Top

Wireless Networks

Published in:

02-07-2019

Ant-based efficient energy and balanced load routing approach for optimal path convergence in MANET

Authors: Arockiasamy Karmel, Varadarajan Vijayakumar, Radhakrishnan Kapilan

Published in: Wireless Networks | Issue 8/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Ant colony optimization, a swarm intelligence technique, inspired by the foraging behavior of ants in colonies was used in the past research works to compute the optimal path. The existing works of routing using ant colony optimization of MANETS face challenges in load balancing and energy efficiency. The proposed A-EEBLR approach chooses the next hop node based on metrics like delay, energy drain rate, congestion, link quality. Based on these metrics the probability of choosing next hop node as neighbor node is determined. The next hop probability determines the forward and backward ant agents to establish multiple paths among which the most optimal path is selected for transmission. The implementation results shows that the proposed A-EEBLR approach outperforms the existing A-ESR approach when evaluated by varying the number of packets, number of nodes and node mobility.

previous article Reduced carbon emission and optimized power consumption technique using container over virtual machine

next article An optimization framework for routing protocols in VANETs: a multi-objective firefly algorithm approach

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Zhu, J., & Wang, X. (2011). Model and protocol for energy-efficient routing over mobile ad hoc networks. IEEE Transaction on Mobile Computing, 10(11), 1546–1557.CrossRef

Dorigo, M. (1992). Optimization, learning and natural algorithms (in Italian). Ph.D. thesis, DEI, Politecnico di Milano, Italy.

Dorigo, M., & Di Caro, G. (1999). The ant colony optimization meta-heuristic. New ideas in optimization (pp. 11–32). New York: McGraw-Hill.

Dorigo, M., Di Caro, G., & Gambardella, L. M. (1999). Ant algorithms for discrete optimization. Artificial Life, 5(2), 137–172.CrossRef

Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: A cooperative learning approach to the travelling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53–66.CrossRef

Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics-Part B, 26(1), 29–41.CrossRef

Dorigo, M., & Stutzle, T. (2002). The ant colony optimization metaheuristic: Algorithms, applications and advances. International Series in Operations Research & Management Science, 57, 251–285.MathSciNetMATH

Dorigo, M., & Stutzle, T. (2004). Ant colony optimization. Boston, MA: MIT Press.CrossRef

Eric, B., Florian, H., Sylvain, G., et al. (1999). Routing in telecommunications networks with’smart’ ant-like agents. In Proceedings of the 2nd international workshop on intelligent agents for telecommunication applications, Paris, France.

10.

Gudakahriz, S. J., Jamali, S., & Zeinali, E. (2011). NISR: A nature inspired scalable routing protocol for mobile ad hoc networks. International Journal of Computer Science Engineering and Technology, 1(4), 180–194.

11.

Pavani, G. S., Zuliani, L. G., Waldman, H., & Magalhaes, M. (2008). Distributed approaches for impairment aware routing and wavelength assignment algorithms in GMPLS networks. Computer Networks, 52, 1905–1915.CrossRef

12.

Paramasiven, A. (2011). Using swarm intelligence to optimize caching techniques for ad hoc network. International Journal of Computer Science and Telecommunications, 2(6), 15–19.

13.

Wankhade, S. B., & Ali, M. S. (2011). Ant based techniques for qos routing in mobile ad hoc network: An overview. International Journal of Advanced Networking and Applications, 3(2), 1094–1107.

14.

Pankajavalli, P. B., & Arumugam, N. (2011). BADSR: An enhanced dynamic source routing algorithm for MANETS based on ant and bee colony optimization. European Journal of Scientific Research, 53(4), 576–581.

15.

Yan, J., Yan, L., Minai, A. A., & Polycarpou, M. M. (2006). Balancing search and target response in cooperative unmanned aerial vehicle (UAV) teams. IEEE Transactions systems, MAN and Cybernetics-Part-B: Cybernetics, 36(3), 571–587.CrossRef

16.

Al-Zurba, H., Landolsi, T., Hassan, M., & Abdelaziz, F. (2011). On the suit- ability of using ant colony optimization for routing multimedia content over wireless sensor networks. International Journal on Applications of Graph Theory in Wireless Ad Hoc Networks and Sensor Networks, 3(2), 15–35.CrossRef

17.

Roy, B., Banik, S., Dey, P., Sanyal, S., & Chaki, N. (2011). Ant colony based routing for mobile ad-hoc networks towards improved quality of services. Journal of Emerging Trends in Computing and Information Sciences, 3(1), 10–24.

