traditional link state algorithm. The protocol largely relies on the idea of multipoint relays (MPRs). During the flooding process, MPRs are chosen to forward broadcast messages. This technique considerably minimizes message overhead associated to a standard flooding system in which each node retransmits each message after receiving the first copy. In RL-MPEAOLSR, only nodes designated as MPRs generate link state information. Furthermore, the RL-MPEAOLSR node can opt to report just links between itself and its MPR selectors, decreasing the number of control messages flooding the network. Because MPRs function well in large and dense networks, the MPEAOLSR protocol is suited for them. The proposed approach outperforms the conventional Energy Awareness Optimised Link State Routing for Flying Ad-Hoc Networks, according to the results. The technique far outperforms current methods in terms of modern bandwidth consumption of 1478.04kpbs, network density of 95.64%, packet delivery ratio of 95.85%, packet loss ratio of 31.94%, delay in transmission time of 6.981 s and accuracy 6.981%.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Pattnaik SK, Samal SR, Bandopadhaya S, Swain K, Choudhury S, Das JK, Mihovska A, Poulkov V (2022) Future wireless communication technology towards 6G IoT: an application-based analysis of IoT in Real-time location monitoring of employees inside underground mines by using BLE. Sensors 22:3438. https://doi.org/10.3390/s22093438

    Article  Google Scholar 

  2. Rana A, Taneja A, Saluja N, Rani S, Singh A, Alharithi FS, Aldossary SM (2022) Intelligent network solution for improved efficiency in 6G-Enabled expanded IoT Network. Electronics 11:2569. https://doi.org/10.3390/electronics11162569

    Article  Google Scholar 

  3. Sharma R, Patel K, Shah S, Aibin M (2022) Aerial footage analysis using computer vision for efficient detection of points of interest near railway tracks. Aerospace 9:370. https://doi.org/10.3390/aerospace9070370

    Article  Google Scholar 

  4. Khan A, Zhang J, Ahmad S, Memon S, Qureshi HA, Ishfaq M (2022) Dynamic positioning and energy-efficient path planning for disaster scenarios in 5G-Assisted Multi-UAV environments. Electronics 11:2197. https://doi.org/10.3390/electronics11142197

    Article  Google Scholar 

  5. Dahmane S, Yagoubi MB, Brik B, Kerrache CA, Calafate CT, Lorenz P (2022) Multi-constrained and edge-enabled selection of UAV participants in Federated Learning process. Electronics 11:2119. https://doi.org/10.3390/electronics11142119

    Article  Google Scholar 

  6. Park K-W, Kim HM, Shin O-S (2022) A survey on intelligent-reflecting-surface-assisted UAV communications. Energies 15:5143. https://doi.org/10.3390/en15145143

    Article  Google Scholar 

  7. Zhang Z, Xiao Y, Ma Z, Xiao M, Ding Z, Lei X, Fan P (2019) 6G Wireless Networks: Vision, requirements, Architecture, and Key Technologies. IEEE Veh Technol Mag 14:28–41

    Article  Google Scholar 

  8. Alsamhi SH, Shvetsov AV, Kumar S, Hassan J, Alhartomi MA, Shvetsova SV, Sahal R (2022) Hawbani A computing in the Sky: a survey on intelligent ubiquitous computing for UAV-Assisted 6G networks and industry 4.0/5.0. Drones 6:177. https://doi.org/10.3390/drones6070177

    Article  Google Scholar 

  9. Cardoso CMM, Barros FJB, Carvalho JAR, Machado AA, Cruz HAO, de Alcântara Neto MC, Araújo JPL (2022) SNR Prediction with ANN for UAV Applications in IoT Networks based on measurements. Sensors 22:5233. https://doi.org/10.3390/s22145233

    Article  Google Scholar 

  10. Zhang Y, Qiu H (2022) DDQN with Prioritized Experience Replay-Based optimized geographical routing protocol of considering Link Stability and Energy Prediction for UANET. Sensors 22:5020. https://doi.org/10.3390/s22135020

    Article  Google Scholar 

  11. Wheeb AH, Nordin R, Samah AA, Alsharif MH, Khan MA (2022) Topology-based routing protocols and mobility models for flying ad hoc networks: a contemporary review and future research directions. Drones 6:9. https://doi.org/10.3390/drones6010009

    Article  Google Scholar 

  12. Hong L, Guo H, Liu J, Zhang Y (2020) Toward swarm coordination: topology-aware inter-UAV Routing optimization. IEEE Trans Veh Technol 69:10177–10187. https://doi.org/10.1109/TVT.2020.3003356

    Article  Google Scholar 

  13. Abbas M, Sudhanshu M, Thangaraj NA (2023) Eagle strategy arithmetic optimisation algorithm with optimal deep convolutional forest based fintech application for hyper-automation. Enterp Inform Syst 2188123. https://doi.org/10.1080/17517575.2023.2188123

  14. Oubbati OS, Atiquzzaman M, Lorenz P, Tareque MH, Hossain MS (2019) Routing in flying ad hoc networks: survey, constraints, and future challenge perspectives. IEEE Access 7:81057–81105

