A Reliable Link-Adaptive Position-Based Routing Protocol for Flying ad hoc Network

Abstract

Flying ad hoc network (FANET) provides portable and flexible communication for many applications and possesses several unique design challenges; a key one is the successful delivery of messages to the destination, reliably. For reliable communication, routing plays an important role, which establishes a path between source and destination on the basis of certain criteria. Conventional routing protocols of FANET generally use a minimum hop count criterion to find the best route between source and destination, which results in lower latency with the consideration that there is single source/destination network environment. However, in a network with multiple sources, the minimum hop count routing criterion along with the 1-Hop HELLO messages broadcasted by each node in the network may deteriorate the network performance in terms of high End-to-End (ETE) delay and decrease in the lifetime of the network. This research work proposes a Reliable link-adaptive position-based routing protocol (RLPR) for FANET. It uses relative speed, signal strength, and energy of the nodes along with the geographic distance towards the destination using a forwarding angle. This angle is used to determine the forwarding zone that decreases the undesirable control messages in the network in order to discover the route. RLPR enhances the network performance by selecting those relay nodes which are in the forwarding zone and whose geographic movement is towards the destination. Additionally, RLPR selects the next hop with better energy level and uses signal strength and relative speed of the nodes to achieve high connectivity-level. Based on the performance evaluation performed in the Network simulator (ns-2.35), it has been analysed that RLPR outperforms the Robust and reliable predictive based routing (RARP) and Ad hoc on-demand distance vector (AODV) protocols in different scenarios. The results show that RLPR achieves a 33% reduction in control messages overhead as compared to RARP and 45% reduction as compared to AODV. Additionally, RLPR shows a 55% improvement in the lifetime of the network as compared to RARP and 65% as compared to AODV. Moreover, the search success rate in RLPR is 16% better as compared to RARP and 28% as compared to AODV.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

References

  1. 1.

    Bekmezci I, Sahingoz OK, Temel Ş (2013) Flying ad-hoc networks (fanets): A survey. Ad Hoc Netw 11(3):1254–1270

    Article  Google Scholar 

  2. 2.

    Shakoor S, Kaleem Z, Baig MI, Chughtai O, Duong TQ, Nguyen LD (2019) Role of uavs in public safety communications: Energy efficiency perspective. IEEE Access 7:140665–140679

    Article  Google Scholar 

  3. 3.

    Sheng Z, Tuan HD, Nasir AA, Duong TQ, Poor HV (2020) Secure uav-enabled communication using han–kobayashi signaling. IEEE Trans Wirel Commun 19(5):2905–2919

    Article  Google Scholar 

  4. 4.

    Wang B, Sun Y, Liu D, Nguyen HM, Duong TQ (2020) Social-aware uav-assisted mobile crowd sensing in stochastic and dynamic environments for disaster relief networks. IEEE Trans Veh Technol 69(1):1070–1074

    Article  Google Scholar 

  5. 5.

    Kaleem Z, Yousaf M, Qamar A, Ahmad A, Duong TQ, Choi W, Jamalipour A (2019) Uav-empowered disaster-resilient edge architecture for delay-sensitive communication. IEEE Network 33(6):124–132

    Article  Google Scholar 

  6. 6.

    Bekmezci I, Sen I, Erkalkan E (2015) Flying ad hoc networks (fanet) test bed implementation. In: 2015 7th International conference on recent advances in space technologies (RAST), pp 665–668. IEEE

  7. 7.

    Waharte S, Trigoni N (2010) Supporting search and rescue operations with uavs. In: 2010 International conference on emerging security technologies, pp 142–147. IEEE

  8. 8.

    Arafat MY, Moh S (2019) Routing protocols for unmanned aerial vehicle networks: A survey. IEEE Access 7:99694–99720

    Article  Google Scholar 

  9. 9.

    Alshabtat AI, Dong L, Li J, Yang F (2010) Low latency routing algorithm for unmanned aerial vehicles ad-hoc networks. Int J Electr Comput Eng 6(1):48–54

    Google Scholar 

  10. 10.

    Rosati S, Krużelecki K, Traynard L, Mobile BR (2013) Speed-aware routing for uav ad-hoc networks. In: 2013 IEEE globecom workshops (GC Wkshps), pp 1367–1373. IEEE

  11. 11.

    Ni X, Lan K-C, Malaney R (2008) On the performance of expected transmission count (etx) for wireless mesh networks. In: Proceedings of the 3rd International conference on performance evaluation methodologies and tools, p 77, ICST (Institute for Computer Sciences, Social-Informatics and …

  12. 12.

    Pu C (2018) Link-quality and traffic-load aware routing for uav ad hoc networks. In: 2018 IEEE 4th International conference on collaboration and internet computing (CIC), pp 71–79. IEEE

  13. 13.

    Xie P (2018) An enhanced olsr routing protocol based on node link expiration time and residual energy in ocean fanets. In: 2018 24th Asia-Pacific Conference on Communications (APCC), pp 598–603, IEEE

  14. 14.

