Skip to main content

Advertisement

SpringerLink
Log in

Cluster head selection for energy efficient and delay-less routing in wireless sensor network

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Wireless sensor network (WSN) is comprised of tiny, cheap and power-efficient sensor nodes which effectively transmit data to the base station. The main challenge of WSN is the distance, energy and time delay. The power resource of the sensor node is a non-rechargeable battery. Here the greater the distance between the nodes, higher the energy consumption. For having the effective transmission of data with less energy, the cluster-head approach is used. It is well known that the time delay is directly proportional to the distance between the nodes and the base station. The cluster head is selected in such a way that it is spatially closer enough to the base station as well as the sensor nodes. So, the time delay can be substantially reduced. This, in turn, the transmission speed of the data packets can be increased. Firefly algorithm is developed for maximizing the energy efficiency of network and lifetime of nodes by selecting the cluster head optimally. In this paper firefly with cyclic randomization is proposed for selecting the best cluster head. The network performance is increased in this method when compared to the other conventional algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Abbreviations

\(N_{n}\) :

Number of sensor nodes

\(N_{c}\) :

Cluster head

\(N^{0}\) :

Number of clusters

\(B_{s}\) :

Base station

\(DM(m*n)\) :

Distance matrix

\(d_{{N_{c} }}\) :

Distance between cluster head and node

\(d_{0}\) :

Threshold distance

\(E_{fs}\) :

Required energy during free space model

\(E_{mp}\) :

Energy of the power amplifier

\(E_{TX} (N:d)\) :

Total energy transmitted

\(E_{RX}\) :

Total energy received

\(N\) :

Number of bits

\(d\) :

Distance between nodes

\(E_{e}\) :

Energy required in per bit transmits circuitry

\(E_{am}\) :

Energy required for amplification

\(E_{total}\) :

Total energy of network

\(E_{1}\) :

Energy cost during idle state

\(E_{el}\) :

Electronic energy

\(E_{S}\) :

Energy cost while sensing

\(E_{ae}\) :

Data aggregation energy

\(\sigma_{1}\), \(\sigma_{2}\) and \(\sigma_{3}\) :

Constant parameters of distance, energy and delay

\(X_{x}\) :

Available nodes

\(X_{y}\) :

Unavailable nodes

\(C_{x}\) :

\(x^{th}\) cluster head

\(F_{n}\) :

Objective function

\(f_{i}^{dis}\) :

Distance function

\(f_{i}^{ene}\) :

Energy function

\(f_{i}^{del}\) :

Delay function

\(I\) :

Light intensity of firefly

\(\nu\) :

Absorption coefficient of firefly

\(\beta_{0}\) :

Attractiveness of firefly

\(r\) :

Distance of fireflies

\(x_{i}\) :

Initial solution of firefly algorithm

\(x_{i + 1}\) :

Updated solution of firefly algorithm

References

  1. Li, B. L. (2013). High performance flexible sensor based on inorganic nanomaterials. Discovering Value, 176, 522–533.

    Google Scholar 

  2. Yu, X., Li, C., & Low, Z. N. (2008). Wireless hydrogen sensor network using AlGaN/GaN high electron mobility transistor differential diode sensors. Sensors and Actuators B-Chemical, 135(1), 188–194.

    Article  Google Scholar 

  3. Chung, W. Y., Lee, B. G., & Yang, C. S. (2009). 3D virtual viewer on mobile device for wireless sensor network-based RSSI indoor tracking system. Sensors and Actuators b-Chemical, 140(1), 35–42.

    Article  Google Scholar 

  4. Shen, H., & Li, Z. (2015). A P2P-based market-guided distributed routing mechanism for high-throughput hybrid wireless networks. IEEE Transactions on Mobile Computing, 14, 245–260.

    Article  Google Scholar 

  5. Vivekchandran, K. C., & Nikesh Narayan, P. (2015). Energy efficiency and latency improving in wireless sensor networks. International Journal of Science and Research (IJSR), 4(5), 1291–1295.

    Google Scholar 

  6. Kumar, R., & Kumar, D. (2016). Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network. Wireless Networks, 22(5), 1461–1474.

