Skip to main content

Advertisement

SpringerLink
Log in

COCOMA: a resource-optimized cooperative UAVs communication protocol for surveillance and monitoring applications

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

The deployment of a swarm of cooperative Unmanned Aerial Vehicles (UAVs) to pursue a mission is knowing an increasing success nowadays. This is mainly because deploying a group of cooperating UAVs instead of one single UAV offers numerous advantages, including the extension of the mission coverage, fault tolerance in case of losing a UAV during a mission, and improving the data gathering accuracy. Multicast routing, on the other hand, is a critical operation for UAV swarm networks, as it facilitates critical tasks including information transmission and swarm coordination. Moreover, designing efficient multicast protocols with Quality of Service (QoS) can be challenging due to various factors such as limited energy constraints and the dynamically changing topology with 3D movement which causes frequent changes in the network topology. Therefore, in this paper, we investigate the multicast routing problem in a swarm of UAVs. We first provide a detailed classification of existing efforts in swarm routing protocols in terms of transmission strategies. And second, we propose a new Energy Efficient Inter-UAV Multicast Routing Protocol for surveillance and monitoring applications called “COCOMA”. We illustrate through extensive simulations that COCOMA achieves the desired efficient communication backbone and Quality-of-Service. We also discuss and implement an improvement of COCOMA called COCOMA+ and show that both versions are efficient and effective in term of reducing the total emission energy by at least 10 dBm compared to the state-of-art work SP-GMRF. In addition, our proposal optimizes also the End-to-End Delay, the number of hops in the routing process, and the network throughput which consequently leads to increasing the packet delivery ratio by more than to 22% compared to SP-GMRF protocol.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Bushnaq, O. M., Chaaban, A., & Al-Naffouri, T. Y. (2021). The role of UAV-IOT networks in future wildfire detection. IEEE Internet of Things Journal, 8(23), 16984–16999.

    Article  Google Scholar 

  2. Ruetten, L., Regis, P.A., Feil-Seifer, D., Sengupta, S. (2020).“Area-optimized uav swarm network for search and rescue operations,” in 2020 10th Annual computing and communication workshop and conference (CCWC). IEEE, pp. 0613–0618.

  3. Zhou, X., Lee, W. S., Ampatzidis, Y., Chen, Y., Peres, N., & Fraisse, C. (2021). Strawberry maturity classification from UAV and near-ground imaging using deep learning. Smart Agricultural Technology, 1, 100001.

    Article  Google Scholar 

  4. Zhang C., Fu, W. (2021).“Optimal model for patrols of uavs in power grid under time constraints.” International Journal of Performability Engineering, vol. 17, no. 1,

  5. Gu, Q., Fan, T., Pan, F., & Zhang, C. (2020). A vehicle-UAV operation scheme for instant delivery. Computers & Industrial Engineering, 149, 106809.

    Article  Google Scholar 

  6. Zuo, Z., Liu, C., Han, Q. L., & Song, J. (2022). Unmanned aerial vehicles: Control methods and future challenges’’. IEEE/CAA Journal of Automatica Sinica, 28(99), 1–14.

    Google Scholar 

  7. Liu, Z., Wang, X., Shen, L., Zhao, S., Cong, Y., Li, J., et al. (2020). Mission-oriented miniature fixed-wing UAV swarms: A multilayered and distributed architecture. IEEE Transactions on Systems, Man, and Cybernetics: Systems,. https://doi.org/10.1109/TSMC.2020.3033935.

    Article  Google Scholar 

  8. Tazrin, T., Fouda, M. M., Fadlullah, Z. M., & Nasser, N. (2021). Uv-cds: An energy-efficient scheduling of UAVS for premises sterilization. IEEE Transactions on Green Communications and Networking, 5(3), 1191–1201.

    Article  Google Scholar 

  9. Tazrin, T., Ayaz, Z., Fouda, M.M., Fadlullah, Z.M., (2021).“On energy-efficient uav route scheduling to offload health data from under-served rural communities,” In: 2021 IEEE international conference on internet of things and intelligence systems (IoTaIS). IEEE, pp. 86–91.

