Skip to main content

Advertisement

SpringerLink
Log in

Giraffe kicking optimization algorithm provides efficient routing mechanism in the field of vehicular ad hoc networks

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Deployment of vehicular ad hoc network (VANET) has drawn considerable attention in current times. Energy efficient designs and sensing coverage in networks are the critical issues. Scheming an optimal wake-up system that avoids awakening extra nodes is a very challenging problem. This nature-inspired algorithm known as Giraffe kicking optimization (GKO) helps to balance between exploitation and exploration then helps to awake minimum number of sensor nodes by using the kicking style of a mother giraffe and also help to improve the throughput and prolong the lifetime of the network. Energy efficient routing is a vital phenomenon in the field of VANET which is a part of Wireless Sensor Network (WSNs) are developed to collect the information and to impel them towards the cluster head and the base station. The hybrid C-means based GKO algorithm is useful for VANET to avoid a large amount of energy consumption triggered by the redundant sensor nodes. To provide quality of service, it is essential to awake the minimum number of sensor nodes to consume less energy in the network by using optimized clustering techniques. For this issue, here we have planned a hybrid C-means Giraffe optimization technique with a multi-fitness function used to reach efficient routing enactment in VANET. Then, the GKO is contrasted with some other popular nature-inspired algorithms which are widely used in the field of VANET. We have confirmed our projected procedure against performance parameters and two non-parametric tests, such as Friedman and Holm’s test has been used to analyze the results.

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

  • Angelov PP, Gu X, Principe JC (2018) A Generalized methodology for data analysis. IEEE Trans Cybern. https://doi.org/10.1109/tcyb.2017.2753880

    Article  Google Scholar 

  • Ari AAA, Yenke BO, Labraoui N, Damakoa I, Gueroui A (2016) A power efficient cluster-based routing algorithm for wireless sensor networks: Honeybees swarm intelligence based approach. J Netw Comput Appl 69:77–97

    Article  Google Scholar 

  • Arianmehr S, Jamali MAJ (2020) HybTGR: a hybrid routing protocol based on topological and geographical information in vehicular ad hoc networks. J Ambient Intell Human Comput 11(4):1683–1695

    Article  Google Scholar 

  • Bache M, Lichman K (2013) UCI machine learning repository

  • Bagherlou H, Ghaffari A (2018) A routing protocol for vehicular ad hoc networks using simulated annealing algorithm and neural networks. J Supercomput 74(6):2528–2552

    Article  Google Scholar 

  • Behura A (2021) Optimized data transmission scheme based on proper channel coordination used in vehicular ad hoc networks. Int J Inf Technol, pp 1–10

  • Boussoufa-Lahlah S, Semchedine F, Bouallouche-Medjkoune L (2018) Geographic routing protocols for Vehicular Ad hoc NETworks (VANETs): a survey. Veh Commun 11:20–31

    Google Scholar 

  • Daely PT, Shin SY (2016) Range based wireless node localization using dragonfly algorithm. In: 2016 eighth international conference on ubiquitous and future networks (ICUFN). IEEE, pp 1012–1015

  • Dai M, Tang D, Giret A, Salido MA, Li WD (2013) Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robot Comput-Integr Manuf 29(5):418–429

    Article  Google Scholar 

  • Darwish TS, Bakar KA, Haseeb K (2018) Reliable intersection-based traffic aware routing protocol for urban areas vehicular ad hoc networks. IEEE Intell Transp Syst Mag 10(1):60–73

    Article  Google Scholar 

  • Derrac J, García J, Molina SD, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18

    Article  Google Scholar 

  • Dorigo M, Stützle T (2019) Ant colony optimization: overview and recent advances. In: Handbook of metaheuristics. Springer, Cham, pp 311–351

  • Fatemidokht H, Rafsanjani MK, Gupta BB, Hsu CH (2021) Efficient and secure routing protocol based on artificial intelligence algorithms with UAV-assisted for vehicular Ad Hoc networks in intelligent transportation systems. IEEE Trans Intell Transport Syst

  • Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845

    Article  MathSciNet  Google Scholar 

  • Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35

    Article  Google Scholar 

  • Gerez C, Silva LI, Belati EA, Sguarezi Filho AJ, Costa EC (2019) Distribution network reconfiguration using selective firefly algorithm and a load flow analysis criterion for reducing the search space. IEEE Access 7:67874–67888

    Article  Google Scholar 

  • Ghaffari A (2020) Hybrid opportunistic and position-based routing protocol in vehicular ad hoc networks. J Ambient Intell Humaniz Comput 11(4):1593–1603

    Article  Google Scholar 

  • Gupta GP, Jha S (2018) Integrated clustering and routing protocol for wireless sensor networks using Cuckoo and Harmony search based metaheuristic techniques. Eng Appl Artif Int 68:101–109 (0952-1976)

    Article  Google Scholar 

  • Hamdi MM, Audah L, Rashid SA, Mohammed AH, Alani S, Mustafa AS (2020) A review of applications, characteristics and challenges in vehicular ad hoc networks (VANETs). In: 2020 International Congress on human-computer interaction, optimization and robotic applications (HORA). IEEE, pp 1–7

  • Khelifi H, Luo S, Nour B, Moungla H, Faheem Y, Hussain R, Ksentini A (2019) Named data networking in vehicular ad hoc networks: state-of-the-art and challenges. IEEE Commun Surveys Tutor 22(1):320–351

    Article  Google Scholar 

  • KS SR, Murugan S (2017) Memory based hybrid dragonfly algorithm for numerical optimization problems. Expert Syst Appl 83:63–78

    Article  Google Scholar 

  • Kuila P, Jana PK (2014) A novel differential evolution based clustering algorithm for wireless sensor networks. Appl Soft Comput 25:414–425

    Article  Google Scholar 

  • Kumar D, Mishra K (2017) Portfolio optimization using novel co-variance guided artificial bee colony algorithm, Swarm. Evol Comput 33:119–130

    Article  Google Scholar 

  • Lalwani P, Banka H, Kumar C (2017) CRWO: clustering and routing in wireless sensor networks using optics inspired optimization. Peer-To-Peer Netw Appl 10:453–471

    Article  Google Scholar 

  • Lalwani P, Banka H, Kumar C (2018) BERA: a biogeography-based energy saving routing architecture for wireless sensor networks. Soft Comput 22(5):1651–1667

    Article  Google Scholar 

  • Lee JW, Choi BS, Lee JJ (2011) Energy-efficient coverage of wireless sensor networks using ant colony optimization with three types of pheromones. IEEE Trans Ind Inf 7(3):419–427

    Article  Google Scholar 

  • Li C, Li S, Liu Y (2016) A least squares support vector machine model optimized by moth-flame optimization algorithm for annual power load forecasting. Appl Intell 45(4):1166–1178

    Article  Google Scholar 

  • Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore 635:490

  • Liu C, Zhang G, Guo W, He R (2019) Kalman prediction-based neighbor discovery and its effect on routing protocol in vehicular ad hoc networks. IEEE Trans Intell Transp Syst 21(1):159–169

    Article  Google Scholar 

  • Mann PS, Singh S (2017) Artificial bee colony metaheuristic for energy-efficient clustering and routing in wireless sensor networks. Soft Comput 21(22):6699–6712

    Article  Google Scholar 

  • Méndez E, Castillo O, Soria J, Sadollah A (2017) Fuzzy dynamic adaptation of parameters in the water cycle algorithm. In: Nature-inspired design of hybrid intelligent systems. Springer, pp 297–311

  • Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98

    Article  Google Scholar 

  • Mirjalili S (2016) Dragonfly algorithm: a new metaheuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073

