Abstract
In recent years, flying ad hoc networks (FANETs) have witnessed a notable increase in its applications after the onset of the collaborations of the small unmanned aerial vehicles (UAVs). Because of its inherent characteristics, FANETs are used in diverse application ranging from the military to civil domain. Conversely, there are certain issues pertaining to the communication among the UAVs in view of the high mobility and limited battery resources available in the UAVs, resulting in their short lifetime. The paper is an attempt to address these issues plaguing to the short lifespan of the UAVs. In this paper, we propose a hybrid bio-inspired algorithm HGSOFA for optimizing cluster head (CH) selection in a FANETs. HGSOFA utilizes the hybrid implementation of glowworm swarm optimization (GSO) and firefly algorithm (FA). In this paper, we explain the step-by-step working of the HGSOFA and then performance is evaluated through rigorous simulations. Two separate network areas with varying node density is considered for conducting all the simulations. A robust experimental environment is developed using Taguchi and orthogonal methods. HGSOFA’s performance is tested against the conventional GSO and FA algorithms in respect of cluster building time, energy consumption and first node death. Comparable results have showcased the advantages of the HGSOFA as compared to other algorithms.
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References
Aadil F, Raza A, Khan MF et al (2018) Energy aware cluster-based routing in flying ad-hoc networks. Sensors (Switzerland) 18:1–16. https://doi.org/10.3390/s18051413
Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2017.06.059
Ali H, Shahzad W, Khan FA (2012) Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2011.05.036
Arafat MY, Moh S (2019) A survey on cluster-based routing protocols for unmanned aerial vehicle networks. IEEE Access 7:498–516. https://doi.org/10.1109/ACCESS.2018.2885539
Asha G, Gowrishankar (2018) An energy aware routing mechanism in WSNs using PSO and GSO algorithm. In: 2018 5th International conference on signal processing and integrated networks, SPIN 2018
Barrado C, Meseguer R, López J et al (2010) Wildfire monitoring using a mixed air-ground mobile network. IEEE Pervasive Comput 9:24–32
Barzin A, Sadegheih A, Zare HK, Honarvar M (2019) Hybrid swarm intelligence-based clustering algorithm for energy management in wireless sensor networks. J Ind Syst Eng 12:78–106
Baskaran M, Sadagopan C (2015) Synchronous firefly algorithm for cluster head selection in WSN. Sci World J. https://doi.org/10.1155/2015/780879
Bekmezci I, Sahingoz OK, Temel Ş (2013) Flying ad-hoc networks (FANETs): a survey. Ad Hoc Netw 11:1254–1270. https://doi.org/10.1016/j.adhoc.2012.12.004
Bekmezci I, Ermis M, Kaplan S (2014) Connected multi UAV task planning for flying ad hoc networks. In: 2014 IEEE International Black Sea conference on communications and networking, BlackSeaCom 2014
Bitam S, Mellouk A, Member S et al (2015) Bio-inspired routing algorithms survey for vehicular ad hoc networks. IEEE Commun Surv Tutor 17:843–867. https://doi.org/10.1109/COMST.2014.2371828
Chinara S, Rath SK (2009) A survey on one-hop clustering algorithms in mobile ad hoc networks. J Netw Syst Manag 17:183–207. https://doi.org/10.1007/s10922-009-9123-7
Cooper C, Franklin D, Ros M et al (2017) A comparative survey of VANET clustering techniques. IEEE Commun Surv Tutor 19:657–681. https://doi.org/10.1109/COMST.2016.2611524
Dattatraya KN, Rao KR (2019) Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2019.04.003
Fister I, Yang XS, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46. https://doi.org/10.1016/j.swevo.2013.06.001
Guo L, Wang GG, Wang H, Wang D (2013) An effective hybrid firefly algorithm with harmony search for global numerical optimization. Sci World J. https://doi.org/10.1155/2013/125625
Kalaiselvi T, Nagaraja P, Basith ZA (2017) A review on glowworm swarm optimization. Int J Inf Technol 3:49–56
