Wireless Networks

, Volume 24, Issue 4, pp 1139–1159 | Cite as

Intelligent energy-aware efficient routing for MANET

  • Santosh Kumar Das
  • Sachin Tripathi


Designing an energy efficient routing protocol is one of the main issue of Mobile Ad-hoc Networks (MANETs). It is challenging task to provide energy efficient routes because MANET is dynamic and mobile nodes are fitted with limited capacity of batteries. The high mobility of nodes results in quick changes in the routes, thus requiring some mechanism for determining efficient routes. In this paper, an Intelligent Energy-aware Efficient Routing protocol for MANET (IE2R) is proposed. In IE2R, Multi Criteria Decision Making (MCDM) technique is used based on entropy and Preference Ranking Organization METHod for Enrichment of Evaluations-II (PROMETHEE-II) method to determine efficient route. MCDM technique combines with an intelligent method, namely, Intuitionistic Fuzzy Soft Set (IFSS) which reduces uncertainty related to the mobile node and offers energy efficient route. The proposed protocol is simulated using the NS-2 simulator. The performance of the proposed protocol is compared with the existing routing protocols, and the results obtained outperforms existing protocols in terms of several network metrics.


Mobile ad-hoc network Multi-criteria decision making Soft set Fuzzy soft sets Intuitionistic fuzzy soft sets 



The authors would like to thank the associate editor and the anonymous reviewers for their insightful comments and suggestions that helped us to improve the content of this paper.