    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. In: 2014 International conference on information and communications technologies (ICT 2014), pp 1–7

  15. 15.

    Hong J, Zhang D (2019) Tarcs: A topology change aware-based routing protocol choosing scheme of fanets. Electronics 8(3):274

    MathSciNet  Article  Google Scholar 

  16. 16.

    Choi S-C, Hussen HR, Park J-H, Kim J (2018) Geolocation-based routing protocol for flying ad hoc networks (fanets). In: 2018 Tenth international conference on ubiquitous and future networks (ICUFN), pp 50–52. IEEE

  17. 17.

    Li X, Yan J (2017) Lepr: Link stability estimation-based preemptive routing protocol for flying ad hoc networks. In: 2017 IEEE symposium on computers and communications (ISCC), pp 1079–1084. IEEE

  18. 18.

    Liu K, Zhang J, Zhang T (2008) The clustering algorithm of uav networking in near-space. In: 2008 8th International symposium on antennas, propagation and EM theory, pp 1550–1553. IEEE

  19. 19.

    Zang C, Zang S (2011) Mobility prediction clustering algorithm for uav networking. In: 2011 IEEE GLOBECOM Workshops (GC Wkshps), pp 1158–1161. IEEE

  20. 20.

    Aadil F, Raza A, Khan M, Maqsood M, Mehmood I, Rho S (2018) Energy aware cluster-based routing in flying ad-hoc networks. Sensors 18(5):1413

    Article  Google Scholar 

  21. 21.

    Kuiper E, Nadjm-Tehrani S (2010) Geographical routing with location service in intermittently connected manets. IEEE Trans Veh Technol 60(2):592–604

    Article  Google Scholar 

  22. 22.

    Hyeon S, Kim K-I, Yang S (2010) A new geographic routing protocol for aircraft ad hoc networks. In: 29th digital avionics systems conference, pp 2–E. IEEE

  23. 23.

    Gankhuyag G, Shrestha AP, Yoo S-J (2017) Robust and reliable predictive routing strategy for flying ad-hoc networks. IEEE Access 5:643–654

    Article  Google Scholar 

  24. 24.

    Lin L, Sun Q, Li J, Yang F (2012) A novel geographic position mobility oriented routing strategy for uavs. J Comput Inf Syst 8(2):709–716

    Google Scholar 

  25. 25.

    Broyles D, Jabbar A, Sterbenz JP, et al. (2010) Design and analysis of a 3–d gauss-markov mobility model for highly-dynamic airborne networks. In: Proceedings of the international telemetering conference (ITC), San Diego, CA, 25–28

  26. 26.

    Karp B, Kung H-T (2000) Gpsr: Greedy perimeter stateless routing for wireless networks. In: Proceedings of the 6th annual international conference on Mobile computing and networking, pp 243–254. ACM

  27. 27.

    Lin L, Sun Q, Wang S, Yang F (2012) A geographic mobility prediction routing protocol for ad hoc uav network. In: 2012 IEEE Globecom Workshops, pp 1597–1602. IEEE

  28. 28.

    Iordanakis M, Yannis D, Karras K, Bogdos G, Dilintas G, Amirfeiz M, Colangelo G, Baiotti S (2006) Ad-hoc routing protocol for aeronautical mobile ad-hoc networks. In: Fifth international symposium on communication systems, networks and digital signal processing (CSNDSP), pp 1–5. Citeseer

  29. 29.

    Marconato EA, Maxa JA, Pigatto DF, Pinto AS, Larrieu N, Branco KRC (2016) Ieee 802.11 n vs. ieee 802.15. 4: a study on communication qos to provide safe fanets. In: 2016 46th Annual IEEE/IFIP international conference on dependable systems and networks workshop (DSN-W), pp 184–191. IEEE

  30. 30.

    Fabra F, Calafate CT, Cano J-C, Manzoni P (2017) On the impact of inter-uav communications interference in the 2.4 ghz band. In: 2017 13th international wireless communications and mobile computing conference (IWCMC), pp 945–950. IEEE

  31. 31.

    Li C, Chen Y, Han X, Zhu L (2015) A self-adaptive and link-aware beaconless forwarding protocol for vanets. Int J Distrib Sens Netw 11(8):757269

    Article  Google Scholar 

  32. 32.

    Rezende C, Ramos HS, Pazzi RW, Boukerche A, Frery AC, Loureiro AA (2012) Virtus: A resilient location-aware video unicast scheme for vehicular networks. In: 2012 IEEE international conference on communications (ICC), pp 698–702. IEEE

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Omer Chughtai.

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

Verify currency and authenticity via CrossMark

Cite this article

Usman, Q., Chughtai, O., Nawaz, N. et al. A Reliable Link-Adaptive Position-Based Routing Protocol for Flying ad hoc Network. Mobile Netw Appl (2021). https://doi.org/10.1007/s11036-021-01758-w

Download citation

Keywords

  • Reliable routing
  • Data dissemination
  • Composite metric
  • Reliable progression
  • Flying ad hoc network