A Review on Utilizing Bio-Mimetics in Solving Localization Problem in Wireless Sensor Networks

  • R. I. Malar
  • M. ShanmugamEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)


Wireless Sensor Networks (WSNs) have the feasibility to connect the physical world with the virtual world by framing a network of sensors. The supreme function of a sensor network is to collect and forward data to the destination. Applications based on WSNs needs location knowledge about randomly deployed nodes. Localization of these nodes is the basic problem in WSNs. Several types of research have been done so far, using various strategies to improve the network performance as well as energy efficiency and communications effectiveness of WSNs. Among the strategies used, algorithms inspired by natural behaviours of a group of organisms like butterflies, fireflies, grey wolf, etc., showed higher efficiency in locating the nodes. In this survey, some of the inherent nature inspired localization algorithms are briefly discussed. Also, some other collective behaviours which can be used to develop localization algorithms are also explained.


Sensor networks Bio inspired Localization 


  1. 1.
    Kumar, A., Shwe, H.Y., Wong, K.J., Chong, P.H.J.: Location-based routing protocols for wireless sensor networks: A survey. Wireless Sens. Netw. 9, 25–72 (2017)Google Scholar
  2. 2.
    Rashid, B., Rehmani, M.H.: Applications of wireless sensor networks for urban areas: A survey. J. Netw. Comput, Appl. 60, 192–219 (2016)Google Scholar
  3. 3.
    Patwari, N., Ash, J.N., Kyperountas, S., Hero, A.O., Moses, R.L., Corral, N.S.: Locating the nodes: Cooperative localization in wireless sensor networks. IEEE Sig. Process. Mag. 22, 54–69 (2005)Google Scholar
  4. 4.
    Chong, C.-Y., Kumar, S.P.: Sensor networks: Evolution, opportunities, and challenges. Proc. IEEE 91(8), 1247–1256 (2013)Google Scholar
  5. 5.
    Rawat, P., Singh, K.D., Chaouchi, H., Bonnin, J.M.: Wireless sensor networks: A survey on recent developments and potential synergies. J. Super Comput. 68(1), 353–393 (2014)Google Scholar
  6. 6.
    Kuriakose, J., Joshi, S., Vikram Raju, R., Kilaru, A.: A review on localization in wireless sensor networks. In: Thampi, S.M., Gelbukh, A., Mukhopadhyay, J. (eds.) Advances in Signal Processing and Intelligent Recognition Systems. AISC, vol. 264, pp. 599–610. Springer, Cham (2014). Scholar
  7. 7.
    Chaczko, Z., Klempous, R., Nikodem, J., Nikodem, M.: Methods of sensors localization in wireless sensor networks. In: IEEE International Conference and Workshops on the Engineering of Computer-Based Systems, ECBS 2007, Tucson, AZ, pp. 26–29 (2007)Google Scholar
  8. 8.
    Kulkarni, R.V., Venayagamoorthy, G.K., Cheng, M.X.: Bio-inspired node localization in wireless sensor networks. In: IEEE International Conference on Systems, Man and Cybernetics, San Antonio, pp. 205–210 (2009)Google Scholar
  9. 9.
    Arora, S., Singh, S.: Node localization in wireless sensor networks using butterfly optimization algorithm. Arab. J. Sci. Eng. 42(8), 3325–3335 (2017)Google Scholar
  10. 10.
    Arora, S., Singh, S.: An improved butterfly optimization algorithm with chaos. J. Intell. Fuzzy Syst. 32(1), 1079–1088 (2017)zbMATHGoogle Scholar
  11. 11.
    Arora, S., Singh, S.: An effective hybrid butterfly optimization algorithm with artificial bee colony for numerical optimization. Int. J. Interact. Multimed. Artif. Intell 4(4), 14 (2017)Google Scholar
  12. 12.
    Arora, S., Singh, S.: An improved butterfly optimization algorithm for global optimization. Adv. Sci. Eng. Med. 8(9), 711–717 (2016)Google Scholar
  13. 13.
    Arora, S., Singh, S., Yetilmezsoy, K.: A modified butterfly optimization algorithm for mechanical design optimization problems. J. Braz. Soc. Mech. Sci. Eng. 40(1), 21 (2018)Google Scholar
  14. 14.
    Yang, X.-S.: Engineering Optimization An Introduction with Metaheuristic Applications. 2nd edn. John Wiley and Sons INC, Hoboken, New Jersey (2010)Google Scholar
  15. 15.
    Yang, X.-S., He, X.: Firefly algorithm: Recent advances and applications. Int. J. Swarm Intell. 1(1), 36–50 (2013)Google Scholar
  16. 16.
    Gopakumar, A., Jacob, L.: Localization in wireless sensor networks using Particle Swarm Optimization. In: IET International Conference on Wireless Mobile and Multimedia Networks, Beijing, China, pp. 227–230 (2008)Google Scholar
