Skip to main content

Advertisement

Log in

Optimum bandwidth allocation in wireless networks using differential evolution

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

Abstract

Wireless networking is experiencing a tremendous growth in new standards of communication and computer applications. Currently, wireless networks exist in various forms, providing different facilities. However, due to some limitations as compared with wired counterparts, wireless networks face several major challenges and one of them is optimum bandwidth allocation. The focus of optimum bandwidth allocation is to reduce the losses and satisfying quality of service (QoS) requirements. In wireless networks, the term bandwidth allocation is attributed as the distribution of bandwidth resources among different users, which affects the serviceability of the entire system. Though many studies related to bandwidth allocation have been reported already, however, only sub-optimal solutions have been provided so far. In this research, we proposed to use the differential evolution (DE) algorithm to allocate bandwidth through a bandwidth reservation scheme in the Cellular IP network, in order to improve the QoS at an acceptable level. DE belongs to a class of evolutionary algorithms (EA), like particle swarm optimization and genetic algorithm. A DE-based method is used which looks for any free bandwidth in the cell or in adjacent cells and provides it to the cell where required. In case, it fails to find the free available bandwidth then it will search the bandwidth which is standby for non real-time users and allocates it to the real-time users that will help in improving the QoS in terms of connection/call dropping probability for real-time users. Simulation results show that the proposed method performs better as compared to previously used EA models for bandwidth allocation.

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

Access this article

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

Instant access to the full article PDF.

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

Similar content being viewed by others

References

  • Aalo VA, Efthymoglou GP, Soithong T, Alwakeel M, Alwakeel S (2014) Performance analysis of multi-hop amplify-and-forward relaying systems in Rayleigh fading channels with a Poisson interference field. IEEE Trans Wirel Commun 13(1):24–35

    Article  Google Scholar 

  • Ali M, Siarry P, Pant M (2012) An efficient differential evolution based algorithm for solving multi-objective optimization problems. Eur J Oper Res 217(2):404–416

    MathSciNet  MATH  Google Scholar 

  • Amzallag D, Raz D (2010) Resource allocation algorithms for the next generation cellular networks. In: Cormode G, Thottan M (eds) Algorithms for next generation networks. Springer, London, pp 99–129

    Chapter  Google Scholar 

  • Anbar M, Vidyarthi D (2009) On demand bandwidth reservation for real-time traffic in cellular ip network using evolutionary techniques. Int J Recent Trend Eng 2(1):150–156

    Google Scholar 

  • Anbar M, Vidyarthi DP (2009) On demand bandwidth reservation for real-time traffic in cellular IP network using particle swarm optimization. Int J Bus Data Comm Netw 5(3):53–66

    Article  Google Scholar 

  • Behzadi MS, Niasati M (2015) Comparative performance analysis of a hybrid PV/FC/battery stand-alone system using different power management strategies and sizing approaches. Int J Hydrogen Energy 40(1):538–548

    Article  Google Scholar 

  • Chai R, Wang X, Chen Q, Svensson T (2013) Utility-based bandwidth allocation algorithm for heterogeneous wireless networks. Sci China Inf Sci 56(2):1–13

    Article  MathSciNet  Google Scholar 

  • Chang J-Y, Chen H-L (2003) Dynamic-grouping bandwidth reservation scheme for multimedia wireless networks. IEEE J Sel Areas Commun 21(10):1566–1574

    Article  Google Scholar 

  • Chen Z, Mi CC, Xiong R, Xu J, You C (2014) Energy management of a power-split plug-in hybrid electric vehicle based on genetic algorithm and quadratic programming. J Power Sources 248:416–426

    Article  Google Scholar 

  • Civicioglu P, Besdok E (2013) A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif Intell Rev 39(4):315–346

    Article  Google Scholar 

  • Coello CAC, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems. Springer, Berlin

    MATH  Google Scholar 

  • Deb A, Roy JS, Gupta B (2014) Performance comparison of differential evolution, particle swarm optimization and genetic algorithm in the design of circularly polarized microstrip antennas. IEEE Trans Antennas Propag 62(8):3920–3928

