Cluster Computing

, Volume 22, Supplement 5, pp 12157–12168 | Cite as

FSO–PSO based multihop clustering in WSN for efficient Medical Building Management System

  • G. ShanthiEmail author
  • M. Sundarambal


A Wireless Sensor Network (WSN) is the one that is formed keeping a maximum number of sensor nodes that have been positioned in any application or an environment for monitoring the physical entities in the target area. The main challenge is the organizing of sensor networks with efficacy of energy. This need for efficacy of energy is owing to the capacity of the sensor node being limited and their replacement not being viable. An efficient technique to prolong the lifetime of WSNs is by means of adapting clustering algorithm. This includes the grouping of sensor nodes into clusters and the electing of Cluster Heads (CH) and the forwarding of their aggregated data to that of the Base Station (BS). A challenge involved in the WSN is the choice of suitable CH. Building Management Systems are a control system that is computer-based and installed in buildings which tend to control and also monitor the mechanical as well as the electrical equipment of the building like the ventilation, power systems, lighting etc., Medical sensor nodes have been used for examine several signals from a human body to monitor parameters like blood pressure, body temperature, blood sugar, pulse oxygenation of the blood etc. The work proposed the Fish Swarm Optimization (FSO), the Particle Swarm Optimization (PSO) that is based on multi-hop clustering algorithm for the saving energy consumption in WSN. When the visual and the step dominated the FSO they are quite challenging to be set as well. The work employs the PSO formulation for modifying the FSO and make is free from step. Also, visual along with the searching domain is formulated to improve ease of setting. The results of the experiment show that this method has better performance.


Wireless Sensor Network (WSN) Clustering Energy Medical Building Management System (BMS) Fish Swarm Optimization (FSO) and Particle Swarm Optimization (PSO) 


