Predator–prey optimization based clustering algorithm for wireless sensor networks


Grouping the sensor nodes into clusters is an effective way to organize wireless sensor networks and to prolong the networks’ lifetime. This paper presents a static clustering algorithm that employs predator–prey optimization (PPO) for identifying cluster heads as well as routes for sending data to the sink. The objective of the optimization algorithm is to reduce the energy consumed in data collection and transmission, to achieve equalization in energy utilization by the wireless sensor nodes and to prolong the wireless sensor network lifetime while avoiding the expenses of cluster reformation in each communication round. The novelty of this algorithm is to treat the identification of cluster heads and the choice of transmission paths a unified optimization problem of minimizing the total energy cost of the network, whereas existing algorithms consider them two separate optimization sub-problems. PPO algorithm is applied to select the most appropriate pair of cluster heads for each cluster. It also identifies the optimum communication path, which can be single or multiple hop. The energy consumed in data transmission is reduced and a uniformity in residual energy of the nodes is achieved. The performance of the novel algorithm has been evaluated by observing the patterns in which nodes consume their energies. The number of packets that are successfully delivered has been found to be better than the existing static clustering algorithms, and at par with the finest dynamic clustering algorithms.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15


  1. 1.

    Sohraby K, Minoli D, Znati T (2007) Wireless sensor networks—technology, protocols, and applications. Wiley, New Jersey

    Google Scholar 

  2. 2.

    Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52:2292–2330.

    Article  Google Scholar 

  3. 3.

    Zheng J, Jamalipour A (eds) (2008) Wireless sensor networks: a networking perspective. Wiley, New Jersey

    Google Scholar 

  4. 4.

    Mainetti L, Patrono L, Vilei A (2011) Evolution of wireless sensor networks towards the Internet of Things: a survey. In: International conference on software, telecommunications and computer networks (SoftCOM), Croatia, pp 1–6

  5. 5.

    Zhao J, Xi W, He Y et al (2013) Localization of wireless sensor networks in the wild: pursuit of ranging quality. IEEE/ACM Trans Netw 21(1):311–323.

    Article  Google Scholar 

  6. 6.

    Martin I, O’Farrell T, Aspey R et al (2014) A high-resolution sensor network for monitoring glacier dynamics. IEEE Sens J 14(11):3926–3931.

    Article  Google Scholar 

  7. 7.

    Gruden M, Jobs M, Rydberg A (2014) Empirical tests of wireless sensor network in jet engine including characterization of radio wave propagation and fading. IEEE Antennas Wirel Propag Lett 13:762–765.

    Article  Google Scholar 

  8. 8.

    Bhuiyan MZA, Wang G, Cao J, Wu J (2015) Deploying wireless sensor networks with fault-tolerance for structural health monitoring. IEEE Trans Comput 64(2):382–395.

    MathSciNet  Article  MATH  Google Scholar 

  9. 9.

    Chen C, Yan J, Lu N, Wang Y, Yang X, Guan X (2015) Ubiquitous monitoring for industrial cyber-physical systems over relay-assisted wireless sensor networks. IEEE Trans Emerg Top Comput 3(3):352–362.

    Article  Google Scholar 

  10. 10.

    Dominguez-Morales JP, Rios-Navarro A, Dominguez-Morales M et al (2016) Wireless sensor network for wildlife tracking and behavior classification of animals in Doñana. IEEE Commun Lett 20(12):2534–2537.

    Article  Google Scholar 

  11. 11.

    Aguirre E, Lopez-Iturri P, Azpilicueta L et al (2017) Design and implementation of context aware applications with wireless sensor network support in urban train transportation environments. IEEE Sens J 17(1):169–178.

    Article  Google Scholar 

  12. 12.

    Ciuonzo D, Salvo Rossi P (2017) Distributed detection of a non-cooperative target via generalized locally-optimum approaches. Inf Fusion 36:261–274.

    Article  Google Scholar 

  13. 13.

    Hamouda YEM, Msallam MM (2019) Smart heterogeneous precision agriculture using wireless sensor network based on extended Kalman filter. Neural Comput Appl 31(9):5653–5669.

    Article  Google Scholar 

  14. 14.

    Gupta P, McClatchey R, Caleb-Solly P (2020) Tracking changes in user activity from unlabelled smart home sensor data using unsupervised learning methods. Neural Comput Appl.

    Article  Google Scholar 

  15. 15.

    Zang W, Miao F, Gravina R et al (2020) CMDP-based intelligent transmission for wireless body area network in remote health monitoring. Neural Comput Appl 32:829–837.

    Article  Google Scholar 

  16. 16.