  10. Hassan, M.A., Javed, A.R.,Hassan, T., Band, S.S., Sitharthan, R., Rizwan, M. (2022). “Reinforcing communication on the internet of aerial vehicles,” IEEE Transactions on Green Communications and Networking,

  11. Arafat, M. Y., & Moh, S. (2021). A q-learning-based topology-aware routing protocol for flying ad hoc networks. IEEE Internet of Things Journal, 9(3), 1985–2000.

    Article  Google Scholar 

  12. Guo, J., Gao, H., Liu, Z., Huang, F., Zhang, J., Li, X., Ma, J. (2022).“Icra: An intelligent clustering routing approach for uav ad hoc networks,” IEEE Transactions on Intelligent Transportation Systems

  13. Sahraoui, Y., Kerrache, C.A., Amadeo, M., Vegni, A.M., Korichi, A., Nebhen, J. Imran, M. (2021).“A cooperative crowdsensing system based on flying and ground vehicles to control respiratory viral disease outbreaks,” Ad Hoc Networks, p. 102699,

  14. Maddikunta, P. K. R., Hakak, S., Alazab, M., Bhattacharya, S., Gadekallu, T. R., Khan, W. Z., & Pham, Q. V. (2021). Unmanned aerial vehicles in smart agriculture: Applications, requirements, and challenges’’. IEEE Sensors Journal, 21(16), 17608–17619.

    Article  Google Scholar 

  15. YU, Z., ZHANG, Y., JIANG, B., FU, J., JIN, Y. (2022). A review on fault-tolerant cooperative control of multiple unmanned aerial vehicles. Chinese Journal of Aeronautics, vol.35, no.1, pp. 1–18, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1000936121001771

  16. Qiu, J., Grace, D., Ding, G., Zakaria, M. D., & Wu, Q. (2019). Air-ground heterogeneous networks for 5g and beyond via integrating high and low altitude platforms. IEEE Wireless Communications, 26(6), 140–148.

    Article  Google Scholar 

  17. Chen, X., Tang, J., Lao, S. (2020). Review of unmanned aerial vehicle swarm communication architectures and routing protocols, Applied Sciences, vol.10, no.10, [Online]. Available: https://www.mdpi.com/2076-3417/10/10/3661

  18. Khan, M.A., Safi, A., Qureshi, I.M., Khan, I.U. (2017).Flying ad-hoc networks (fanets): A review of communication architectures, and routing protocols. In 2017 First International conference on latest trends in electrical engineering and computing technologies (INTELLECT), pp. 1–9.

  19. Khan, M., Qureshi, I., & Khanzada, F. (2019). A hybrid communication scheme for efficient and low-cost deployment of future flying ad-hoc network (fanet). Drones, 3(1), 16.

    Article  Google Scholar 

  20. Asaamoning, G., Mendes, P., Rosário, D., Cerqueira, E. (2021).Drone swarms as networked control systems by integration of networking and computing, Sensors, vol.21, no.8, [Online]. Available: https://www.mdpi.com/1424-8220/21/8/2642

  21. Fidanova, S., Atanassov, K. (2017). Flying ant colony optimization algorithm for combinatorial optimization,Studia Informatica, vol.38, no.4, pp. 31–40, [Online]. Available: http://studiainformatica.polsl.pl/index.php/SI/article/view/824

  22. Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 26(1), 29–41.

    Article  Google Scholar 

  23. Konatowski S. Pawlowski, P. (2018). Ant colony optimization algorithm for uav path planning,” in 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET), pp. 177–182.

  24. Yu, Y., Ru, L., Chi, W., Liu, V., Yu, Q., & Fang, K. (2016). Ant colony optimization based polymorphism-aware routing algorithm for ad hoc uav network. Multimedia Tools and Applications, 75, 1–26.

    Article  Google Scholar 

  25. Maistrenko, V.A., Alexey, L.V., Danil, V.A., (2016).“Experimental estimate of using the ant colony optimization algorithm to solve the routing problem in fanet,” In 2016 International siberian conference on control and communications (SIBCON), pp. 1–10.

  26. Leonov, A.V. (June 2016)“Application of bee colony algorithm for fanet routing,” In 2016 17th International conference of young specialists on micro/nanotechnologies and electron devices (EDM), pp. 124–132.