  1. 1.
    Kumar, D., Aseri, T.C., Patel, R.B.: A novel multihop energy efficient heterogeneous clustered scheme for wireless sensor networks. Tamkang J. Sci. Eng. 14(4), 359–368 (2011)Google Scholar
  2. 2.
    Arioua, M., el Assari, Y., Ez-Zazi, I., El Oualkadi, A.: Multi-hop cluster based routing approach for wireless sensor networks. Procedia Comput. Sci. 83, 584–591 (2016)CrossRefGoogle Scholar
  3. 3.
    Li, N., Becerik-Gerber, B.: Exploring the use of wireless sensor networks in building management. In: Proceedings of the International Conference on Computing in Civil and Building Engineering, vol. 30, pp. 91-97. Nottingham University Press, UK (2010, June)Google Scholar
  4. 4.
    Guerrieri, A., Fortino, G., Ruzzelli, A., O’Hare, G.M.: A WSN-based building management framework to support energy-saving applications in buildings. In: Advancements in Distributed Computing and Internet Technologies: Trends and Issues, pp. 258–273. IGI Global (2012)Google Scholar
  5. 5.
    Alemdar, H., Ersoy, C.: Wireless sensor networks for healthcare: a survey. Comput. Netw. 54(15), 2688–2710 (2010)CrossRefGoogle Scholar
  6. 6.
    Kumar, P., Lee, H.J.: Security issues in healthcare applications using wireless medical sensor networks: a survey. Sensors 12(1), 55–91 (2011)CrossRefGoogle Scholar
  7. 7.
    Al Ameen, M., Liu, J., Kwak, K.: Security and privacy issues in wireless sensor networks for healthcare applications. J. Med. Syst. 36(1), 93–101 (2012)CrossRefGoogle Scholar
  8. 8.
    Xu, L., O’Hare, G.M., Collier, R.: A balanced energy-efficient multihop clustering scheme for wireless sensor networks. In: 2014 7th IFIP on Wireless and Mobile Networking Conference (WMNC), pp. 1–8. IEEE (2014, May)Google Scholar
  9. 9.
    Rao, P.S., Jana, P.K., Banka, H.: A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wirel. Netw. 23, 2005–2020 (2016)CrossRefGoogle Scholar
  10. 10.
    Solaiman, B.: Energy optimization in wireless sensor networks using a hybrid k-means pso clustering algorithm. Turkish J. Electr. Eng. Comput. Sci. 24(4), 2679–2695 (2016)CrossRefGoogle Scholar
  11. 11.
    de Oliveira Matos, V., Arroyo, J.E.C., dos Santos, A.G., Gonçalves, L.B.: An energy-efficient clustering algorithm for wireless sensor networks. IJCSNS 12(10), 6 (2012)Google Scholar
  12. 12.
    Guinard, A., McGibney, A., Pesch, D.: A wireless sensor network design tool to support building energy management. In: Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, pp. 25–30. ACM (2009, November)Google Scholar
  13. 13.
    Kazmi, A.H., O’grady, M.J., Delaney, D.T., Ruzzelli, A.G., O’hare, G.M.: A review of wireless-sensor-network-enabled building energy management systems. ACM Trans. Sens. Netw. (TOSN) 10(4), 66 (2014)Google Scholar
  14. 14.
    Špinar, R., Muthukumaran, P., de Paz, R., Pesch, D., Song, W., Chaudhry, S.A., ... Costa, A.: Efficient building management with IP-based wireless sensor network. In: EWSN 2009 6th European Conference on Wireless Sensor Networks, February 11th–13th, Cork, Ireland (2009)Google Scholar
  15. 15.
    Ko, J., Lu, C., Srivastava, M.B., Stankovic, J.A., Terzis, A., Welsh, M.: Wireless sensor networks for healthcare. Proc. IEEE 98(11), 1947–1960 (2010)CrossRefGoogle Scholar
  16. 16.
    Lawrence, E., Navarro, K.F., Hoang, D., Lim, Y.Y.: Data collection, correlation and dissemination of medical sensor information in a WSN. In: Fifth International Conference on Networking and Services, 2009. ICNS’09, pp. 402–408. IEEE (2009, April)Google Scholar
  17. 17.
    Chen, Y., Shen, W., Huo, H., Xu, Y.: A smart gateway for health care system using wireless sensor network. In: 2010 Fourth International Conference on Sensor Technologies and Applications (SENSORCOMM), pp. 545–550. IEEE (2010, July)Google Scholar
  18. 18.
    Anandamurugan, S., Abirami, T.: Antipredator adaptation shuffled frog leap algorithm to improve network life time in wireless sensor network. Wireless Personal Communications, 1–12 (2017)Google Scholar
  19. 19.
    Azharuddin, M., Jana, P.K.: Particle swarm optimization for maximizing lifetime of wireless sensor networks. Comput. Electr. Eng. 51, 26–42 (2016)CrossRefGoogle Scholar
  20. 20.
    Wang, J., Cao, Y., Li, B., Kim, H.J., Lee, S.: Particle swarm optimization based clustering algorithm with mobile sink for WSNs. Future Gener. Comput. Syst. 76, 452–457 (2017)CrossRefGoogle Scholar
  21. 21.
    Shankar, T., Shanmugavel, S., Rajesh, A.: Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm Evol. Comput. 30, 1–10 (2016)CrossRefGoogle Scholar
  22. 22.
    Azizi, R., Sedghi, H., Shoja, H., Sepas-Moghaddam, A.: A novel energy aware node clustering algorithm for wireless sensor networks using a modified artificial fish swarm algorithm. arXiv preprint arXiv:1506.00099 (2015)
  23. 23.
    Neshat, M., Sepidnam, G., Sargolzaei, M., Toosi, A.N.: Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif. Intell. Rev. 1–33 (2014)Google Scholar
  24. 24.
    Islam, S.M.M., Reza, M.A.R., Kiber, M.A.: Wireless sensor network using particle swarm optimization. In: Proceedings of the International Conference on Advances in Control System and Electricals Engineering (2013)Google Scholar
  25. 25.
    Sarangi, S., Thankchan, B.: A novel routing algorithm for wireless sensor network using particle swarm optimization. Int. J. Res. Eng. Inf. Soc. Sci. 4, 26–30 (2012)Google Scholar
  26. 26.
    Song, X., Wang, C., Wang, J., Zhang, B.: A hierarchical routing protocol based on AFSO algorithm for WSN. In: 2010 International Conference on Computer Design and Applications (ICCDA), vol. 2, pp. V2-635. IEEE (2010, June)Google Scholar
  27. 27.
    Vimalarani, C., Subramanian, R., Sivanandam, S.N.: An enhanced PSO-based clustering energy optimization algorithm for wireless sensor network. Sci. World J. (2016)Google Scholar
  28. 28.
    Prasad, D.R., Naganjaneyulu, P.V., Prasad, K.S.: Energy efficient clustering in multi-hop wireless sensor networks using differential evolutionary MOPSO. Braz. Arch. Biol. Technol. 59(SPE2), (2016)Google Scholar
  29. 29.
    Mao, M., Zhang, L., Chong, B. V. P., Musembi, M., Duan, Q.: Artificial fish swarm algorithm based-maximum power generation for grid-connected PV panels. In: 2017 UKSim-AMSS 19th International Conference on Modelling & Simulation, pp. 130–135. Institute of Electrical and Electronics Engineers (IEEE) (2017, March)Google Scholar
  30. 30.
    Duan, Q., Mao, M., Duan, P., Hu, B.: An improved artificial fish swarm algorithm optimized by particle swarm optimization algorithm with extended memory. Kybernetes 45(2), 210–222 (2016)CrossRefGoogle Scholar
  31. 31.
    Ali, Q.I.: Simulation framework of wireless sensor network (WSN) using matlab/simulink software. In: MATLAB-A Fundamental Tool for Scientific Computing and Engineering Applications, vol. 2. intech (2012)Google Scholar
  32. 32.
    Llor, J., Malumbres, M.P., Garrido, P.: Performance evaluation of underwater wireless sensor networks with OPNET. In: Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques, pp. 19–26. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (2011, March)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and CommunicationSVS College of EngineeringCoimbatoreIndia
  2. 2.Department of Electrical and ElectronicsCoimbatore Institute of TechnologyCoimbatoreIndia

Personalised recommendations