Energy Management Routing in Wireless Sensor Networks

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 258)


Wireless Sensor Networks sensor nodes collect, process, and communicate data acquired from the physical environment to an external Base-Station (BS). Its flexibility in terms of the shape of the network and mobility of the sensor nodes makes it special. Sensor nodes in WSNs are normally battery-powered, so energy has to be carefully utilized in order to avoid early termination of sensors’ lifetimes. Also sensors position in network is also initially not determined so sensor should be capable of generating optimal routing path and transmitting data to the base station. Second constraint with the sensors is bandwidth. Considering these two limitations it is necessary routing and sensing algorithm that use innovative methods to preserve energy of sensors. In this paper we use neural network to conserve energy of WSN and increase the life of network.


WSN Neural network Energy optimization 


  1. 1.
    Akyildiz, I.F., Su, W., Sankarasubramania, Y., Cayirci, E.: A survey on sensor networks. IEEE Commun. Mag. 40(8), 102–114, (2002)Google Scholar
  2. 2.
    Chong, C-Y., Kumar, S.P.: Sensor networks: evolution, opportunities, and challenges. Proc. IEEE 91(8), 1247–1256 (2003)Google Scholar
  3. 3.
    Al-Karaki, N., Kamal, A.E.: Routing techniques in wireless sensor networks: a survey. IEEE Wirel. Commun. 11(6), 6–28 (2004)CrossRefGoogle Scholar
  4. 4.
    Al-Karaki, J.N., Kamal, A.E.: Routing techniques in wireless sensor networks: a survey. IEEE Wirel. Commun. 11(6), 6–28 (2004)CrossRefGoogle Scholar
  5. 5.
    Heinzelman, W., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Hawaii International Conference on System Sciences (HICSS ‘00) (2000)Google Scholar
  6. 6.
    Hu, L., Li, Y., Chen, Q., Liu, J-Y., Long, K-P.: A new energy-aware routing protocol for wireless sensor networks. International conference on wireless communications, networking and mobile computing (WiCom 2007), pp. 2444–2447, 21–25 Sept 2007 Google Scholar
  7. 7.
    Bates, P.: Debugging heterogeneous distributed systems using event based models of behavior. ACM Trans. Comput. Syst. 13, 1 (1995)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Frei, C.: Abstraction techniques for resource allocation in communication networks. Ph.D. Dissertation, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland, 2000Google Scholar
  9. 9.
    Cerpa, A., Busek, N., Estrin, D.: Scale: a tool for simple connectivity assessment in lossy environments. Tech. Rep. 21, Center for Embedded Networked Sensing, University of California, Los Angeles (2003)Google Scholar
  10. 10.
    Yarvis, M., Conner, W., Krishnamurthy, L., Chhabra, J., Elliott, B., Mainwaring, A.: Real-world experiences with an interactive ad hoc sensor network. In: Proceedings of the 31st IEEE International Conference on Parallel Processing Workshops (ICPPW), IEEE Computer Society, Vancouver (2002)Google Scholar
  11. 11.
    Zhao, J., Govindan, R.: Understanding packet delivery performance in dense wireless sensor networks. In: Proceedings of the 1st ACM International Conference on Embedded Networked Sensor Systems, SENSYS, ACM Press, Los Angeles (2003)Google Scholar
  12. 12.
    Okdem, S., et al.: Routing in WSN using ant colony optimization router chip. Sensors 9, 909–921. ISSN: 1424-8220 (2009)Google Scholar
  13. 13.
    Sharma, N.K., Kumar, S., Singh, M.P.: Conjugate descent formulation of backpropogation error in feed forward neural network. ORiON 25(1), 69–86, ISSN: 0529-191-X (2009)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.MITMIET GroupMeerutIndia

Personalised recommendations