  17. 17.
    Harikrishnan, R., Jawahar Senthil Kumar, V., Sridevi Ponmalar, P.: Firefly algorithm approach for localization in wireless sensor networks. In: Nagar, A., Mohapatra, D.P., Chaki, N. (eds.) Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics. SIST, vol. 44, pp. 209–214. Springer, New Delhi (2016). Scholar
  18. 18.
    Bingnan, P., Zhang, H., Pei, T., Wang, H.: Firefly algorithm optimization based WSN localization algorithm. In: International Conference on Information and Communication Technologies, Xi’an, China, pp. 26–5 (2015)Google Scholar
  19. 19.
    Arora, S., Singh, S.: A conceptual comparison of firefly algorithm, bat algorithm, and cuckoo search. In: International Conference on Control Computing Communication and Materials (ICCCCM), Allahabad, India, pp. 1–4. IEEE (2013)Google Scholar
  20. 20.
    Goyal, S., Patterh, M.S.: Wireless sensor network localization based on BAT algorithm. Int. J. Emerg. Technol. Comput. Appl. Sci. 3(192), 507–512 (2013)Google Scholar
  21. 21.
    Yang, X.-S., He, X.: Bat algorithm: Literature review and applications. Int. J. Bio-Inspired Comput. 5(3), 141–149 (2013)Google Scholar
  22. 22.
    Gandomi, A.H., Yang, X.S.: Chaotic bat algorithm. J. Comput. Sci. 5(2), 224–232 (2014)MathSciNetGoogle Scholar
  23. 23.
    Rezaee Jordehi, A.: Chaotic bat swarm optimization (CBSO). Appln. Soft. Comput. 26, 523–530 (2015)Google Scholar
  24. 24.
    Dao, T.-K., Pan, T.-S., Nguyen, T., Pan, J.-S.: Parallel bat algorithm for optimizing makespan in job scheduling problems. J. Intell. Manuf. 29(2), 451–462 (2015)Google Scholar
  25. 25.
    Jayabarathi, T., Raghunathan, T., Gandomi, A.H.: The bat algorithm, variants and some practical engineering applications: A review. In: Yang, X.-S. (ed.) Nature-Inspired Algorithms and Applied Optimization. SCI, vol. 744, pp. 313–330. Springer, Cham (2018). Scholar
  26. 26.
    Mihoubi, M., Rahmoun, A., Lorenz, P., Lasla, N.: An effective bat algorithm for node localization in a distributed wireless sensor network. Secur. Priv. 1(1), e7 (2018)Google Scholar
  27. 27.
    Goyal, S., Patterh, M.S.: Modified bat algorithm for localization of wireless sensor network. Wireless Pers. Commun. 86(2), 657–670 (2016)Google Scholar
  28. 28.
    Yılmaz, S., Ugur Kucuksille, E., Cengiz, Y.: Modified bat algorithm. Elektronika ir Elektrotechnika 20(2), 71–78 (2014)Google Scholar
  29. 29.
    Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: Proceedings of World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), pp. 210–214. IEEE (2009)Google Scholar
  30. 30.
    Rajabioun, R.: Cuckoo optimization algorithm. Appl. Soft Comput. 11(8), 5508–5518 (2011)Google Scholar
  31. 31.
    Walton, S., Hassan, O., Morgan, K., Rowan Brown, M.: A review of the development and applications of the cuckoo search algorithm. In: Swarm Intelligence and Bio-Inspired Computation Theory and Applications, pp. 257–271 (2013)Google Scholar
  32. 32.
    Walton, S., Hassan, O., Morgan, K., Rowan Brown, M.: Modified cuckoo search: A new gradient-free optimization algorithm chaos. Solitons and Fractals 44(9), 710–718 (2011)Google Scholar
  33. 33.
    Mareli, M., Tawla, B.: An adaptive Cuckoo search algorithm for optimization. Appl. Comput. Inf. 14(2), 107–115 (2018)Google Scholar
  34. 34.
    Marichelvam, M.K., Prabaharan, T., Yang, X.-S.: Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan. Appl. Soft Comput. 19, 93–101 (2014)Google Scholar
  35. 35.
    Marichelvam, M.K.: An improved hybrid Cuckoo Search (IHCS) metaheuristics algorithm for permutation flow shop scheduling problems. Int. J. Bio-Inspired Comput. 4(4), 200–205 (2012)Google Scholar
  36. 36.
    Li, X., Yin, M.: A hybrid cuckoo search via Lévy flights for the permutation flow shop scheduling problem. Int. J. Prod. Res. 51(16), 4732–4754 (2013)Google Scholar
  37. 37.
    Gherboudj, A., Layeb, A., Chikhi, S.: Solving 0–1 knapsack problems by a discrete binary version of the cuckoo search algorithm. Int. J. Bio-Inspired Comput. 4(4), 229–236 (2012)Google Scholar
  38. 38.
    Valian, E., Mohanna, S., Tavakoli, S.: Improved cuckoo search algorithm for feedforward neural network training. Int. J. Artif. Intell. Appl. 2(3), 36–43 (2011)Google Scholar
  39. 39.
    Ouaarab, A., Ahiod., B., Yang, X.-S.: Discrete cuckoo search algorithm for the traveling salesman problem. Neural Computing and Applications 24(7-8), 1659–1669 (2014)Google Scholar