    Article  Google Scholar 

  7. Amaro, J. P., Ferreira, F. J., Cortesão, R., & Landeck, J. (2012). Powering wireless sensor networks nodes for complex protocols on harvested energy. In 4th conference of enterprise information systems – aligning technology, organizations and people (CENTERIS 2012), vol. 5, (pp. 518–526).

  8. Walton, R., Anthony, C., Ward, M., Metje, N., & Chapman, D. N. (2013). Radioisotopic battery and capacitor system for powering wireless sensor networks. Sensors and Actuators, A: Physical, 203, 405–412.

    Article  Google Scholar 

  9. He, P., Tian, H., & Shen, H. (2012). Energy-efficient cooperative MIMO routing inwireless sensor networks. In IEEE international conference on networks (pp.74–79).

  10. Hu, X., Li, Y., & Xu, H. (2017). Multi-mode clustering model for hierarchical wireless sensor networks. Physica A: Statistical Mechanics and its Applications, 469, 665–675.

    Article  Google Scholar 

  11. Mann, P. S., & Singh, S. (2017). Improved metaheuristic based energy-efficient clustering protocol for wireless sensor networks. Engineering Applications of Artificial Intelligence, 57, 142–152.

    Article  Google Scholar 

  12. Hosseinirad, S. M., Ali, M. N., & Basu, S. K. (2014). LEACH routing algorithm optimization through imperialist approach. International Journal of Engineering, Transactions A: Basics, 27(1), 39–50.

    Google Scholar 

  13. Fotouhi, H., Alves, M., & Zamalloa, M. Z. (2014). Reliable and fast hand-offs in low-power wireless networks. IEEE Transactions on Mobile Computing, 13(11), 2621–2633.

    Article  Google Scholar 

  14. Chung-Shuo, F. A. N. (2013). Rich: Region-based intelligent cluster-head selection and node deployment strategy in concentric-based WSNs. Advances in Electrical and Computer Engineering, 13(4), 3–8.

    Article  Google Scholar 

  15. Geeta, D. D., Nalini, N., & Biradar, R. C. (2013). Fault tolerance in wireless sensor network using hand-off and dynamic power adjustment approach. Journal of Network and Computer Applications, 36(4), 1174–1185.

    Article  Google Scholar 

  16. Tyagi, S., & Kumar, N. (2013). A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks. Journal of Network and Computer Applications, 36(2), 623–645.

    Article  Google Scholar 

  17. Javaid, N., Waseem, M., & Khan, Z.A. (2013). ACH: away cluster heads scheme for energy efficient clustering protocols in WSNs. In Saudi International electronics, communications and photonics conference, Piscataway (pp. 364–367). IEEE.

  18. Poduri, S., & Sukhatme, G. S. (2004). Constrained coverage for mobile sensor networks. In Proceedings of the IEEE international conference robotics and automation (ICRA’04), (pp. 165–172).

  19. Zou, Y., & Chakrabarty, K. (2003). Sensor deployment and target localizations based on virtual forces. In Proceedings of the IEEE INFOCOM’03.

  20. Singh, B., & Lobiyal, D. K. (2012). A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Human-Centric Computing and Information Sciences, 2, 1–18.

    Article  Google Scholar 

  21. 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.

    Article  Google Scholar 

  22. Rakhee, & Srinivas, M. B. (2016). Cluster based energy efficient routing protocol using ANT colony optimization and breadth first search. Procedia Computer Science, 89, 124–133.

    Article  Google Scholar 

  23. Pal, V., Singh, G., & Yadav, R. P. (2015). Cluster head selection optimization based on genetic algorithm to prolong lifetime of wireless sensor networks. Procedia Computer Science, 57, 1417–1423.

    Article  Google Scholar 

  24. Cheng, L., Niu, J., Cao, J., Das, S. K., & Gu, Y. (2014). QoS aware geographic opportunistic routing in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 25(7), 1864–1875.

