ACO optimized self-organized tree-based energy balance algorithm for wireless sensor network

  • Vishal Kumar AroraEmail author
  • Vishal Sharma
  • Monika Sachdeva
Original Research


Energy-efficient routing algorithms must handle power-limitation issue of the sensor nodes intelligently to prolong the network life of wireless networks. Accordingly, it is indispensable to collect and exchange the sensor data in an optimized way to reduce energy consumption. Subsequently, an ACO Optimized Self-Organized Tree-Based (AOSTEB) Energy Balance Algorithm for Wireless Sensor Network has been proposed that discovers an efficient route during intra-cluster communication. AOSTEB scheme operates in three phases: cluster-formation, multi-path creation, and data transmission. During cluster-formation, the desired number of sensor nodes are alleviated to the role of cluster-heads (CHs), and the remaining neighboring sensor nodes join the nearest CHs to form a cluster. Further, the multiple paths between the CH and member nodes are discovered using Ant Colony Optimization algorithm. A dynamic energy efficient optimized route is selected within a specific cluster on account of shortest distance and less energy-consumption to initiate the data exchange process within the cluster. The extensive simulation observations ascertain the efficiency of the proposed algorithm by demonstrating the prolonged network lifetime, enhanced stability period, and reduced energy consumption in contrast to the earlier reported works in wireless sensor networks.


Wireless sensor network Energy efficient routing Ant colony optimization 



The authors of the work highly acknowledge the contribution of I.K.G Punjab Technical University, Kapurthala, Punjab, India.


This study is not funded from any grant.

Compliance with ethical standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical approval

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


  1. Dorigo M, Birattari M (1999) Ant colony optimization. In: Encyclopedia of machine learning. Springer, pp 36–39Google Scholar
  2. Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39CrossRefGoogle Scholar
  3. Fei Z, Li B, Yang S, Xing C, Chen H, Hanzo L (2016) A survey of multi-objective optimization in wireless sensor networks: metrics, algorithms and open problems. IEEE Commun Surv Tutor 19(1):550–586CrossRefGoogle Scholar
  4. Han Z, Wu J, Zhang J, Liu L, Tian K (2014) A general self-organized tree-based energy-balance routing protocol for wireless sensor network. IEEE Trans Nucl Sci 61(2):732–740CrossRefGoogle Scholar
  5. Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd annual Hawaii international conference on system sciences (HICSS). IEEE, pp 1–10Google Scholar
  6. Heinzelman WR, Chandrakasan A, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670CrossRefGoogle Scholar
  7. Khan MY, Javaid N, Khan MA, Javaid A, Khan ZA, Qasim U (2013) Hybrid DEEC: towards efficient energy utilization in wireless sensor networks. arXiv preprint. arXiv:1303.4679Google Scholar
  8. Kim KT, Lyu CH, Moon SS, Youn HY (2010) Tree-based clustering (TBC) for energy efficient wireless sensor networks. In: Advanced information networking and applications workshops (WAINA), 2010 IEEE 24th international conference on WAINA. IEEE, pp 680–685Google Scholar
  9. Kim J-Y, Sharma T, Kumar B, Tomar GS, Berry K, Lee WH (2014) Intercluster ant colony optimization algorithm for wireless sensor network in dense environment. Int J Distrib Sens Netw 10(4):402–457Google Scholar
  10. Lindsey S, Raghavendra CS (2002) PEGASIS: power-efficient gathering in sensor information systems. In: Aerospace conference proceedings (ACP). IEEE, pp 1125–1130Google Scholar
  11. Liu X, He D (2014) Ant colony optimization with greedy migration mechanism for node deployment in wireless sensor networks. J Netw Comput Appl 34:310–318CrossRefGoogle Scholar
  12. Mehmood A, Khan S, Shams B, Lloret J (2015) Energy-efficient multi-level and distance-aware clustering mechanism for WSNS. Int J Commun Syst 28(5):972–989CrossRefGoogle Scholar
  13. Pantazis NA, Nikolidakis SA, Vergados DD (2013) Energy-efficient routing protocols in wireless sensor networks: a survey. IEEE Commun Surv Tutor 15(2):551–591CrossRefGoogle Scholar
  14. Qing L, Zhu Q, Wang M (2006) Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Comput Commun 29(12):2230–2237CrossRefGoogle Scholar
  15. Sabet M, Naji H (2016) An energy efficient multi-level route-aware clustering algorithm for wireless sensor networks: a self-organized approach. Comput Electr Eng 56:399–416CrossRefGoogle Scholar
  16. Saini P, Sharma AK (2010a) Energy efficient scheme for clustering protocol prolonging the lifetime of heterogeneous wireless sensor networks. Int J Comput Appl 6(2):30–36Google Scholar
  17. Saini P, Sharma AK (2010b) E-DEEC-enhanced distributed energy efficient clustering scheme for heterogeneous WSN. In: Parallel distributed and grid computing (PDGC), pp 205–210Google Scholar
  18. Sajwan M, Gosain D, Sharma AK (2018) Hybrid energy-efficient multi-path routing for wireless sensor networks. Comput Electr Eng 67:96–113CrossRefGoogle Scholar
  19. Satapathy SS, Sarma N (2006) TREEPSI: tree based energy efficient protocol for sensor information. In: International conference on wireless and optical communications networks, 2006, pp 1–4Google Scholar
  20. Sert SA, Bagci H, Yazici A (2015) MOFCA: multi-objective fuzzy clustering algorithm for wireless sensor networks. Soft Comput 30:151–165CrossRefGoogle Scholar
  21. Sun Y, Dong W, Chen Y (2017) An improved routing algorithm based on ant colony optimization in wireless sensor networks. IEEE Commun Lett 21(6):1317–1320CrossRefGoogle Scholar
  22. Tan HO, Korpeoglu I, Stojmenovi I (2011) Computing localized power-efficient data aggregation trees for sensor networks. IEEE Trans Parallel Distrib Syst 22(3):489–500CrossRefGoogle Scholar
  23. Wang H, Chen Y, Dong S (2016) Research on efficient-efficient routing protocol for WSNs based on improved artificial bee colony algorithm. IET Wirel Sens Syst 7(1):15–20CrossRefGoogle Scholar
  24. Ye Z, Mohamadian H (2014) Adaptive clustering based dynamic routing of wireless sensor networks via generalized ant colony optimization. IERI Procedia 10:2–10CrossRefGoogle Scholar
  25. Yetgin H, Cheung KTK, Mohammed H, Lajos H (2017) A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Commun Surv Tutor 19(2):828–854CrossRefGoogle Scholar
  26. Younis O, Fahmy S (2004) Heed: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput 3(4):366–379CrossRefGoogle Scholar
  27. Zahedi ZM, Akbari R, Shokouhifar M, Safaei F, Jalali A (2016) Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks. Expert Syst Appl 55:313–328CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Vishal Kumar Arora
    • 1
  • Vishal Sharma
    • 2
  • Monika Sachdeva
    • 3
  1. 1.IKG PTUKapurthalaIndia
  2. 2.Department of Electronics and EngineeringShaheed Bhagat Singh State Technical CampusFerozepurIndia
  3. 3.Computer Science and Engineering DepartmentIKG PTUKapurthalaIndia

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