Skip to main content

Advertisement

SpringerLink
Log in

A hybrid genetic artificial neural network (G-ANN) algorithm for optimization of energy component in a wireless mesh network toward green computing

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Wireless mesh networks are a special class of wireless networks that are implemented as a collection of radio nodes in a mesh pattern or topology. Unlike MANETs, the mobility of nodes is very less in the topology. Quality of service is an essential metric in the performance of mesh networks which are attributed to several parameters including optimal routing through shortest path, ability for other nodes to communicate even if a particular node in the mesh fails, minimization of packet loss and time delay, computational complexity and cost, energy. This research paper is focused toward minimization of energy taken as the objective function and a five-stage neural network is used and trained after optimizing with a genetic algorithm. The experiments have been conducted in NS2 and Qualnet environment with a varying number of mesh routers and energy computed. The performance of energy savings has been compared against conventional routing techniques like AODV and a bee colony optimization technique presented in the literature. An energy savings of 51% have been reported in the paper justifying the superiority of the hybrid G-ANN algorithm.

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
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Ali M, Kamoun F (1993) Neural networks for shortest path computation and routing in computer networks. IEEE Trans Neural Netw 4:941–953

    Article  Google Scholar 

  • Bheemalingaiah M, Naidu MM, Rao DS, Varaprasad G (2009) Energy aware node disjoint multipath routing in mobile ad-hoc network. J Theor Appl Inf Technol 5(4):416–419

    Google Scholar 

  • Buratti C, Conti A, Dardari D, Verdone R (2009) An overview on wireless sensor networks technology and evolution. Sensors 9:6869–6896

    Article  Google Scholar 

  • Cardei M, Cheng MX, Cheng X, Du D-Z (2002) Connected domination in ad hoc wireless networks. In: Proceedings of the sixth international conference on computer science and informatics

  • Chen B, Jamieson K, Balakrishnan H, Morris R (2002) Span: an energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks. Kluwer Academic Publishers, Dordrecht

    MATH  Google Scholar 

  • Curry RM, Smith JC (2016) A survey of optimization algorithms for wireless sensor network lifetime maximization. J Comput Ind Eng 101:145–166

    Article  Google Scholar 

  • Girgis MR, Mahmoud TM, Abdullatif BA, Rabie A (2014) Solving the wireless mesh network design problem using genetic algorithm and simulated annealing optimization methods. Int J Comput Appl 96(11):1–10

    Google Scholar 

  • Gomes RL, Junior WM, Cerqueira E, Abelem AJ (2011) Using fuzzy link cost and dynamic choice of link quality metrics to achieve QoS and QoE in wireless mesh networks. J Netw Comput Appl 34(2):506–516

    Article  Google Scholar 

  • He B, Xie B, Agrawal DP (2007) Optimizing the internet gateway deployment in a wireless mesh network. In: Mobile adhoc and sensor systems, pp 1–9

  • Jie J, Chen J, Chang G, Wen Y, Song J (2009) Multi-objective optimization for coverage control in wireless sensor network with adjustable sensing radius. Comput Math Appl 57:1767–1775

    Article  MathSciNet  MATH  Google Scholar 

  • Jyothirmai P, Raj JS, Smys S (2017) Secured self organizing network architecture in wireless personal networks. Wirel Pers Commun 96(4):5603–5620

    Article  Google Scholar 

  • Karaboga D, Okdem S, Ozturk C (2012) Cluster based wireless sensor network routing using artificial bee colony algorithm. Wirel Netw 18(7):847–860

    Article  Google Scholar 

  • Koksal CE, Balakrishnan H (2006) Quality-aware routing metrics for time-varying wireless mesh networks. IEEE J Sel Areas Commun 24(11):1984–1994

    Article  Google Scholar 

  • Kumar N, Kumar M, Patel RB (2010) Coverage and connectivity aware neural network based energy efficient routing in wireless sensor networks. J Appl Graph Theory Wirel Ad Hoc Netw Sens Netw 2(1):45–60

    Google Scholar 

  • Liu L, Feng G (2007) Simulated annealing based multi-constrained QoS routing in mobile ad hoc networks. Wirel Pers Commun 41(3):393–405

    Article  Google Scholar 

  • More A, Raisinghani V (2017) A survey on energy efficient coverage protocols in wireless sensor networks. J Comput Inf Sci 29(4):428–448

    Google Scholar 

  • Narendran R, Mala C (2012) Optimization of QoS parameters for channel allocation in cellular networks using soft computing techniques. Adv Intell Soft Comput 130:621–631

    Article  Google Scholar 

  • Praveena A, Smys S (2016) Efficient cryptographic approach for data security in wireless sensor networks using MES VU. In: 2016 10th International conference on intelligent systems and control (ISCO). IEEE, pp 1–6

  • Pries R, Staehle D, Staehle B, Tran-Gia P (2010) On optimization of wireless mesh networks using genetic algorithms. Int J Adv Internet Technol 3(2):13–28

    Google Scholar 

  • Sharma S, Kumar S, Singh B (2014) Hybrid intelligent routing in wireless mesh networks: soft computing based approaches. Int J Intell Syst Appl 1:45–57

    Google Scholar 

  • Smys S, Bala GJ, Raj JS (2010) Self-organizing hierarchical structure for wireless networks. In: 2010 International conference on advances in computer engineering (ACE). IEEE, pp 268–270

  • Wang X, Vasilakos AV, Chen M, Liu Y, Kwon TT (2012) A survey of green mobile networks: opportunities and challenges. Mob Netw Appl 17:4–20

    Article  Google Scholar 

  • Yie W, Heidemann J, Estrin D (2002) An energy-efficient MAC protocol for wireless sensor networks. In: Proceedings of the 21st annual joint conference of the IEEE computer and communications societies (INFOCOM), New York, USA, pp 1567–1576

  • Younis M, Senturk IF, Akkaya K, Lee S, Senel F (2014) Topology management techniques for tolerating node failures in wireless sensor networks: a survey. Comput Netw 58:254–283

    Article  Google Scholar 

  • Zhu N, O’Connor I (2013) iMASKO: a genetic algorithm based optimization framework for wireless sensor networks. J Sens Actuator Netw 2:675–699

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Adhiparasakthi Engineering College, Melmaruvathur, Tamilnadu, India

    B. Prakash

  2. Department of Electronics and Communication Engineering, Adhiparasakthi Engineering College, Melmaruvathur, Tamilnadu, India

    S. Jayashri

  3. Department of Electronics and Communication Engineering, Sapthagiri College of Engineering, Dharmapuri, Tamilnadu, India

    T. S. Karthik

Authors
  1. B. Prakash
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Jayashri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. T. S. Karthik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B. Prakash.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interests.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by P. Pandian.

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

Prakash, B., Jayashri, S. & Karthik, T.S. A hybrid genetic artificial neural network (G-ANN) algorithm for optimization of energy component in a wireless mesh network toward green computing. Soft Comput 23, 2789–2798 (2019). https://doi.org/10.1007/s00500-019-03789-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-03789-8

Keywords

Search

Navigation