    Article  Google Scholar 

  25. Hoang, D. C., Yadav, P., Kumar, R., & Panda, S. K. (2014). Real-time implementation of a harmony search algorithm-based clustering protocol for energy-efficient wireless sensor networks. IEEE Transactions on Industrial Informatics, 10(1), 774–783.

    Article  Google Scholar 

  26. Puggelli, A., Mozumdar, M. M. F., Lavagno, L. (2016). Routing-aware design of indoor wireless sensor networks using an interactive tool. IEEE Systems Journal, 9(3), 714–727.

    Article  Google Scholar 

  27. Tang, D., Li, T., Ren, J., & Wu, J. (2015). Cost-aware secure routing (CASER) protocol design for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26(4), 960–973.

    Article  Google Scholar 

  28. 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 Transactions on Nuclear Science, 61(2), 732–740.

    Article  Google Scholar 

  29. RejinaParvin, J., & Vasanthanayaki, C. (2015). Particle swarm optimization-based clustering by preventing residual nodes in wireless sensor networks. IEEE Sensors Journal, 15(8), 4264–4274.

    Article  Google Scholar 

  30. Baskaran, M., & Sadagopan, C. (2015). Synchronous firefly algorithm for cluster head selection in WSN. The Scientific World Journal, 2015, 1–7.

    Article  Google Scholar 

  31. Hamrioui, S., & Lorenz, P. (2015). ES-WSN: Energy efficient by switching between roles of nodes in WSNs. In IEEE global communications conference (GLOBECOM), (pp. 1–6). San Diego, CA.

  32. Lv, Y., Miao, Z., Zhang, D., & Li, A. (2016). A low energy uneven clustering topology control algorithm for wireless networks. In 3rd international conference on information science and control engineering (ICISCE), (pp. 1203–1207). Beijing.

  33. Dong, Y., Wang, J., Shim, B., & Kim, D. I. (2016). DEARER: A distance-and-energy-aware routing with energy reservation for energy harvesting wireless sensor networks. IEEE Journal on Selected Areas Communications, 34(12), 3798–3813.

    Article  Google Scholar 

  34. Zungeru, A. M., Ang, L.-M., & Seng, K. P. (2012). Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison. Journal of Network and Computer Applications, 35(5), 1508–1536.

    Article  Google Scholar 

  35. Al-Karaki, J. N., Ul-Mustafa, R., & Kamal, A. E. (2009). Data aggregation and routing in wireless sensor networks: Optimal and heuristic algorithms. Computer Networks, 53(7), 945–960.

    Article  MATH  Google Scholar 

  36. Kiri, Y., Sugano, M., & Murata, M. (2007). Self-organized data-gathering scheme for multi sink sensor networks inspired by swarm intelligence. In Proceedings of first international conference on self-adaptive and self-organizing systems (SASO), (pp. 161–172).

  37. Wang, G., Wang, Y., & Tao, X. (2009). An ant colony clustering routing algorithm for wireless sensor networks. In Proceedings of third international conference on genetic and evolutionary computing, (pp. 670–673).

  38. Ziyadi, M., Yasami, K., & Abolhassani, B. (2009). Adaptive clustering for energy efficient wireless sensor networks based on ant colony optimization. In Proceedings of the seventh annual communication networks and services research conference, (pp. 330–334).

  39. Wang, L., Zhang, R., & Model, A. N. (2009). An energy-balanced ant-based routing protocol for wireless sensor networks. In Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing, WiCOM'09, (pp. 3556–3559). Beijing, China.

  40. Xiu-li, R., Hong-wei, L., & Yu, W. (2008). Multipath routing based on ant colony system in wireless sensor networks. In Proceedings of international conference on computer science and software engineering, (pp. 202–205).

  41. Liu, P. X. (2004). Data gathering communication in wireless sensor networks using ant colony optimization. In Proceedings of IEEE international conference on robotics and biomimetics, (pp. 822–827).

  42. Ramachandran, C., Misra, S., & Obaidat, M. S. (2008). Probabilistic zonal approach for swarm inspired wildfire detection using sensor networks. International Journal of Communication Systems, 21, 1047–1073.

