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UAV Communication in FANETs with Metaheuristic Techniques

  • Meghna GoswamiEmail author
  • Rajeev Arya
  • Prateek
Conference paper
  • 33 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1162)

Abstract

the recent advancement in communication technologies has paved the way for the flying ad hoc networks (fanets) by enabling the deployment of small uavs unmanned aerial vehicles (uavs). owing to its potential features, fanets have a wide variety of applications. however, the uavs face certain restrictions in terms of their battery power and mobility, resulting in short lifetime and unreliable routing. in this paper, we try to primarily address the issue plaguing to the short lifetime of the fanets, namely the limited power availability of the uavs. the paper aims to minimize the energy consumption with the aid of a clustering scheme and observe its impact on the lifetime of the network. two different clustering methods are employed to achieve this, and a comparative analysis based on its performances is presented. the first scheme implemented is the combination of the k-means clustering algorithm and the firefly algorithm while the second method is based on the glowworm swarm optimization (gso) and the firefly algorithm. the primary clusters are formed with the k-means clustering and the gso, respectively, while the firefly algorithm elects the cluster heads and derives the optimal positions of the uavs in the cluster. the performance of the schemes is further compared in terms of the cluster building time and energy consumption.

Keywords

FANETs Clustering Cluster head selection GSO with firefly Minimum energy consumption 

References

  1. 1.
    Khan, M.A., Safi, A., Qureshi, I.M., Khan, I.U.: 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), Karachi, pp. 1–9 (2017).  
  2. 2.
    Bekmezci, I., Sahingoz, O.K., Temel, S.: Flying Ad-Hoc Networks (FANETs): a survey. Ad Hoc Netw. 11, 1254–1270 (2013)
  3. 3.
    Aadil, F., Raza, A., Khan, M.F., Maqsood, M., Mehmood, I., Rho, S.: Energy aware cluster-based routing in Flying Ad-Hoc Networks. Sensors 18, 1413 (2018).  
  4. 4.
    Khan, A., Aftab, F., Zhang, Z.: BISCF: bio-inspired clustering scheme for FANETs. IEEE Access (2019).  
  5. 5.
    Alijarah, I., Ludwig, S.A.: A new clustering approach based on glowworm swarm optimization. In: IEEE Congress on Evolutionary Computation, 20–23 June 2013, Cancun, Mexico
  6. 6.
    Baskaran, M., Sadagopan, C.: Synchronous firefly algorithm for cluster head selection in WSN. Sci. World J. Article ID 78087 (2015)
  7. 7.
    Li, Q., Liu, B.: Clustering using an improved krill herd algorithm. Algorithms 10, 56 (2017).  
  8. 8.
    Merwe, D., Engelbrecht, A.P.: Data clustering using particle swarm optimization
  9. 9.
    Dattatraya, K.N., Rao, K.R.: Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN. J. King Saud Univ. Comput. Inf. Sci.
  10. 10.
    Asha G.R., Gowrishankar: An energy aware routing mechanisms in WSNs using PSO and GSO algorithm. In: 5th International Conference on Signal Processing and Integrated Network (SPIN) (2018)
  11. 11.
    Kalaiselvi T., Nagaraja P., Basith A.: A review on glowworm swarm optimization. Int. J. Inf. Technol. (IJIT) 3(2) (2017)
  12. 12.
    Fister, I., Fister Jr., I., Yang, X., Brest, J.: A comprehensive review of firefly algorithms. Swarm Evol. Comput. 13, 34–46 (2013)

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.National Institute of Technology PatnaPatnaIndia

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