Abstract
Mobile ad hoc network comprises of wireless nodes which are mobile in nature and have short lifespan. They join together to create a self-configured infrastructure-less network where routing is an important challenge. In AODV routing, the hello messages are broadcast periodically by nodes for monitoring the link connectivity to neighbors and for maintaining routing table. The broadcasting of hello messages increases when link failure occurs due to node mobility, which leads to higher consumption of node energy and increases overhead within network. This paper proposes an energy-efficient routing approach (EE-RA), which calculates optimal hello interval for reducing the unnecessary broadcasting of hello messages that further reduces node’s energy consumption and network overhead. This is achieved by using Mamdani-based fuzzy inference system and adaptive neuro-fuzzy inference system (ANFIS) to calculate the resultant optimal hello interval in which energy and mobility of node are taken as inputs. Moreover, simulation results illustrate that the performance of EE-RA outperforms AODV and achieve better results for ANFIS in hello message fraction, network overhead, average energy consumption, packet delivery ratio, end-to-end delay and throughput, especially in highly mobile and dense environment.
Similar content being viewed by others
Abbreviations
- V x, V y and V xy :
-
Speed of node x, y and their relative speed
- P r and P t :
-
Receiving and transmitting power
- (\(X_{N} ,\; Y_{N}\)) and (\(X_{K} , \;Y_{K}\)):
-
Coordinates of node N and its Kth neighbors
- DISN :
-
Sum of distance to its neighbors
- NDISN :
-
Normalized distance value
- NE, NM:
-
Node energy, node mobility
- EC:
-
Node energy consumption
- \(\mu_{{X_{i} }} \;({\text{NE}})\), \(\mu_{{Y_{i} }} \;({\text{NM}})\) :
-
Gaussian type membership function of inputs NE and NM
- c i :
-
Center of the ith fuzzy set Xi
- σ i :
-
Width of the same ith fuzzy set Xi
- \(C_{i}\) :
-
Firing strength
- F i :
-
Consequent parameters
- \({\text{net}}_{{{\text{h}}1}}\) and \({\text{Out}}_{{{\text{h}}1}}\) :
-
Net input and activation function for output in hidden layer neuron
- \({\text{net}}_{\text{output}}\) and \({\text{Out}}_{\text{output}}\) :
-
Net input and activation function for output in output layer
- \(W_{1}^{ + }\) :
-
Updated weight at hidden layer
- \(\eta\) :
-
Learning rate
References
Murthy, S.R., Manoj, B.S.: Ad-Hoc Wireless Networks: Architecture and Protocols. Pearson Ltd, Prentice-hall, Upper saddle river, NJ (2004)
Chlamtac, I., Contj, M., Liu, J.: Mobile ad hoc networking: imperatives and challenges. Ad Hoc Netw. 1(1), 13–64 (2003)
Wang, Y.: Study on energy conservation in MANET. J. Netw. 5(6), 708–715 (2010)
Dua, A., Kumar, N., Bawa, S.: A systematic review on routing protocols for vehicular ad hoc networks. Veh. Commun. 1, 33–52 (2014)
Mueller, S., Tsang, R.P., Ghosal, D.: Multipath routing in mobile ad hoc networks: issues and challenges. In: Calzarossa, M.C., Gelenbe, E. (eds.) Performance Tools and Applications to Networked Systems, pp. 209–234. Springer, Berlin (2004)
Shangchao, Pi, Baolin, S.: Fuzzy controllers based multipath routing algorithm in MANET. Phys. Procedia 24, 1178–1185 (2012)
Wang, C., Chen, S., Yang, X., Gao, Y.: Fuzzy logic-based dynamic routing management policies for mobile adhoc networks. In: HPSR. 2005 Workshop on High Performance Switching and Routing, 2005, pp. 341–345 (2005). https://doi.org/10.1109/HPSR.2005.1503251
Gupta, A., Bisen, D.: Review of different routing protocols in mobile ad-hoc networks. Int. J. Comput. Sci. Eng. (IJCSE) 3(3), 105–112 (2015)
Jain, J., Gupta, R., Bandhopadhyay, T.K.: Performance analysis of proposed local link repair schemes for ad hoc on demand distance vector. IET Netw. 3(2), 129–136 (2014)
Oliveira, R., Luis, M., Bernardo, L., Dinis, R., Pinto, P.: The impact of node’s mobility on link-detection based on routing hello messages. In: IEEE Wireless Communication and Networking Conference, pp. 1–6 (2010). https://doi.org/10.1109/WCNC.2010.5506529
Karia, D.C., Godbole, V.V.: New approach for routing in mobile ad-hoc networks based on ant colony optimisation with global positioning system. IET Netw. 2(3), 171–180 (2013)
Zhao, Q., Tong, L., Counsil, D.: Energy-aware adaptive routing for large-scale adhoc networks: protocol and performance analysis. IEEE Trans. Mobile Comput. 6(9), 1048–1059 (2007)
Thakur, N., Bisen, D., Gupta, N.: Proposed agent based black hole node detection algorithm for ad-hoc wireless network. Int. J. Comput. Sci. Appl. 5(2), 69–85 (2015)