    Sodhro AH, Pirbhulal S, Lodro MM, Shah MA (2018) Energy-efficiency in wireless body sensor networks. In: Networks of the future architectures, technologies, and implementations. Chapman & Hall/CRC, computer and information science series, Taylor & Francis Group, p 492

  17. 17.

    Abdel-Basset M, Shawky LA, Eldrandaly K (2020) Grid quorum-based spatial coverage for IoT smart agriculture monitoring using enhanced multi-verse optimizer. Neural Comput Appl 32:607–624.

    Article  Google Scholar 

  18. 18.

    Chakrabarty K, Iyengar SS, Qi H (2002) Grid coverage for surveillance and target location in distributed sensor networks. IEEE Trans Comput 51(12):1448–1453

    MathSciNet  Article  Google Scholar 

  19. 19.

    Dhillon SS, Chakrabarty K (2003) Sensor placement for effective coverage and surveillance in distributed sensor networks. In: IEEE wireless communications and networking conference (WCNC).

  20. 20.

    Zou Y, Chakrabarty K (2003) Sensor deployment and target localization based on virtual forces. In: IEEE INFOCOM, 2(C), pp 1293–1303

  21. 21.

    Zou Y, Chakrabarty K (2004) Sensor deployment and target localization in distributed sensor networks. ACM Trans Embed Comput Syst 3(1):61–91.

    Article  Google Scholar 

  22. 22.

    Huang CF, Tseng YC (2005) The coverage problem in a wireless sensor network. Mob Netw Appl 10(4):519–528.

    Article  Google Scholar 

  23. 23.

    Xu X, Sahni S (2007) Approximation algorithms for sensor deployment. IEEE Trans Comput 56(12):1681–1695.

    MathSciNet  Article  MATH  Google Scholar 

  24. 24.

    Guo Z, Zhou MC, Jiang G (2008) Adaptive sensor placement and boundary estimation for monitoring mass objects. IEEE Trans Syst Man Cybern Part B Cybern 38(1):222–232.

    Article  Google Scholar 

  25. 25.

    Seo JH, Kim YH, Bin Ryou H, Cha SH, Jo M (2008) Optimal sensor deployment for wireless surveillance sensor networks by a hybrid steady-state genetic algorithm. IEICE Trans Commun E91-B(11):3534–3543.

    Article  Google Scholar 

  26. 26.

    Tsai YR (2008) Sensing coverage for randomly distributed wireless sensor networks in shadowed environments. IEEE Trans Veh Technol 57(1):556–564.

    MathSciNet  Article  Google Scholar 

  27. 27.

    Wang YC, Hu CC, Tseng YC (2008) Efficient placement and dispatch of sensors in a wireless sensor network. IEEE Trans Mob Comput 7(2):262–274.

    Article  Google Scholar 

  28. 28.

    Ferrari S, Zhang G, Wettergren TA (2010) Probabilistic track coverage in cooperative sensor networks. IEEE Trans Syst Man Cybern Part B Cybern 40(6):1492–1504.

    Article  Google Scholar 

  29. 29.

    Mukherjee K, Gupta S, Ray A, Wettergren TA (2011) Statistical-mechanics-inspired optimization of sensor field configuration for detection of mobile targets. IEEE Trans Syst Man Cybern Part B Cybern 41(3):783–791.

    Article  Google Scholar 

  30. 30.

    Singh S, Chand S, Kumar R, Kumar B (2013) Optimal sensor deployment for WSNs in grid environment. Electron Lett 49(16):1040–1041.

    Article  Google Scholar 

  31. 31.

    Derr K, Manic M (2013) Wireless sensor network configuration-part I: mesh simplification for centralized algorithms. IEEE Trans Ind Inf 9(3):1717–1727.

    Article  Google Scholar 

  32. 32.

    Yoon Y, Kim YH (2013) An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks. IEEE Trans Cybern 43(5):1473–1483.

    Article  Google Scholar 

  33. 33.

    Khanjary M, Sabaei M, Reza Meybodi M (2015) Critical density for coverage and connectivity in two-dimensional fixed-orientation directional sensor networks using continuum percolation. J Netw Comput Appl 57:169–181.

    Article  Google Scholar 

  34. 34.

    Dadwal S, Panag TS (2015) Coverage enhancement of average distance based self-relocation algorithm using augmented Lagrange optimization. Int J Next-Gen Netw 7(2/3):11–24.

    Article  Google Scholar 

  35. 35.

    Panag TS, Dhillon JS (2019) Maximal coverage hybrid search algorithm for deployment in wireless sensor networks. Wirel Netw 25(2):637–652.

    Article  Google Scholar 

  36. 36.