    Article  Google Scholar 

  15. Xu M, Xie J, Xia Y, Liu W, Luo R, Hu S, Huang D. Improving traditional routing protocols for flying ad hoc networks: A survey. In Proceedings of the 2020 IEEE 6th International Conference on Computer and Communications (ICCC), Chengdu, China, 11–14 December 2020; pp. 162–166. https://doi.org/10.1109/ICCC51575.2020.9345206

  16. Mukherjee A, Keshary V, Pandya K, Dey N, Satapathy SC (2018) Flying ad hoc networks: a comprehensive survey. Inf Decis Sci 701:569–580

    Google Scholar 

  17. Paulraj D, Sethukarasi T, Baburaj E (2023) An efficient hybrid job scheduling optimization (EHJSO) approach to enhance resource search using cuckoo and grey wolf job optimization for cloud environment. PLoS ONE 18(3):e0282600. https://doi.org/10.1371/journal.pone.0282600

    Article  Google Scholar 

  18. Singh K, Verma AK (2019) Flying adhoc networks concept and challenges. Advanced methodologies and technologies in network architecture, mobile computing, and data analytics. IGI Global, Hershey, pp 903–911

    Google Scholar 

  19. Agrawal DP (2017) Applications of sensor networks. Embedded sensor systems. Springer, Singapore, pp 35–63

    Chapter  Google Scholar 

  20. Bekmezci İ, Ülkü EE (2015) Location information sharing with multi token circulation in Flying Ad-Hoc Networks. In: 2015 7th international conference on recent advances in space technologies (RAST). IEEE, pp 669–673

  21. Malik AA, Mahboob A, Khan TMR (2016) Evaluation of OLSR Protocol Implementations using Analytical hierarchical process (AHP). Int J Adv Comput Sci Appl 7(11). https://doi.org/10.14569/ijacsa.2016.071144.

  22. Rezwan S, Choi W (2021) A survey on applications of reinforcement learning in flying ad hoc networks. Electronics 10:449

    Article  Google Scholar 

  23. Jiang J, Han G (2018) Routing protocols for unmanned aerial vehicles. IEEE Commun Mag 56(1):58–63

    Article  Google Scholar 

  24. Awang A, Husain K, Kamel N, Aïssa S (2017) Routing in vehicular Ad-hoc networks: a survey on single- and cross-layer design techniques, and perspectives. IEEE Access 5:9497–9517

    Article  Google Scholar 

  25. Arafat MY, Moh S (2019) Routing protocols for unmanned aerial vehicle networks: a survey. IEEE Access 7:99694–99720. https://doi.org/10.1109/ACCESS.2019.2930813

    Article  Google Scholar 

  26. Arnaldo J, Rosário D, Rosário D, Santos A, Gerla M (2018) Satisfactory video dissemination on FANETs based on an enhanced UAV relay placement service. Ann Telecommun 73:601–612. https://doi.org/10.1007/s12243-018-0658-z

    Article  Google Scholar 

  27. Wu C, Chen X, Ji Y, Liu F, Ohzahata S, Yoshinaga T et al (2019) Packet size-aware broadcasting in VANETs with fuzzy logic and RL-based parameter adaptation. IEEE Access 3:2481–2491

    Article  Google Scholar 

  28. Santhosh Kumar B, Geetha MP, Padmapriya G, Premkumar M (2020) An approach for improving the labelling in a text Corpora using sentiment analysis, advances in Mathematics. Sci J 9(10):81658174

    Google Scholar 

  29. Srivastava A, Prakash J (2021) Future FANET with application and enabling techniques: anatomization and sustainability issues. Comput Sci Rev 39:100359

    Article  MathSciNet  Google Scholar 

  30. Noor F, Khan MA, Al-Zahrani A, Ullah I, Al-Dhlan KA (2020) A review on communications perspective of flying ad hoc networks: Key enabling wireless technologies, applications, challenges and open research topics. Drones 4:65. https://doi.org/10.3390/drones4040065

    Article  Google Scholar 

  31. Guillen-Perez A, Cano MD (2018) Flying ad hoc networks: a new domain for network communications. Sensors 18:3571. https://doi.org/10.3390/s18103571

    Article  Google Scholar 

  32. Agrawal J, Kapoor M (2021) A comparative study on geographic-based routing algorithms for flying ad hoc networks. Concurr Comput Pract Exp 33:e6253 ([CrossRef])

    Article  Google Scholar 

  33. Kim DY, Lee JW (2017) Topology construction for flying ad hoc networks (FANETs). In 2017 IEEE International Conference on Information and Communication Technology Convergence (ICTC). Jejus Island, Korea, pp 153–157

    Google Scholar 

  34. Rahman MFF, Fan S, Zhang Y, Chen L (2021) A comparative study on application of unmanned aerial vehicle systems in agriculture. Agriculture 11:22 [CrossRef]

    Article  Google Scholar 

  35. Shrestha R, Bajracharya R, Kim S (2021) 6G enabled unmanned aerial vehicle traffic management: a perspective. IEEE Access 9:91119–91136 ([CrossRef])

    Article  Google Scholar 

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Correspondence to Bong-Hyun Kim.

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Prakash, M., Neelakandan, S. & Kim, BH. Reinforcement Learning-Based Multidimensional Perception and Energy Awareness Optimized Link State Routing for Flying Ad-Hoc Networks. Mobile Netw Appl 29, 315–333 (2024). https://doi.org/10.1007/s11036-023-02255-y

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