Skip to main content

Advertisement

Log in

A delay aware routing approach for FANET based on emperor penguins colony algorithm

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Many drones work together in an ad hoc manner to form flying ad hoc networks. While these networks have opened up new possibilities for a wide range of applications like the commercial, and residential, they have also presented several problems, including high-speed nodes, limited density, and abrupt dynamic topology. Therefore, routing is a complex problem in such networks. The optimized link state routing protocol served as an inspiration for this plan. This article proposes a delay-conscious routing protocol for flying ad hoc networks and offers a new method for calculating the link lifespan among two unmanned aerial vehicles based on factors such as their distance apart, relative speed, and the direction in which they travel. An approach is presented in which the emperor penguin colony algorithm is used to select multi-point relay nodes. A node's ability to serve as a multi-point relay node is based on its remaining energy, link lifespan, neighboring degree, and eagerness. In sum up, the suggested approach generates paths between nodes taking energy and lifespan into account. The performance evaluation of the proposed routing is done against ML-OLSR and MP-OLSR. At a minimum, a 15% and 32% increase in latency and energy consumption were achieved by implementing the proposed technique.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Algorithm 1:
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The author confirms that, if necessary, all data will be made available to the journal and the esteemed editor.

References

  1. Rahmani AM et al (2021) E-learning development based on internet of things and blockchain technology during COVID-19 pandemic. Mathematics 9(24):3151

    Article  Google Scholar 

  2. Prakash M, Neelakandan S, Kim B-H (2023) Reinforcement learning-based multidimensional perception and energy awareness optimized link state routing for flying Ad-Hoc networks. Mobile Netw Appl 1–19

  3. Sadrishojaei M, Kazemian F (2023) Development of an enhanced blockchain mechanism for internet of things authentication. Wirel Pers Commun 132(4):2543–2561. https://doi.org/10.1007/s11277-023-10731-7

    Article  Google Scholar 

  4. Pham-Quoc C, Nguyen-Duy-Nhat V, Le MT, Nguyen-Le H, Tang-Tan C, Tang-Anh T, Nguyen-Xuan N (2023) Robust 3D beamforming for secure UAV communications by DAE. Mobile Netw Appl 1–9

  5. Sadrishojaei M, Kazemian F (2024) Clustered routing scheme in IoT during COVID-19 pandemic using hybrid black widow optimization and harmony search algorithm. In: Operations Research Forum (vol 5, No 2). Springer International Publishing, Cham, p 47

  6. Liu C, Zhang Z (2022) Towards a robust FANET: Distributed node importance estimation-based connectivity maintenance for UAV swarms. Ad Hoc Netw 125:102734

    Article  Google Scholar 

  7. Beegum TR, Idris MYI, Ayub MNB, Shehadeh HA (2023) Optimized routing of UAVs using bio-inspired algorithm in FANET: A systematic review. IEEE Access 11:15588–15622

    Article  Google Scholar 

  8. Aote SS et al (2024) An improved deep learning method for flying object detection and recognition. SIViP 18(1):143–152

    Article  Google Scholar 

  9. Abbas G et al (2022) A position-based reliable emergency message routing scheme for road safety in VANETs. Comput Netw 213:109097

    Article  Google Scholar 

  10. Gupta A, Barthwal A, Vardhan H, Kakria S, Kumar S, Parihar AS (2023) Evolutionary study of distributed authentication protocols and its integration to UAV-assisted FANET. Multim Tools Appl 82(27):42311–42330

    Article  Google Scholar 

  11. Hemalatha R, Umamaheswari R, Jothi S (2022) An efficient stable node selection based on Garson’s pruned recurrent neural network and MSO model for multipath routing in MANET. Concurr Comp: Pract Exp 34(21):e7105

    Article  Google Scholar 

  12. Deepa S, Sridhar K (2023) Design of routing protocol with the internet of things devices over mobile ad hoc networks. SIViP 17(8):4513–4522