    Article  MATH  Google Scholar 

  • Dimitriou CD, Mavromoustakis CX, Mastorakis G, Pallis E (2013) On the performance response of delay-bounded energy-aware bandwidth allocation scheme in wireless networks. In: Communications Workshops (ICC), 2013 IEEE International Conference on, IEEE

  • Dixit A, Lannoo B, Colle D, Pickavet M, Demeester P (2015) Energy efficient dynamic bandwidth allocation for ethernet passive optical networks: overview, challenges, and solutions. Opt Switch Netw 18:169–179

    Article  Google Scholar 

  • Dong X-l, Liu S-q, Tao T, Li S-p, Xin K-l (2012) A comparative study of differential evolution and genetic algorithms for optimizing the design of water distribution systems. J Zhejiang Univ Sci A 13(9):674–686

    Article  Google Scholar 

  • Gabryel M, Woźniak M, Damaševičius R (2015) An application of differential evolution to positioning queueing systems. In: Rutkowski L, Korytkowski M, Scherer R, Tadeusiewicz R, Zadeh L, Zurada J (eds) Artificial intelligence and soft computing, vol 9120. Springer, Cham

    Chapter  Google Scholar 

  • Guo J, Liu F, Lui J, Jin H (2016) Fair network bandwidth allocation in IaaS datacenters via a cooperative game approach. IEEE/ACM Trans Netw 24(2):873–886

    Article  Google Scholar 

  • Huang Z, Liu W, Zhong J (2017) Estimating the real positions of objects in images by using evolutionary algorithm. In: International Conference on, IEEE Machine vision and information technology (CMVIT)

  • Iwan M, Akmeliawati R, Faisal T, Al-Assadi HM (2012) Performance comparison of differential evolution and particle swarm optimization in constrained optimization. Proc Eng 41:1323–1328

    Article  Google Scholar 

  • Javidy B, Hatamlou A, Mirjalili S (2015) Ions motion algorithm for solving optimization problems. Appl Soft Comput 32:72–79

    Article  Google Scholar 

  • Jiang S, Ji Z, Shen Y (2014) A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints. Int J Electr Power Energy Syst 55:628–644

    Article  Google Scholar 

  • Kukkonen S, Coello CAC (2017) Generalized Differential Evolution for Numerical and Evolutionary Optimization. NEO 2015. Springer, New York, pp 253–279

    MATH  Google Scholar 

  • Lampinen J, Storn R (2004) Differential evolution. In: New optimization techniques in engineering, vol 141. Springer, Berlin, Heidelberg, pp 123–166

    Chapter  Google Scholar 

  • Lim W, Kourtessis P, Kanonakis K, Milosavljevic M, Tomkos I, Senior JM (2014) Dynamic bandwidth allocation in heterogeneous OFDMA-PONs featuring intelligent LTE-A traffic queuing. J Lightwave Technol 32(10):1877–1885

    Article  Google Scholar 

  • Martignon F, Paris S, Filippini I, Chen L, Capone A (2015) Efficient and truthful bandwidth allocation in wireless mesh community networks. IEEE/ACM Trans Netw 23(1):161–174

    Article  Google Scholar 

  • Mohamed AW (2017) An efficient modified differential evolution algorithm for solving constrained non-linear integer and mixed-integer global optimization problems. Int J Mach Learn Cybernet 8(3):989–1007

    Article  Google Scholar 

  • Moradi MH, Abedini M (2012) A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Int J Electr Power Energy Syst 34(1):66–74

    Article  Google Scholar 

  • Panduro MA, Brizuela CA, Balderas LI, Acosta DA (2009) A comparison of genetic algorithms, particle swarm optimization and the differential evolution method for the design of scannable circular antenna arrays. Prog Electromagn Res B 13:171–186

    Article  Google Scholar 

  • Pourmousavi SA, Nehrir MH, Colson CM, Wang C (2010) Real-time energy management of a stand-alone hybrid wind-microturbine energy system using particle swarm optimization. IEEE Trans Sustain Energy 1(3):193–201