    Binh HTT, Hanh NT, Van Quan L, Dey N (2018) Improved Cuckoo Search and Chaotic Flower Pollination optimization algorithm for maximizing area coverage in Wireless Sensor Networks. Neural Comput Appl 30(7):2305–2317.

    Article  Google Scholar 

  37. 37.

    Baek SJ, De Veciana G, Su X (2004) Minimizing energy consumption in large-scale sensor networks through distributed data compression and hierarchical aggregation. IEEE J Sel Areas Commun 22(6):1130–1140.

    Article  Google Scholar 

  38. 38.

    Cardei M, Wu J (2006) Energy-efficient coverage problems in wireless ad-hoc sensor networks. Comput Commun 29(4):413–420.

    Article  Google Scholar 

  39. 39.

    Yu Y, Prasanna VK, Krishnamachari B (2006) Energy minimization for real-time data gathering in wireless sensor networks. IEEE Trans Wirel Commun 5(11):3087–3096.

    Article  Google Scholar 

  40. 40.

    Cui S, Madan R, Goldsmith AJ, Lall S (2007) Cross-layer energy and delay optimization in small-scale sensor networks. IEEE Trans Wirel Commun 6(10):3688–3699.

    Article  Google Scholar 

  41. 41.

    Iyengar SS, Wu HC, Balakrishnan N, Chang SY (2007) Biologically inspired cooperative routing for wireless mobile sensor networks. IEEE Syst J 1(1):29–37.

    Article  Google Scholar 

  42. 42.

    Chang CY, Chang HR (2008) Energy-aware node placement, topology control and MAC scheduling for wireless sensor networks. Comput Netw 52(11):2189–2204.

    Article  MATH  Google Scholar 

  43. 43.

    Leung H, Chandana S, Wei S (2008) Distributed sensing based on intelligent sensor networks. IEEE Circuits Syst Mag 8(2):38–52

    Article  Google Scholar 

  44. 44.

    Panag TS, Dhillon JS (2015) Heuristic Search Algorithm (HSA) for enhancing the lifetime of wireless sensor networks. Int J Electron Commun Eng 9(8):672–678.

    Article  Google Scholar 

  45. 45.

    Panag TS, Dhillon JS (2017) Two stage grid classification based algorithm for the identification of fields under a wireless sensor network monitored area. Wirel Pers Commun 95(2):1055–1074.

    Article  Google Scholar 

  46. 46.

    Panag TS, Dhillon JS (2018) A novel random transition based PSO algorithm to maximize the lifetime of wireless sensor networks. Wirel Pers Commun 98(2):2261–2290.

    Article  Google Scholar 

  47. 47.

    Abbasi AA, Younis M (2007) A survey on clustering algorithms for wireless sensor networks. Comput Commun 30(14–15):2826–2841.

    Article  Google Scholar 

  48. 48.

    Pitchaimanickam B, Murugaboopathi G (2019) A hybrid firefly algorithm with particle swarm optimization for energy efficient optimal cluster head selection in wireless sensor networks. Neural Comput Appl.

    Article  Google Scholar 

  49. 49.

    Chen G, Li C, Ye M, Wu J (2009) An unequal cluster-based routing protocol in wireless sensor networks. Wirel Netw 15(2):193–207.

    Article  Google Scholar 

  50. 50.

    Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670.

    Article  Google Scholar 

  51. 51.

    Ye M, Li C, Chen G, Wu J (2005) EECS: An energy efficient clustering scheme in wireless sensor networks 10a.2. In: Conference proceedings of the IEEE international performance, computing, and communications conference, pp 535–540

  52. 52.

    Dahnil DP, Singh YP, Ho CK (2012) Topology-controlled adaptive clustering for uniformity and increased lifetime in wireless sensor networks. IET Wirel Sens Syst 2(4):318–327.

    Article  Google Scholar 

  53. 53.

    Tarhani M, Kavian YS, Siavoshi S (2014) SEECH: scalable energy efficient clustering hierarchy protocol in wireless sensor networks. IEEE Sens J 14(11):3944–3954.

    Article  Google Scholar 

  54. 54.

    Malathi L, Gnanamurthy RK, Chandrasekaran K (2015) Energy efficient data collection through hybrid unequal clustering for wireless sensor networks. Comput Electr Eng 48:358–370.

    Article  Google Scholar 

  55. 55.

    Gu X, Yu J, Yu D, Wang G, Lv Y (2014) ECDC: an energy and coverage-aware distributed clustering protocol for wireless sensor networks. Comput Electr Eng 40(2):384–398.

    Article  Google Scholar 

  56. 56.