Hayajna, T., Kadoch, M.: Analysis and enhancements of HELLO based link failure detection in wireless mesh networks. Telecommun. Syst. (2017). https://doi.org/10.1007/s11235-017-0293-4
Perkins, C.E., Royer, E.: Ad hoc on-demand distance vector routing (AODV). In: 2003, IETF RFC 3561
Jain, J., Gupta, R., Bandhopadhyay, T.K.: Scalability enhancement of AODV using local link repairing. Int. J. Electron. 101(9), 1230–1243 (2014)
Ravi, G., Kashwan, K.R.: A new routing protocol for energy efficient mobile applications for ad-hoc networks. Comput. Electr. Eng. 48, 77–85 (2015)
Rajeswari, S., Venkataramani, Y.: Adaptive energy conserve routing protocol for mobile ad hoc networks. WSEAS Trans. Commun. 11(12), 464–475 (2012)
Das, S., Tripathi, S.: Energy efficient routing protocol for MANET based on vague set measurement technique. Procedia Comput. Sci. 58, 348–355 (2015)
Chettibi, S., Chikhi, S.: FEA-OLSR: an adaptive energy aware routing protocol for MANETs using zero-order Sugeno fuzzy system. Int. J. Comput. Sci. 10, 136–141 (2013)
Chettibi, S., Chikhi, S.: Dynamic fuzzy logic and reinforcement learning for adaptive energy efficient routing in mobile ad-hoc networks. Appl. Soft Comput. 38, 321–328 (2016)
Naruephiphat, W., Usaha, W.: Balancing tradeoffs for energy efficient routing in MANETs based on reinforcement learning. In: Proceedings of the 67th IEEE Vehicular Technology Conference, pp. 2361–2365 (2008)
Torshiz, M.N., Amintoosi, H., Movaghar A.: A fuzzy energy-based extension to AODV routing. In: Telecommunications, IST, International Symposium on, Tehran, pp. 371–375 (2008)
Tabatabaei, S., Teshnehlab, M., Mirabedini, S.J.: Fuzzy-based routing protocol to increase throughput in mobile ad hoc networks. Wireless Pers. Commun. 84(4), 2307–2325 (2015)
Tabatabaei, S., Hosseini, F.: A fuzzy logic-based fault tolerance new routing protocol in mobile ad hoc networks. Int. J. Fuzzy Syst 18, 883 (2016). https://doi.org/10.1007/s40815-015-0119-z
Bisen, D., Sharma, S.: Fuzzy based detection of malicious activity for security assessment of MANET. Natl. Acad. Sci. Lett. (2017). https://doi.org/10.1007/s40009-017-0602-1
Bisen, D., Sharma, S.: An enhanced performance through agent based secure approach for mobile ad-hoc networks. Int. J. Electron. 105(1), 116–136 (2018)
Kar, S., Das, S., Ghosh, P.K.: Applications of neuro fuzzy systems: A brief review and future outline. Appl. Soft Comput. 15, 243–259 (2014)
Chai, Y., Jia, L., Zhang, Z.: Mamdani model based adaptive neural fuzzy inference system and its application. World Acad. Sci. Eng. Technol. 3(3), 663–670 (2009)
Han, S.Y., Lee, D.: An adaptive hello messaging scheme for neighbor discovery in on-demand MANET routing protocols. IEEE Commun. Lett. 17(5), 1040–1043 (2013)
Harrag, N., Refoufi, A. and Harrag, A.: Neighbor discovery using Novel DE-based adaptive hello messaging scheme improving OLSR routing protocol performances. In: Proceedings of the 6th International Conference on Systems and Control, University of Batna 2, Batna, Algeria, May 7–9, pp. 308–312 (2017)
Park, N.U., Nam, J.C., Cho, Y.Z.: Impact of node speed and transmission range on the hello interval of MANET routing protocols. In: ICTC-2016, pp. 634–636
Soleymani, S.A., Abdullah, A.H., Anisi, M.H., et al.: BRAIN-F: beacon rate adaption based on fuzzy logic in vehicular ad hoc network. Int. J. Fuzzy Syst. 19, 301 (2017). https://doi.org/10.1007/s40815-016-0171-3
Sumathia, K., Priyadharshinib, A.: Energy optimization in MANETS using on-demand routing protocol. Procedia Comput. Sci. 47, 460–470 (2015)
Kaur, K., Pawar, L.: Optimization of hello messaging scheme in MANET on-demand routing protocol using PSO. Int. J. Comput. Sci. Netw. 4(4), 554–558 (2015)
Godo, L., Gottwald, S.: Fuzzy sets and formal logics. Fuzzy Sets Syst. 281, 44–60 (2015)
Mendel, J.M.: Uncertain Rule-Based Fuzzy Systems: Introduction and New Directions, 2nd edn. Springer, New York (2017)
The Network Simulator ns-2, Information Sciences Institute, USA. Viterbi School of Engineering, 2004 September. Retrieved from http://www.isi.eu/nsnam/ns/
Mathworks, Fuzzy Logic Toolbox: User’s Guide (R2018a), 2018 January. Retrieved from http://in.mathworks.com/help/fuzzy/index.html
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Bisen, D., Sharma, S. An Energy-Efficient Routing Approach for Performance Enhancement of MANET Through Adaptive Neuro-Fuzzy Inference System. Int. J. Fuzzy Syst. 20, 2693–2708 (2018). https://doi.org/10.1007/s40815-018-0529-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40815-018-0529-9