    Article  Google Scholar 

  • Rekanos IT (2008) “Shape reconstruction of a perfectly conducting scatterer using differential evolution and particle swarm optimization. IEEE Trans Geosci Remote Sens 46(7):1967–1974

    Article  Google Scholar 

  • Sabar NR, Abawajy J, Yearwood J (2017) Heterogeneous cooperative co-evolution memetic differential evolution algorithm for big data optimization problems. IEEE Trans Evol Comput 21(2):315–327

    Article  Google Scholar 

  • Sarigiannidis AG, Iloridou M, Nicopolitidis P, Papadimitriou G, Pavlidou F-N, Sarigiannidis PG, Louta MD, Vitsas V (2015) Architectures and bandwidth allocation schemes for hybrid wireless-optical networks. IEEE Commun Surv Tutor 17(1):427–468

    Article  Google Scholar 

  • Sedighizadeh M, Esmaili M, Esmaeili M (2014) Application of the hybrid Big Bang-Big crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems. Energy 76:920–930

    Article  Google Scholar 

  • Shams R, Khan FH, Abbass S, Javaid R (2017) Bandwidth allocation for wireless cellular network by using genetic algorithm. Wirel Pers Commun 95(2):245–260

    Article  Google Scholar 

  • Sharma N, Anpalagan A (2014) Composite differential evolution aided channel allocation in OFDMA systems with proportional rate constraints. J Commun Netw 16(5):523–533

    Article  Google Scholar 

  • Singh H, Srivastava L (2014) Modified differential evolution algorithm for multi-objective VAR management. Int J Electr Power Energy Syst 55:731–740

    Article  Google Scholar 

  • Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  • Teijeiro D, Pardo XC, Penas DR, González P, Banga JR, Doallo R (2017) A cloud-based enhanced differential evolution algorithm for parameter estimation problems in computational systems biology. Cluster Comput 20:1937–1950

    Article  Google Scholar 

  • Wang Y, Cai Z (2012) Combining multiobjective optimization with differential evolution to solve constrained optimization problems. IEEE Trans Evol Comput 16(1):117–134

    Article  Google Scholar 

  • Wang R, Mukherjee B (2014) Spectrum management in heterogeneous bandwidth optical networks. Opt Switch Netw 11:83–91

    Article  Google Scholar 

  • Wang G-G, Hossein Gandomi A, Yang X-S, Hossein Alavi A (2014) A novel improved accelerated particle swarm optimization algorithm for global numerical optimization. Eng Comput 31(7):1198–1220

    Article  Google Scholar 

  • Wang Y, Bi J, Zhang K (2015) Design and implementation of a software-defined mobility architecture for IP networks. Mob Netw Appl 20(1):40–52

    Article  Google Scholar 

  • Yang GY, Dong ZY, Wong KP (2008) A modified differential evolution algorithm with fitness sharing for power system planning. IEEE Trans Power Syst 23(2):514–522

    Article  Google Scholar 

  • Yildiz AR (2013a) Hybrid Taguchi-differential evolution algorithm for optimization of multi-pass turning operations. Appl Soft Comput 13(3):1433–1439

    Article  Google Scholar 

  • Yildiz AR (2013b) A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations. Appl Soft Comput 13(3):1561–1566

    Article  Google Scholar 

  • Zhang T, Molisch AF, Shen Y, Zhang Q, Win MZ (2014) Joint power and bandwidth allocation in cooperative wireless localization networks. In: IEEE International Conference on, IEEE Communications (ICC), 2014

  • Zheng SY, Zhang SX (2017) A jumping genes inspired multi-objective differential evolution algorithm for microwave components optimization problems. Appl Soft Comput 59:276–287

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Peer Azmat Shah or Khalid M. Awan.

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

Afzal, Z., Shah, P.A., Awan, K.M. et al. Optimum bandwidth allocation in wireless networks using differential evolution. J Ambient Intell Human Comput 10, 1401–1412 (2019). https://doi.org/10.1007/s12652-018-0858-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-018-0858-4

Keywords

Navigation