  27. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization, technical report - tr06, Technical Report, Erciyes University, 01

  28. Pham, D., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., Zaidi, M. (2006). “The bees algorithm - a novel tool for complex optimisation problems,” Proceedings of IPROMS 2006 conference, 12

  29. Leonov, A.V. (2016). Modeling of bio-inspired algorithms anthocnet and beeadhoc for flying ad hoc networks (fanets), In 2016 13th International scientific-technical conference on actual problems of electronics instrument engineering (APEIE), vol.2. IEEE, pp. 90–99.

  30. Pan, T. S., Kien, D., Pan, J. S., & Nguyen, T. T. (2017). An Unmanned Aerial Vehicle Optimal Route Planning Based on Compact Artificial Bee Colony, 11, 361–369.

  31. Bousbaa, F.Z., Kerrache, C.A., Mahi, Z., Tahari, A.E.K., Lagraa, N., Yagoubi, M.B. (2020). Geouavs: A new geocast routing protocol for fleet of uavs, Computer Communications, vol. 149, pp. 259 – 269, [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0140366419307947

  32. Bine, L.M.S., Boukerche, A., Ruiz, L.B., Loureiro, A.A.F. (2021). Iodagr: An airway-based geocast routing protocol for internet of drones, In ICC 2021 - IEEE International conference on communications, pp. 1–6.

  33. Farhan, K.A., Abdel-Fattah, F., Altarawneh, F., Lafi, M. (2019). Survey paper on multicast routing in mobile ad-hoc networks, In 2019 IEEE Jordan international joint conference on electrical engineering and information technology (JEEIT), , pp. 449–452.

  34. Redwan, H., Choi, S., Park, J. H., & Kim, J. (2019). Predictive geographic multicast routing protocol in flying ad hoc networks. International Journal of Distributed Sensor Networks, 15(7), 155014771984387.

    Article  Google Scholar 

  35. Wang, S., Li, W., Zhao, P., Fu, Y., Li, C. (2020). A reliability-aware adaptive greedy-multicast routing protocol for 3d highly dynamic networks, In 2020 IEEE 91st Vehicular technology conference (VTC2020-Spring), pp. 1–5.

  36. Sharma, V., Kumar, R., & Kumar, N. (2018). Dptr: Distributed priority tree-based routing protocol for fanets. Computer Communications, 122, 129–151.

    Article  Google Scholar 

  37. Oubbati, O. S., Atiquzzaman, M., Lorenz, P., Tareque, M. H., & Hossain, M. S. (2019). Routing in flying ad hoc networks: Survey, constraints, and future challenge perspectives. IEEE Access, 7, 81057–81105.

    Article  Google Scholar 

  38. Peng, J., Gao, H., Liu, L., Li, N., Xu, X. (2020).“TBM: An efficient trajectory-based multicast routing protocol for sparse uav networks,” In: 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International conference on smart city; IEEE 6th International conference on data science and systems (HPCC/SmartCity/DSS), pp. 867–872.

  39. Bousbaa, F.Z., Lagraa, N., Kerrache, C.A., Zhou, F., Yagoubi, M.B., Hussain, R. (2018). “A distributed time-limited multicast algorithm for vanets using incremental power strategy,” Computer Networks, 145 pp. 141–155, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1389128618303724

Download references

Author information

Authors and Affiliations

  1. Laboratoire d’Informatique et de Mathématiques, Université Amar Telidji de Laghouat, Laghouat, Algeria

    Youssra Cheriguene, Fatima Zohra Bousbaa, Chaker Abdelaziz Kerrache, Soumia Djellikh & Nasreddine Lagraa

  2. Hassan II University ENS Casablanca, Casablanca, Morocco

    Mohamed Lahby

  3. College of Information Technology, UAE University, Al Ain, UAE

    Abderrahmane Lakas

Authors
  1. Youssra Cheriguene
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fatima Zohra Bousbaa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chaker Abdelaziz Kerrache
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Soumia Djellikh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nasreddine Lagraa
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohamed Lahby
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Abderrahmane Lakas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chaker Abdelaziz Kerrache.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheriguene, Y., Bousbaa, F.Z., Kerrache, C.A. et al. COCOMA: a resource-optimized cooperative UAVs communication protocol for surveillance and monitoring applications. Wireless Netw (2022). https://doi.org/10.1007/s11276-022-03031-8

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11276-022-03031-8

Keywords

Search

Navigation