    Article  Google Scholar 

  43. Yang, J., Lin, Y., Xiong, W., & Xu, B. (2008). Ant colony-based multi-path routing algorithm for wireless sensor networks. In Proceedings of international conference on computer science and software engineering, (pp. 2–5).

  44. De-min, G., Huan-yan, Q., Xiao-yong, Y., & Xiao-nan, W. (2008). Based on ant colony multicast trees of wireless sensor network routing research. Journal of iet-wsn.org, 2, 1–7.

    Google Scholar 

  45. Wang, X., Qiaoliang, L., Naixue, X., & Yi, P. (2008). Ant colony optimization based location-aware routing for wireless sensor networks. In Proceedings of the third international conference on wireless algorithms, systems, and applications (WASA’08), (vol. 5258, pp.109–120). Berlin, Heidelberg: Springer.

  46. Chen, W., Li, C., Chiang, F., & Chao, H. (2007). Jumping ant routing algorithm for sensor networks. Computer Communications, 30, 2892–2903.

    Article  Google Scholar 

  47. Bi, J., Li, Z., & Wang, R. (2010). An ant colony optimization-based load balancing routing algorithm for wireless multimedia sensor networks. In Proceedings of the 12th IEEE international conference on communication technology (ICCT), (pp. 584–587).

  48. Camilo, T., Carreto, C., Silva, J. S., & Boavida, F. (2006). An energy efficient ant-based routing algorithm for wireless sensor networks. Ant Colony Optimization and Swarm Intelligence, (pp. 49–59).

  49. Ghasemaghaei, R., Rahman, M. A., Gueaieb, W., & El Saddik, A. (2007). Ant colony-based reinforcement learning algorithm for routing in wireless sensor networks. In Proceedings of IEEE instrumentation and measurement technology conference, IMTC, (pp. 1–6).

  50. Ghasemaghaei, R., Mahfujur Rahman, A., Abdur Rahman, M., Gueaieb, W., & El Saddik, A. (2008). Ant colony-based many-to-one sensory data routing in wireless sensor networks. In Proceedings of IEEE/ACS international conference on computer systems and applications, (pp. 1005–1010).

  51. Misra, R., & Mandal, C. (2006). Ant-aggregation: ant colony algorithm for optimal data aggregation in wireless sensor networks. In Proceedings of IEEE international conference on wireless and optical communications networks, (pp. 1–5).

  52. Sun, Y., Ma, H., Liu, L., & Zheng, Y. (2008). ASAR: An ant-based service-aware routing algorithm for multimedia sensor networks. Frontiers of Electrical and Electronic Engineering in China, 3(1), 25–33.

    Article  Google Scholar 

  53. White, T., Pagurek, B., & Oppacher, F. (1998) Connection management using adaptive mobile agents. In Proceedings of international conference on parallel distributed processing techniques and applications (pp. 802–809). CSREA Press.

  54. Xia, S., & Wu, S. (2009). Ant colony-based energy-aware multipath routing algorithm for wireless sensor networks. In Proceedings of second international symposium on knowledge acquisition and modeling, (pp.198–201).

  55. Jietai, W., Jiadong, X. U., & Mantian, X. (2009). EAQR: an energy-efficient ACO based QoS routing algorithm in wireless sensor networks. Chinese Journal of Electronics, 18, 113–116.

    Google Scholar 

  56. Peng, S., Yang, S. X., Gregori, S., & Tian, F. (2008). An adaptive QoS and energy-aware routing algorithm for wireless sensor networks. IEEE International Conference on Information and Automation (ICIA 2008), (pp. 578–583). Changsha, China.

  57. Wen, Y.-F., Chen, Y.-Q., & Pan, M. (2008). Adaptive ant-based routing in wireless sensor networks using energy delay metrics. Journal of Zhejiang University Science, 9, 531–538.

    Article  MATH  Google Scholar 

  58. Dhurandher, S. K., Misra, S., Obaidat, M. S., & Gupta, N. (2008). QDV: A quality-of-security-based distance vector routing protocol for wireless sensor networks using ant colony optimization. In Proceedings of 2008 IEEE international conference on wireless and mobile computing, networking and communications, (pp. 598–602).