    Article  MathSciNet  Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  • Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mohammad Mirjalili S (2017) Salp swarm algorithm: a bioinspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  • Mohanakrishnan U, Ramakrishnan B (2020) MCTRP: an energy efficient tree routing protocol for vehicular ad hoc network using genetic whale optimization algorithm. Wirel Pers Commun 110(1):185–206

    Article  Google Scholar 

  • Nayyar A, Garg S, Gupta D, Khanna A (2018a) Evolutionary computation: theory and algorithms. In: Nayyar A, Le DN, Nguyen NG (eds) Advances in swarm intelligence for optimizing problems in computer science. Chapman CRC, pp 1–26

    Chapter  Google Scholar 

  • Nayyar A, Le DN, Nguyen NG (eds) (2018b) Advances in swarm intelligence for optimizing problems in computer science. CRC Press, Boca Raton

    Google Scholar 

  • Peraza C, Valdez F, Garcia M, Melin P, Castillo O (2016) A new fuzzy harmony search algorithm using fuzzy logic for dynamic parameter adaptation. Algorithms 9:69

    Article  MathSciNet  Google Scholar 

  • Perez J, Valdez F, Castillo O, Melin P, Gonzalez C, Martinez G (2017) Interval type-2 fuzzy logic for dynamic parameter adaptation in the bat algorithm. Soft Comput 21(3):667–685

    Article  Google Scholar 

  • Ramamoorthy R, Thangavelu M (2021) An enhanced hybrid ant colony optimization routing protocol for vehicular ad-hoc networks. J Ambient Intell Human Comput, pp 1–32

  • Rao RS, Narasimham SVL, Raju MR, Rao AS (2010) Optimal network reconfiguration of large-scale distribution system using harmony search algorithm. IEEE Trans Power Syst 26(3):1080–1088

    Google Scholar 

  • Shamsaldin AS, Rashid TA, Al-Rashid Agha RA, Al-Salihi NK, Mohammadi M (2019) Donkey and smuggler optimization algorithm: a collaborative working approach to path finding. J Comput Des Eng 6(4):562–583

    Google Scholar 

  • Srinivas M, Naidu RR, Sastry CS, Mohan CK (2015) Content based medical image retrieval using dictionary learning. Neurocomputing 168:880–895

    Article  Google Scholar 

  • Suganthi K, Vinayagasundaram J, Aarthi S (2015) Randomized fault-tolerant virtual backbone tree to improve the lifetime of wireless sensor networks. Comput Electric Eng 4:8. https://doi.org/10.1016/j.compeleceng.2015.02.017

    Article  Google Scholar 

  • Thangaramya K, Kulothungan K, Logambigai R, Selvi M, Ganapathy S, Kannan A (2019) Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IOT. Comput Netw 151:211–223

    Article  Google Scholar 

  • Vaisakh K, Praveena P, Rao SRM, Meah K (2012) Solving dynamic economic dispatch problem with security constraints using bacterial foraging PSO-DE algorithm. Int J Electric Power Energy Syst 39(1):56–67

    Article  Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  • Yıldız BS, Yıldız AR (2017) Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes. Mater Test 59(5):425–429

    Article  Google Scholar 

  • Zhang D, Ge H, Zhang T, Cui YY, Liu X, Mao G (2018) New multi-hop clustering algorithm for vehicular ad hoc networks. IEEE Trans Intell Transp Syst 20(4):1517–1530

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Veer Surendra Sai University of Technology, Odisha, Burla, India

    Aradhana Behura & Manas Ranjan Kabat

  2. NIT Warangal, Warangal, India

    M. Srinivas

Authors
  1. Aradhana Behura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Srinivas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Manas Ranjan Kabat
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Srinivas.

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

Behura, A., Srinivas, M. & Kabat, M.R. Giraffe kicking optimization algorithm provides efficient routing mechanism in the field of vehicular ad hoc networks. J Ambient Intell Human Comput 13, 3989–4008 (2022). https://doi.org/10.1007/s12652-021-03519-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-021-03519-9

Keywords

Search

Navigation