  40. 40.
    Goyal, S., Patterh, M.S.: Wireless sensor network localization based on cuckoo search algorithm. Wireless Pers. Commun. 79(1), 223–234 (2014)Google Scholar
  41. 41.
    Passino, K.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag 22(3), 52–67 (2002)MathSciNetGoogle Scholar
  42. 42.
    Kim, D.H., Abraham, A., Cho, J.H.: A hybrid genetic algorithm and bacterial foraging approach for global optimization. Inf. Sci. 177(18), 3918–3937 (2007)Google Scholar
  43. 43.
    Sathya, P.D., Kayalvizhi, R.: Modified bacterial foraging algorithm based multilevel thresholding for image segmentation. Eng. Appl. Artif. Intell. 24(4), 595–615 (2011)Google Scholar
  44. 44.
    Dasgupta, S., Das, S., Biswas, A., Abraham, A.: Automatic circle detection on digital images with an adaptive bacterial foraging algorithm. Soft Comput. 14(11), 1151–1164 (2011)Google Scholar
  45. 45.
    Kulkarni, R.V., Ganesh Kumar, V.: Bio-inspired algorithms for autonomous deployment and localization of sensor nodes. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(6), 663–675 (2010)Google Scholar
  46. 46.
    Meng, X., Liu, Y., Gao, X., Zhang, H.: A new bio-inspired algorithm: Chicken swarm optimization. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds.) ICSI 2014. LNCS, vol. 8794, pp. 86–94. Springer, Cham (2014). Scholar
  47. 47.
    Wu, D., Kong, F., Gao, W., Shen, Y., Ji, Z.: Improved chicken swarm optimization. In: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Shenyang, China, pp. 681–686. IEEE (2015)Google Scholar
  48. 48.
    Chen, Y.L., He, P.L., Zhang, Y.H.: Combining penalty function with modified chicken swarm optimization for constrained optimization. Adv. Intell. Syst. Res. 126, 1899–1907 (2015)Google Scholar
  49. 49.
    Al Shayokh, M., Shin, S.Y.: Bio-inspired distributed WSN localization based on chicken swarm optimization. Wireless Pers. Commun. 97(4), 5691–5706 (2017)Google Scholar
  50. 50.
    Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)Google Scholar
  51. 51.
    Jitkongchuen, D., Phaidang, P., Pongtawevirat, P.: Grey wolf optimization algorithm with invasion-based migration operation. In: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), Okayama, Japan, pp. 1–5. IEEE (2016)Google Scholar
  52. 52.
    Rajakumar, R., Amudhavel, J., Dhavachelvan, P., Vengattaraman, T.: GWO-LPWSN: Grey wolf optimization algorithm for node localization problem in wireless sensor networks. J. Comput. Netw. Commun. (2017)Google Scholar
  53. 53.
    Chen, G.-C., Yu, J.-S.: Particle swarm optimization algorithm. Inf. Cont.-Shenyang 34, 318 (2005)Google Scholar
  54. 54.
    Schmickl, T., Crailsheim, K.: A navigation algorithm for swarm robotics inspired by slime mold aggregation. In: Şahin, E., Spears, W.M., Winfield, A.F.T. (eds.) SR 2006. LNCS, vol. 4433, pp. 1–13. Springer, Heidelberg (2007). Scholar
  55. 55.
    Nakagaki, T., Yamada, H., Toth, A.: Path finding by tube Morphogenesis in an amoeboid organism. Biophys. Chem. 92(1), 47–52 (2001)Google Scholar
  56. 56.
    Nakagaki, T.: Smart behavior of true slime mold in a labyrinth. Res. Microbiol. 152(9), 767–770 (2001)Google Scholar
  57. 57.
    Li, K., Torres, C.E., Thomas, K., Rossi, L.F., Shen, C.C.: Slime mold inspired routing protocols for wireless sensor networks. Swarm Intell. 5(3–4), 183–223 (2011)Google Scholar
  58. 58.
    Conradt, L., Roper, T.J.: Consensus decision making in animals. Trends Ecol. Evol. 20(8), 449–456 (2005)Google Scholar
  59. 59.
    Pitman, R.L., Durban, J.W.: Cooperative hunting behavior, prey selectivity and prey handling by pack ice killer whales (Orcinus orca), type B, Antarctic Peninsula waters. Marine Mammal Sci. 28(1), 16–36 (2012)Google Scholar
  60. 60.
    Visser, I.N., Smith, T.G., Bullock, I.D., Green, G.D., Carlsson, O.G.L., Imberti, S.: Antarctic peninsula killer whales (Orcinus orca) hunt seals and a penguin on floating ice. Marine Mammal Sci. 24(1), 225–234 (2008)Google Scholar
  61. 61.
    Tarpy, D.R., Gilley, D.C., Seeley, T.D.: Levels of selection in a social insect: A review of conflict and cooperation during honey bee (Apis mellifera) queen replacement. Behav. Ecol. Sociobiol. 55(6), 513–523 (2004)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Vignan’s Foundation for Science, Technology and ResearchGunturIndia

Personalised recommendations