Open Access   Article Go Back

Glowworm Swarm Optimization based Clustered on – Demand Load Balancing Scheme (GSO-COD-LBS) for Heterogeneous Mobile Ad hoc Networks

P. Aruna Devi1 , K. Karthikeyan2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-8 , Page no. 130-136, Aug-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i8.130136

Online published on Aug 31, 2019

Copyright © P. Aruna Devi, K. Karthikeyan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: P. Aruna Devi, K. Karthikeyan, “Glowworm Swarm Optimization based Clustered on – Demand Load Balancing Scheme (GSO-COD-LBS) for Heterogeneous Mobile Ad hoc Networks,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.130-136, 2019.

MLA Style Citation: P. Aruna Devi, K. Karthikeyan "Glowworm Swarm Optimization based Clustered on – Demand Load Balancing Scheme (GSO-COD-LBS) for Heterogeneous Mobile Ad hoc Networks." International Journal of Computer Sciences and Engineering 7.8 (2019): 130-136.

APA Style Citation: P. Aruna Devi, K. Karthikeyan, (2019). Glowworm Swarm Optimization based Clustered on – Demand Load Balancing Scheme (GSO-COD-LBS) for Heterogeneous Mobile Ad hoc Networks. International Journal of Computer Sciences and Engineering, 7(8), 130-136.

BibTex Style Citation:
@article{Devi_2019,
author = {P. Aruna Devi, K. Karthikeyan},
title = {Glowworm Swarm Optimization based Clustered on – Demand Load Balancing Scheme (GSO-COD-LBS) for Heterogeneous Mobile Ad hoc Networks},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {8 2019},
volume = {7},
Issue = {8},
month = {8},
year = {2019},
issn = {2347-2693},
pages = {130-136},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4800},
doi = {https://doi.org/10.26438/ijcse/v7i8.130136}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i8.130136}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4800
TI - Glowworm Swarm Optimization based Clustered on – Demand Load Balancing Scheme (GSO-COD-LBS) for Heterogeneous Mobile Ad hoc Networks
T2 - International Journal of Computer Sciences and Engineering
AU - P. Aruna Devi, K. Karthikeyan
PY - 2019
DA - 2019/08/31
PB - IJCSE, Indore, INDIA
SP - 130-136
IS - 8
VL - 7
SN - 2347-2693
ER -

VIEWS PDF XML
307 315 downloads 159 downloads
  
  
           

Abstract

Obtaining quality of service (QoS) through several routing schemes attracts researchers in the field of MANETs. Optimized routing through energy aware load balanced schemes is plays a significant role in ensuring QoS as well as many real – time applications. In this phase of research work, Glowworm Swarm Optimization is used for performing clustering operation. An adaptive on – demand routing mechanism is also employed. Simulation settings are used for analyzing the performance of the GSO-COD-LBS with other routing protocols / solutions / schemes using the metrics packet delivery ratio, throughput, packets drop, overhead and delay. From the results that are obtained through simulations it is inferred that GSO-COD-LBS outperforms other existing routing protocols and our earlier proposed works.

Key-Words / Index Term

routing, load balancing, energy, QoS, MANET

References

[1]. Hui, Chenga, Shengxiang, Yangb, Xingwei, Wangc, 2012. Immigrants-enhanced multi-population genetic algorithms for dynamic shortest path routing problems in mobile ad hoc networks. Int. J. Appl. Artif. Intell. 26 (7), 673–695.
[2]. Hui, Chenga, Shengxiang, Yangb, Jiannong, Cao, 2013. Dynamic genetic algorithm for the dynamic load balanced clustering problem in mobile ad hoc networks. J. Expert Syst. Appl. 40 (4), 1381–1392.
[3]. Shengxiang, Yang, Hui, Cheng, Fang, Wang, 2010. Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks. J. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 40 (1), 52–63.
[4]. Bhaskar, Nandi, Subhabrata, Barman, Soumen, Paul, 2010. Genetic algorithm based optimization of clustering in ad hoc networks. Int. J. Comput. Sci. Inf. Secur. 7 (1), 165–169.
[5]. Bo, Peng, Lei, Li, 2012. A method for QoS multicast based on genetic simulated annealing algorithm. Int. J. Future Gener. Commun. Netw. 5 (1), 43–60.
[6]. Abin, Paul, Preetha, K.G., 2013. A cluster based leader election algorithm for MANETs. In: International Conference on Control Communication and Computing, pp. 496–499.
[7]. Ting, Lu, Jie, Zhu, 2013. Genetic algorithm for energy efficient QoS multicast routing. IEEE Commun. Lett. 17 (1), 31–34.
[8]. John, M., Shea, Joseph, P., Macker, 2013. Automatic selection of number of cluster in networks using relative Eigen value quality. In: Proceedings of IEEE Military Communication, pp. 131–136.
[9]. Samaneh, A.D., Jamshid, A., 2016. Toward cluster-based weighted compressive data aggregation in wireless sensor networks. Ad Hoc Netw. 36 (1), 368–385.
[10]. Syed Zohaib, H.Z., Aloul, F., Sagahyroon, A., Wassim, El-Hajj, 2013. Optimizing complex cluster formation in MANETs using SAT/ILP techniques. J. IEEE Sens. 13 (6), 2400–2412.
[11]. Peng, Zhao, Xinyu, Yang, Wei, Yu, Xinwen, FuXinyu, 2013. A loose-virtual-clustering-based routing for power heterogeneous MANETs. J. IEEE Trans. Veh. Technol. 62 (5), 2290–2302.
[12]. Ibukunola, A., Modupea, Oludayo, O., Olugbarab, Abiodun, Modupea, 2013. Minimizing energy consumption in wireless adhoc networks with meta heuristics. In: Proceeding of 4th International Conference on Ambient Systems, Networks and Technologies, vol. 19, pp. 106–115.
[13]. El Khawaga, Sally E., Saleh, Ahmed I., Ali, Hesham A., 2016. An administrative cluster-based cooperative caching (ACCC) strategy for mobile ad hoc networks. J. Netw. Comput. Appl. 69, 54–76.
[14]. Jabbar W.A., Ismail M., Nordin R., 2017.Energy and mobility conscious multipath routing scheme for route stability and load balancing in MANETs. Simulation Modelling Practice and Theory. 77, 245-271.
[15]. Ali H.A., Areed M.F., Elewely D.I., 2018. An on-demand power and load-aware multi-path node-disjoint source routing scheme implementation using NS-2 for mobile ad-hoc networks. Simulation Modelling Practice and Theory. 80, 50 – 65.
[16]. Aruna Devi P., Karthikeyan K., 2018, Bio Inspired Ant Colony Optimization Based NeighborNode Selection and Enhanced Ad Hoc on DemandDistance Vector to Defending Against Black HoleAttack by Malicious Nodes in Mobile AD HOC NetworksJournalofComputational and Theoretical Nanoscience. 15, 3011 – 3018.
[17]. Aruna Devi P., Karthikeyan K., 2019, Fuzzy Clustering and Constructive Relay-based Cooperative and Load Balanced Routing in MANET. Journal of Advanced Research in Dynamical and Control Systems. 11 (04), 1743 – 1753.
[18]. Aruna Devi P., Karthikeyan K., 2019, Grey Wolf Optimization based Clustered On – Demand Load Balancing Scheme (GWO-COD-LBS) for Heterogeneous Mobile Ad hoc Networks. International Journal of Computer Sciences and Engineering, 7(6), 641-649.