Kanistras K, Martins G, Rutherford MJ, Valavanis KP (2013) A survey of unmanned aerial vehicles (UAVs) for traffic monitoring. In: 2013 International conference on unmanned aircraft systems, ICUAS 2013—conference proceedings. IEEE, pp 221–234
Khan MA, Khan IU, Safi A, Quershi IM (2018a) Dynamic routing in flying ad-hoc networks using topology-based routing protocols. Drones 2:1–15. https://doi.org/10.3390/drones2030027
Khan MA, Safi A, Qureshi IM, Khan IU (2018b) Flying ad-hoc networks (FANETs): a review of communication architectures, and routing protocols. In: 2017 1st International conference on latest trends in electrical engineering and computing technologies, INTELLECT 2017 2018-Jan, pp 1–9. https://doi.org/10.1109/INTELLECT.2017.8277614
Khan A, Aftab F, Zhang Z (2019a) Self-organization based clustering scheme for FANETs using glowworm swarm optimization. Phys Commun 36:100769. https://doi.org/10.1016/j.phycom.2019.100769
Khan A, Aftab F, Zhang Z (2019b) BICSF: bio-inspired clustering scheme for FANETs. IEEE Access 7:31446–31456. https://doi.org/10.1109/ACCESS.2019.2902940
Krishnanand KN, Ghose D (2009) Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intell 3:87–124. https://doi.org/10.1007/s11721-008-0021-5
Li Q, Liu B (2017) Clustering using an improved krill herd algorithm. Algorithms. https://doi.org/10.3390/a10020056
Liu Y, Wang Y, Wang J, Shen Y (2020) Distributed 3D relative localization of UAVs. IEEE Trans Veh Technol XX:1. https://doi.org/10.1109/tvt.2020.3017162
Manathara JG, Sujit PB, Beard RW (2011) Multiple UAV coalitions for a search and prosecute mission. J Intell Robot Syst 62:125–158. https://doi.org/10.1007/s10846-010-9439-2
Maza I, Caballero F, Capitán J, Ollero JRMA (2011) Experimental results in multi-UAV coordination for disaster management and civil security applications. J Intell Robot Syst 61:563–585. https://doi.org/10.1007/s10846-010-9497-5
Noh S-C, Jeon H-B, Chae C-B (2020) Energy-efficient deployment of multiple UAVs using ellipse clustering to establish base stations. IEEE Wirel Commun Lett 9:1155–1159. https://doi.org/10.1109/lwc.2020.2982889
Shankar T, Shanmugavel S, Rajesh A (2016) Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm Evol Comput. https://doi.org/10.1016/j.swevo.2016.03.003
Singh B, Lobiyal DK (2012) A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Human Centric Comput Inf Sci. https://doi.org/10.1186/2192-1962-2-13
Sun Z, Wang P, Vuran MC et al (2011) BorderSense: border patrol through advanced wireless sensor networks. Ad Hoc Netw 9:468–477. https://doi.org/10.1016/j.adhoc.2010.09.008
Surender Reddy S, Srinivasa Rathnam C (2016) Optimal power flow using glowworm swarm optimization. Int J Electr Power Energy Syst. https://doi.org/10.1016/j.ijepes.2016.01.036
Wang J, Jiang C, Han Z, Ren Y (2017) Taking drones to the next level. IEEE Veh Technol Mag 12:73–82
Xiang H, Tian L (2011) Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosyst Eng 108:174–190. https://doi.org/10.1016/j.biosystemseng.2010.11.010
Xie J, Wan Y, Kim JH et al (2014) A survey and analysis of mobility models for airborne networks. IEEE Commun Surv Tutor. https://doi.org/10.1109/SURV.2013.111313.00138
Yang XS (2009) Firefly algorithms for multimodal optimization. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)
Yuan Y, Cheng L, Wang Z, Sun C (2019) Position tracking and attitude control for quadrotors via active disturbance rejection control method. Sci China Inf Sci 62:1–10. https://doi.org/10.1007/s11432-018-9548-5
Zhu S, Wang D, Low CB (2013) Ground target tracking using UAV with input constraints. J Intell Robot Syst Theory Appl 69:417–429. https://doi.org/10.1007/s10846-012-9737-y
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Kumar, K., Arya, R. A hybrid approach for cluster head determination of unmanned aerial vehicle in flying ad-hoc networks. Int J Syst Assur Eng Manag 14 (Suppl 3), 759–773 (2023). https://doi.org/10.1007/s13198-021-01057-3
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DOI: https://doi.org/10.1007/s13198-021-01057-3