18.

Poojary, M., & Renuka, B. (2011). Ant colony optimization routing to mobile ad hoc networks in urban environments. International Journal of Computer Science and Information Technologies, 2(6), 2776–2779.

19.

De Rango, F., & Tropea, M. (2009). Energy saving and load balancing in wireless ad hoc networks through ant-based routing. In International symposium on performance evaluation of computer & telecommunication systems (Vol. 41, pp. 117–124).

20.

Kaur, R., Dhillon, R. S., Sohal, H. S., & Gill, A. S. (2010). Load balancing of ant based algorithm in MANET. IJCST, 1(2), 173–178.

21.

Kim, Y.-M., Lee, E.-J., & Park, H.-S. (2011). Ant colony optimization based energy saving routing for energy-efficient networks. IEEE Communications Letters, 15(7), 779–781.CrossRef

22.

Dorigo, M., Di Caro, G., & Gambardella, L. M. (1999). Ant algorithms for discrete optimization. Artificial Life, 5(2), 137–172.CrossRef

23.

Visu, P., Koteeswaran, S., & Janet, J. (2012). Artificial bee colony based energy aware and energy efficient routing protocol. Journal of Computer Science, 8(2), 227.CrossRef

24.

Babu, K. A., Rao, D. S., & Lakshminarayana, S. (2013). Swarm intelligence based energy efficient routing protocol for wireless ad-hoc networks. International Journal of Computer Applications, 62(2), 34–39.CrossRef

25.

Li, K.-H., Leu, J.-S., & Hosek, J. (2013). Ant-based on-demand clustering routing protocol for mobile ad-hoc networks. In Seventh international conference on innovative mobile and internet services in ubiquitous computing (pp. 354–359).

26.

Ren, J., Tu, Y., Zhang, M., & Jiang, Y. (2011). An ant-based energy-aware routing protocol for ad hoc networks. In International conference on computer science and service system (CSSS) (pp. 3844–3849).

27.

Abkenar, G. S., Dana, A., & Shokouhifar, M. (2011). Weighted probability ant-based routing (WPAR) in mobile ad hoc networks. In 24th Canadian conference on electrical and computer engineering (pp. 826–831).

28.

Li, L., & Yang, G. (2010). Ant-colony optimization based on cluster routing protocol of ad hoc. In 2nd international conference on computer engineering and technology (Vol. 1, pp. 304–308).

29.

Joardar, S., Bhattacherjee, V., & Giri, D. (2012). A swarm inspired multipath data with congestion control in MANETS using probabilistic approach. International Journal of Wireless & Mobile Networks, 4(4), 109–121.CrossRef

30.

Wang, Y., Sony, M., Wei, Y., Wang, Y., & Wang, X. (2014). Improved ant colony-based multi-constrained QoS energy-saving routing and throughput optimization in wireless Ad hoc networks. The Journal of China Universities of Posts and Telecommunications, 21(1), 43–53.CrossRef

31.

Singh, G., Kumar, N., & Verma, A. K. (2014). ANTALG: An innovative ACO-based routing algorithm for MANETs. Journal of Network and Computer Applications, 45, 151–167.CrossRef

32.

Sandeep, J., & Satheesh Kumar, J. (2015). Efficient packet transmission and energy optimization in military operation scenarios of MANET. Procedia Computer Science, 47, 400–407.CrossRef

33.

Vallikannu, R., & George, A. (2015). Performance analysis of autonomous location-based energy efficient ACO routing protocols with dissimilar MANET mobility models. ARPN Journal of Engineering and Applied Sciences, 10(4), 1804–1809.

Title: Ant-based efficient energy and balanced load routing approach for optimal path convergence in MANET
Authors: Arockiasamy Karmel
Varadarajan Vijayakumar
Radhakrishnan Kapilan
Publication date: 02-07-2019
Publisher: Springer US
Published in: Wireless Networks / Issue 8/2021
Print ISSN: 1022-0038
Electronic ISSN: 1572-8196
DOI: https://doi.org/10.1007/s11276-019-02080-w

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 8/2021

Machine learning-based physical layer security: techniques, open challenges, and applications

Ensuring reliability of high-priority data transport using expected congestion shortfall prediction in wireless sensor networks

Optimization control method for industrial Internet of Things based on biological adaptive coevolutionary

Modified RRP scheme for interference management in OFDMA based heterogeneous networks

Adaptive regressive holt–winters workload prediction and firefly optimized lottery scheduling for load balancing in cloud

A semantic graph-based keyword extraction model using ranking method on big social data