    Mittal N, Singh U, Salgotra R et al (2020) An energy-efficient stable clustering approach using fuzzy-enhanced flower pollination algorithm for WSNs. Neural Comput Appl 32:7399–7419.

    Article  Google Scholar 

  57. 57.

    Zahmati AS, Abolhassani Bahman, Shirazi AAB, Bakhtiari AS (2007) An energy-efficient protocol with static clustering for wireless sensor networks. Int J Comput Electr Autom Control Inf Eng 1(4):874–877

    Google Scholar 

  58. 58.

    Chaurasiya SK, Pal T, Bit S Das (2011) An enhanced energy-efficient protocol with static clustering for WSN. In: International conference on information networking (ICOIN), IEEE, pp 58–63

  59. 59.

    Zhu X, Shen L, Yum TSP (2009) Hausdorff clustering and minimum energy routing for wireless sensor networks. IEEE Trans Veh Technol 58(2):990–997.

    Article  Google Scholar 

  60. 60.

    Ferng HW, Tendean R, Kurniawan A (2012) Energy-efficient routing protocol for wireless sensor networks with static clustering and dynamic structure. Wirel Pers Commun 65(2):347–367.

    Article  Google Scholar 

  61. 61.

    Lung CH, Zhou C (2010) Using hierarchical agglomerative clustering in wireless sensor networks: an energy-efficient and flexible approach. Ad Hoc Netw 8(3):328–344.

    Article  Google Scholar 

  62. 62.

    Min X, Wei-ren S, Chang-jiang J, Ying Z (2010) Energy efficient clustering algorithm for maximizing lifetime of wireless sensor networks. AEU Int J Electron Commun 64(4):289–298.

    Article  Google Scholar 

  63. 63.

    Panag TS, Dhillon JS (2018) Dual head static clustering algorithm for wireless sensor networks. AEU Int J Electron Commun 88:148–156.

    Article  Google Scholar 

  64. 64.

    Hosseini VR, Chen W, Avazzadeh Z (2014) Numerical solution of fractional telegraph equation by using radial basis functions. Eng Anal Bound Elem 38:31–39.

    MathSciNet  Article  MATH  Google Scholar 

  65. 65.

    Avazzadeh Z, Hosseini VR, Chen W (2014) Radial basis functions and FDM for solving fractional diffusion-wave equation. Iran J Sci Technol 38A3:205–212

    MathSciNet  Google Scholar 

  66. 66.

    Avazzadeh Z, Chen W, Hosseini VR (2014) The coupling of RBF and FDM for solving higher order fractional partial differential equations. Appl Mech Mater 598:409–413.

    Article  Google Scholar 

  67. 67.

    Hosseini VR, Shivanian E, Chen W (2016) Local radial point interpolation (MLRPI) method for solving time fractional diffusion-wave equation with damping. J Comput Phys 312:307–332.

    MathSciNet  Article  MATH  Google Scholar 

  68. 68.

    Hosseini VR, Shivanian E, Chen W (2015) Local integration of 2-D fractional telegraph equation via local radial point interpolant approximation. Eur Phys J Plus 130:33.

    Article  Google Scholar 

  69. 69.

    Khalilpourazari S, Pasandideh SHR (2019) Sine–cosine crow search algorithm: theory and applications. Neural Comput Appl.

    Article  Google Scholar 

  70. 70.

    Sodhro AH, Obaidat MS, Abbasi QH et al (2019) Quality of service optimization in IoT driven intelligent transportation system. IEEE Wirel Commun 26(6):10–17.

    Article  Google Scholar 

  71. 71.

    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, Perth, pp 1942–1948

  72. 72.

    Silva A, Neves A, Costa E (2002) An empirical comparison of particle swarm and predator prey optimisation. In: Lecture notes in artificial intelligence (subseries of lecture notes in computer science) 2464:103–110.

  73. 73.

    Narang N, Dhillon JS, Kothari DP (2014) Scheduling short-term hydrothermal generation using predator prey optimization technique. Appl Soft Comput J 21:298–308.

    Article  Google Scholar 

  74. 74.

    Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82

    Article  Google Scholar 

  75. 75.

    Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: The 33rd Hawaii international conference on system sciences, Hawaii, pp 1–10

Download references

Author information



Corresponding author

Correspondence to Tripatjot Singh Panag.

Ethics declarations

Conflict of interest

Authors have no conflict of interest in publishing their work in this journal.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Panag, T.S., Dhillon, J.S. Predator–prey optimization based clustering algorithm for wireless sensor networks. Neural Comput & Applic (2021).

Download citation


  • Optimization
  • WSN lifetime
  • Clustering
  • Predator–prey optimization (PPO)
  • Wireless sensor network