  59. Zhang, Y., Kuhn, L. D., & Fromherz, M. P. J. (2004). Improvements on ant routing for sensor networks. In Ant colony optimization and swarm intelligence. Lecture notes computer science (pp. 289–313).

  60. Cobo, L., Quintero, A., & Pierre, S. (2010). Ant-based routing for wireless multimedia sensor networks using multiple QoS metrics. Computer Networks, 54, 2991–3010.

    Article  Google Scholar 

  61. Mahadevan, V., & Chiang, F. (2010). iACO: A bio-inspired power efficient routing scheme for sensor networks. International Journal of Computer Theory and Engineering, 2, 972–977.

    Article  Google Scholar 

  62. Krishnanand, K. N., & Ghose, D. (2006). Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications. Multiagent and Grid Systems – An International Journal, 2, 209–222.

    Article  MATH  Google Scholar 

  63. Saleem, M., & Farooq, M. (2007). Beesensor: a bee-inspired power aware routing protocol for wireless sensor networks. In Proceedings of EvoWorkshops (EvoCOMNET), (vol. 4448, pp. 81–90).

  64. Jia, D., Zhu, H., Zou, S., & Hu, P. (2015). Dynamic cluster head selection method for wireless sensor network. IEEE Sensors Journal, 16(8), 2746–2754.

    Article  Google Scholar 

  65. Kumar, B., & Sharma, V. K. (2012). Distance based cluster head selection algorithm for wireless sensor network. International Journal of Computer Applications, 57(9), 41–45.

    Google Scholar 

  66. Azad, P., & Sharma, V. (2013). Cluster head selection in wireless sensor networks under fuzzy environment. ISRN Sensor Networks, 2013, 1–8.

    Article  Google Scholar 

  67. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.

    Article  Google Scholar 

  68. Fister, I., Jr., Yang, X. S., & Brest, J. (2013). A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation, 13, 34–46.

    Article  Google Scholar 

  69. Fister, I., Jr., Perc, M., & Kamal, S. M. (2015). A review of chaos-based firefly algorithms: Perspectives and research challenges. Applied Mathematics and Computation, 252, 155–165.

    Article  MathSciNet  MATH  Google Scholar 

  70. Paone, M., Paladina, L., Scarpa, M., & Puliafito, A. (2009). A multi-sink swarm-based routing protocol for wireless sensor networks. In Proceedings of IEEE symposium on computers and communications, (pp. 28–33).

  71. He, L., & Huang, S. (2017). Modified firefly algorithm based multilevel thresholding for color image segmentation. Neurocomputing, 240, 152–174.

    Article  Google Scholar 

  72. Gupta, A., & Padhy, P. K. (2016). Modified Firefly Algorithm based controller design for integrating and unstable delay processes. Engineering Science and Technology, an International Journal, 19(1), 548–558.

    Article  Google Scholar 

  73. Verma, O. P., Aggarwal, D., & Patodi, T. (2016). Opposition and dimensional based modified firefly algorithm. Expert Systems with Applications, 44, 168–176.

    Article  Google Scholar 

  74. Kavousi-Fard, A., Samet, H., & Marzbani, F. (2014). A new hybrid Modified Firefly Algorithm and support vector regression model for accurate short term load forecasting. Expert Systems with Applications, 41(13), 6047–6056.

    Article  Google Scholar 

  75. Zaman, M. A., & Sikder, U. (2015). Bouc–Wen hysteresis model identification using Modified Firefly Algorithm. Journal of Magnetism and Magnetic Materials, 395, 229–233.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Vel Tech Dr RR & Dr SR Technical University, Avadi, Chennai, India

    Amit Sarkar & T. Senthil Murugan

Authors
  1. Amit Sarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Senthil Murugan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amit Sarkar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sarkar, A., Senthil Murugan, T. Cluster head selection for energy efficient and delay-less routing in wireless sensor network. Wireless Netw 25, 303–320 (2019). https://doi.org/10.1007/s11276-017-1558-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-017-1558-2

Keywords

Search

Navigation