  1. 1.
    Ali, H., Shahzad, W., & Khan, F. A. (2012). Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization. Applied Soft Computing, 12(7), 1913–1928.CrossRefGoogle Scholar
  2. 2.
    Cho, J.-H., Chen, R., & Chan, K. S. (2016). Trust threshold based public key management in mobile ad hoc networks. Ad Hoc Networks, 44, 58–75.CrossRefGoogle Scholar
  3. 3.
    Priya, K. S., Revathi, T., Muneeswaran, K., & Vijayalakshmi, K. (2015). Heuristic routing with bandwidth and energy constraints in sensor networks. Applied Soft Computing, 29, 12–25.CrossRefGoogle Scholar
  4. 4.
    Yadav, A. K., & Tripathi, S. (2016). Qmrprns: Design of qos multicast routing protocol using reliable node selection scheme for manets. Peer-to-Peer Networking and Applications. doi: 10.1007/s12083-016-0441-8.
  5. 5.
    Verdone, R., Dardari, D., Mazzini, G., & Conti, A. (2010). Wireless sensor and actuator networks: Technologies, analysis and design. London: Academic Press.Google Scholar
  6. 6.
    Das, S. K., Kumar, A., Das, B., & Burnwal, A. P. (2013). Ethics of e-commerce in information and communications technologies. International Journal of Advanced Computer Research, 3(1), 122–124.Google Scholar
  7. 7.
    Dardari, D., Conti, A., Buratti, C., & Verdone, R. (2007). Mathematical evaluation of environmental monitoring estimation error through energy-efficient wireless sensor networks. IEEE Transactions on Mobile Computing, 6(7), 790–802.CrossRefGoogle Scholar
  8. 8.
    Das, S. K., Kumar, A., Das, B., & Burnwal, A. P. (2013). Ethics of reducing power consumption in wireless sensor networks using soft computing techniques. International Journal of Advanced Computer Research, 3, 301–304.Google Scholar
  9. 9.
    Yadav, A. K., & Tripathi, S. (2015). Dlbmrp: Design of load balanced multicast routing protocol for wireless mobile ad-hoc network. Wireless Personal Communications, 85(4), 1815–1829.CrossRefGoogle Scholar
  10. 10.
    Ilgin, M. A., Gupta, S. M., & Battaïa, O. (2015). Use of MCDM techniques in environmentally conscious manufacturing and product recovery. Journal of Manufacturing Systems, 37, 746–758.CrossRefGoogle Scholar
  11. 11.
    Lu, A., & Ng, W. (2005). Vague sets or intuitionistic fuzzy sets for handling vague data: Which one is better? In International conference on conceptual modeling, pp. 401–416. Berlin: Springer.Google Scholar
  12. 12.
    Das, S. K., Tripathi, S., & Burnwal, A. P. (2015). Fuzzy based energy efficient multicast routing for ad-hoc network. In 2015 third international conference on computer, communication, control and information technology (C3IT), pp. 1–5. New York: IEEE.Google Scholar
  13. 13.
    Das, S. K., Tripathi, S., & Burnwal, A. P. (2015). Design of fuzzy based intelligent energy efficient routing protocol for wanet. In 2015 Third international conference on computer, communication, control and information technology (C3IT), pp. 1–4. New York: IEEE.Google Scholar
  14. 14.
    Das, S K., Tripathi, S., & Burnwal, A. P. (2015). Intelligent energy competency multipath routing in wanet. In Information systems design and intelligent applications, pp. 535–543. Berlin: Springer.Google Scholar
  15. 15.
    Zeshui, X., & Zhao, N. (2016). Information fusion for intuitionistic fuzzy decision making: An overview. Information Fusion, 28, 10–23.CrossRefGoogle Scholar
  16. 16.
    Sridhar, S., Baskaran, R., & Chandrasekar, P. (2013). Energy supported AODV (EN-AODV) for QoS routing in MANET. Procedia-Social and Behavioral Sciences, 73, 294–301.CrossRefGoogle Scholar
  17. 17.
    Chao, G., & Zhu, Q. (2014). An energy-aware routing protocol for mobile ad hoc networks based on route energy comprehensive index. Wireless Personal Communications, 79(2), 1557–1570.CrossRefGoogle Scholar
  18. 18.
    Lou, C., & Zhuang, W. (2015). Energy-efficient routing over coordinated sleep scheduling in wireless ad hoc networks. Peer-to-peer networking and applications, pp. 1–13.Google Scholar
  19. 19.
    Ravi, G., & Kashwan, K. R. (2015). A new routing protocol for energy efficient mobile applications for ad hoc networks. Computers & Electrical Engineering, 48, 77–85.CrossRefGoogle Scholar
  20. 20.
    Abirami, S., Bhanumathi, V., & Dhanasekaran, R. (2012). A balanced approach for power aware routing in MANET using fuzzy logic. In IJCA proceedings on international conference in recent trends in computational methods, communication and controls (ICON3C 2012), no. 5. Foundation of Computer Science (FCS).Google Scholar
  21. 21.
    Hiremath, P. S., & Joshi, S. M. (2012). Energy efficient routing protocol with adaptive fuzzy threshold energy for manets. In International Journal of Computer Networks and Wireless Communications (IJCNWC), Vol. 2. ISSN: 2250-3501.Google Scholar
  22. 22.
    Chettibi, S., & Chikhi, S. (2013). FEA-OLSR: An adaptive energy aware routing protocol for manets using zero-order sugeno fuzzy system. International Journal of Computer Science Issues (IJCSI), 10(2), 136–141.Google Scholar
  23. 23.
    Chettibi, S., & Chikhi, S. (2016). Dynamic fuzzy logic and reinforcement learning for adaptive energy efficient routing in mobile ad-hoc networks. Applied Soft Computing, 38, 321–328.CrossRefGoogle Scholar
  24. 24.
    Carvalho, T., Júnior, J. J., & Francês, R. (2016). A new cross-layer routing with energy awareness in hybrid mobile ad hoc networks: A fuzzy-based mechanism. Simulation Modelling Practice and Theory, 63, 1–22.CrossRefGoogle Scholar
  25. 25.
    Sarkar, S., & Datta, R. (2016). A secure and energy-efficient stochastic multipath routing for self-organized mobile ad hoc networks. Ad Hoc Networks, 37, 209–227.CrossRefGoogle Scholar
  26. 26.
    Das, S. K., Tripathi, S. (2016) Energy efficient routing protocol for MANET using vague set. In Proceedings of fifth international conference on soft computing for problem solving, pp. 235–245. Berlin: Springer.Google Scholar
  27. 27.
    Zimmermann, H. J. (1991) Fuzzy set theory and its applications. Boston: Kluwer Academic Publishers.Google Scholar
  28. 28.
    Das, S. K., & Tripathi, S. (2015). Energy efficient routing protocol for MANET based on vague set measurement technique. Procedia Computer Science, 58, 348–355.Google Scholar
  29. 29.
    Das, S. K., Kumar, A., Das, B., & Burnwal, A. P. (2013). On soft computing techniques in various areas. Intenational Journal of Informational Technology and Computer Science, 3, 59–68.Google Scholar
  30. 30.
    Das, S. K., Tripathi, S., & Burnwal, A. P. (2014). Some relevance fields of soft computing methodology. International Journal of Research in Computer Applications and Robotics, 2, 1–6.Google Scholar
  31. 31.
    Bhawsar, Y., & Thakur, G. S. (2016). Performance evaluation of link prediction techniques based on fuzzy soft set and markov model. Fuzzy Information and Engineering, 8(1), 113–126.MathSciNetCrossRefGoogle Scholar
  32. 32.
    Alcantud, J. C. R. (2016). A novel algorithm for fuzzy soft set based decision making from multiobserver input parameter data set. Information Fusion, 29, 142–148.CrossRefGoogle Scholar
  33. 33.
    Muthukumar, P., & Krishnan, G. S. S. (2016). A similarity measure of intuitionistic fuzzy soft sets and its application in medical diagnosis. Applied Soft Computing, 41, 148–156.CrossRefGoogle Scholar
  34. 34.
    Bartoletti, S., Dai, W., Conti, A., & Win, M. Z. (2015). A mathematical model for wideband ranging. IEEE Journal of Selected Topics in Signal Processing, 9(2), 216–228.CrossRefGoogle Scholar
  35. 35.
    Win, M. Z., Conti, A., Mazuelas, S., Shen, Y., Gifford, W. M., Dardari, D., et al. (2011). Network localization and navigation via cooperation. IEEE Communications Magazine, 49(5), 56–62.CrossRefGoogle Scholar
  36. 36.
    Tseng, Y.-C., Ni, S.-Y., Chen, Y.-S., & Sheu, J.-P. (2002). The broadcast storm problem in a mobile ad hoc network. Wireless Networks, 8(2–3), 153–167.CrossRefzbMATHGoogle Scholar
  37. 37.
    Conti, A., Panchenko, D., Sidenko, S., & Tralli, V. (2009). Log-concavity property of the error probability with application to local bounds for wireless communications. IEEE Transactions on Information Theory, 55(6), 2766–2775.MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    WANG, Y., Mei, S. O. N. G., 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), 43–59.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology (ISM)DhanbadIndia

Personalised recommendations