    Article  Google Scholar 

  13. Pan Y, Yang Y, Liu H, Li W (2023) UAVs and mobile sensors trajectories optimization with deep learning trained by genetic algorithm towards data collection scenario. Mob Netw Appl 28(2):808–823

    Article  Google Scholar 

  14. Ergunsah S, Tümen V, Kosunalp S, Demir K (2023) Energy-efficient animal tracking with multi-unmanned aerial vehicle path planning using reinforcement learning and wireless sensor networks. Concurrency and Computation: Practice and Experience 35(4):e7527

    Article  Google Scholar 

  15. Sadrishojaei M, Navimipour NJ, Reshadi M, Hosseinzadeh M (2023) An energy-aware scheme for solving the routing problem in the internet of things based on jaya and flower pollination algorithms. J Ambient Intell Humaniz Comput 14(8):11363–11372

    Article  Google Scholar 

  16. Tsao KY, Girdler T, Vassilakis VG (2022) A survey of cyber security threats and solutions for UAV communications and flying ad-hoc networks. Ad Hoc Netw 133:102894

    Article  Google Scholar 

  17. Lansky J et al (2022) Development of a lightweight centralized authentication mechanism for the Internet of Things driven by fog. Mathematics 10(22):4166

    Article  Google Scholar 

  18. Silva RRD, Escarpinati MC, Backes AR (2021) Sugarcane crop line detection from UAV images using genetic algorithm and Radon transform. Signal Image Video Process 15(8):1723–1730

    Article  Google Scholar 

  19. Usha M, Sathiamoorthy J, Ahilan A, Mahalingam T (2023) SWEEPER: Secure Waterfall Energy-Efficient Protocol-Enabled Routing in FANET. IETE J Res 1–15

  20. Saffre F, Hildmann H, Anttonen A (2023) Force-Based Self-Organizing MANET/FANET with a UAV Swarm. Future Internet 15(9):315

    Article  Google Scholar 

  21. Sadrishojaei M, Jafari Navimipour N, Reshadi M, Hosseinzadeh M (2021) Clustered routing method in the internet of things using a moth-flame optimization algorithm. Int J Commun Syst 34(16):e4964

    Article  Google Scholar 

  22. Maistrenko VA, Alexey LV, Danil VA (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). IEEE, pp 1–10

    Google Scholar 

  23. Sadrishojaei M et al (2022) An energy-aware clustering method in the IoT using a swarm-based algorithm. Wireless Netw 28(1):125–136

    Article  Google Scholar 

  24. Nematollahi M, Ghaffari A, Mirzaei A (2024) Task offloading in Internet of Things based on the improved multi-objective aquila optimizer. SIViP 18(1):545–552

    Article  Google Scholar 

  25. Hosseinzadeh M et al (2022) A hybrid delay aware clustered routing approach using Aquila Optimizer and firefly algorithm in internet of things. Mathematics 10(22):4331

    Article  Google Scholar 

  26. Malar ACJ, Priya MD, Janakiraman S (2021) A hybrid crow search and gray wolf optimization algorithm-based reliable non-line-of-Sight node positioning scheme for vehicular ad hoc networks. Int J Commun Syst 34(3)

  27. Sadrishojaei M, Navimipour NJ, Reshadi M, Hosseinzadeh M (2022) An energy-aware IoT routing approach based on a swarm optimization algorithm and a clustering technique. Wirel Pers Commun 127(4):3449–3465

    Article  Google Scholar 

  28. Yadav A, Verma S (2021) A hybrid approach based on ACO and firefly algorithm for routing in FANETs. In: International Conference on Computing Science, Communication and Security. Springer International Publishing, Cham, pp 234–246

    Chapter  Google Scholar 

  29. Amponis G, Lagkas T, Argyriou V, Moscholios I, Zevgara M, Ouzounidis S, Sarigiannidis P (2022) Swarm mobility models and impact of link state awareness in ad hoc routing. In: 2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP). IEEE, pp 762–767

    Google Scholar 

  30. Pliatsios D et al (2021) Drone-base-station for next-generation Internet-of-Things: A comparison of swarm intelligence approaches. IEEE Open J Antennas Propag 3:32–47

    Article  Google Scholar 

  31. Deokate B, Lal C, Trček D, Conti M (2019) Mobility-aware cross-layer routing for peer-to-peer networks. Comput Electr Eng 73:209–226

    Article  Google Scholar 

  32. Aissa M, Abdelhafidh M, Mnaouer AB (2021) EMASS: A Novel Energy, Safety and Mobility Aware-Based Clustering Algorithm for FANETs. IEEE Access 9:105506–105520

    Article  Google Scholar 

  33. Abbas Ali F, Erode Dhanapal KR (2022) Topology based energy efficient routing using integration of fuzzy based markov chain cluster-optimized novel ant bee colony approach in FANET. Concurr Comp: Pract Exp 34(23):e7175

    Article  Google Scholar 

  34. Zhao L et al (2021) A novel improved artificial bee colony and blockchain-based secure clustering routing scheme for FANET. China Commun 18(7):103–116

    Article  Google Scholar 

  35. Kumar S, Raw RS, Bansal A (2023) LoCaL: Link-optimized cone-assisted location routing in flying ad hoc networks. Int J Commun Syst 36(2):e5375

    Article  Google Scholar 

  36. Sefati SS, Halunga S, Farkhady RZ (2022) Cluster selection for load balancing in flying ad hoc networks using an optimal low-energy adaptive clustering hierarchy based on optimization approach. Aircr Eng Aerosp Technol 94(8):1344–1356

    Article  Google Scholar 

  37. Darabkh KA, Alfawares MG, Althunibat S (2019) MDRMA: Multi-data rate mobility-aware AODV-based protocol for flying ad-hoc networks. Veh Commun 18:100163

    Google Scholar 

  38. Darabkh KA, Judeh MS, Salameh HB, Althunibat S (2018) Mobility aware and dual phase AODV protocol with adaptive hello messages over vehicular ad hoc networks. AEU-Int J Electron Commun 94:277–292

    Article  Google Scholar 

  39. Zheng Y, Wang Y, Li Z, Dong L, Jiang Y, Zhang H (2014) A mobility and load aware OLSR routing protocol for UAV mobile ad-hoc networks

  40. Yi J, Adnane A, David S, Parrein B (2011) Multipath optimized link state routing for mobile ad hoc networks. Ad Hoc Netw 9(1):28–47

    Article  Google Scholar 

  41. Bounceur A (2016) CupCarbon: A new platform for designing and simulating smart-city and IoT wireless sensor networks (SCI-WSN). In Proceedings of the International Conference on Internet of things and Cloud Computing. pp 1–1

  42. Bounceur A, Clavier L, Combeau P, Marc O, Vauzelle R, Masserann A, ... Lounis M (2018). CupCarbon: A new platform for the design, simulation and 2D/3D visualization of radio propagation and interferences in IoT networks. In 2018 15th IEEE annual consumer communications & networking conference (CCNC). IEEE, pp 1–4 

Download references

Funding

This research is not supported.

Author information

Authors and Affiliations

Authors

Contributions

This article has only one author, and the same person did all cases.

Corresponding author

Correspondence to Mahyar Sadrishojaei.

Ethics declarations

Ethics approval

The manuscript truly represents the author own analysis and research, and it is not under consideration for publication elsewhere.

Consent to publish

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sadrishojaei, M. A delay aware routing approach for FANET based on emperor penguins colony algorithm. Peer-to-Peer Netw. Appl. 17, 3542–3555 (2024). https://doi.org/10.1007/s12083-024-01764-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-024-01764-1

Keywords