Energy Efficient Aggregation in Wireless Sensor Networks for Multiple Base Stations

  • Nagarjuna Reddy Busireddy
  • Siba K. Udgata
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)


Data aggregation is essential for wireless sensor networks (WSN) where energy resources are limited. Due to scarce energy resources, construction of data aggregation tree in WSN from group of source nodes to sink nodes with less energy consumption is challenging. In this work, we propose a method to determine the aggregation tree with less energy consumption using Artificial Bee Colony (ABC) algorithm, which is a swarm intelligence technique. We compute the fitness (energy consumption of whole network) by considering multiple algorithms in the same network and then evolving the solution until fitness value is minimum. Our preliminary results suggest that, under investigated scenarios, the usage of ABC algorithm for aggregation in WSN with multiple base stations can achieve energy savings over Shortest path tree data aggregation and Ant colony data aggregation with multiple base stations.


WSN Aggregation Energy Efficient ABC Algorithm 


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  1. 1.
    Back, T., Schutz, M., Khuri, S.: A comparative study of a penalty function, a repair heuristic, and stochastic operators with the set-covering problem. In: Alliot, J.-M., Ronald, E., Lutton, E., Schoenauer, M., Snyers, D. (eds.) AE 1995. LNCS, vol. 1063, pp. 320–332. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  2. 2.
    Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, pp. 1–6 (January 2000)Google Scholar
  3. 3.
    Intanagonwiwat, C., Estrin, D., Govindan, R., Heidemann, J.: Impact of Network Density on Data Aggregation in Wireless Sensor Networks. Technical Report 01-750, University of Southern California (November 2001)Google Scholar
  4. 4.
    Krishnamachari, B., Estrin, D., Wicker, S.: The Impact of Data Aggregation in Wireless Sensor Networks. In: International Workshop of Distributed Event Based Systems (DEBS), Vienna, Austria, pp. 575–578 (July 2002)Google Scholar
  5. 5.
    Akyildiz, I., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless Sensor Networks: A Survey. Computer Networks 38, 393–422 (2002)CrossRefGoogle Scholar
  6. 6.
    Gandham, S.R., Dawande, M., Prakash, R., Venkatesan, S.: Energy Efficient Schemes for Wireless Sensor Networks with Multiple Mobile base stations. In: Global Telecommunications Conference, pp. 377–381 (December 2003)Google Scholar
  7. 7.
    Al-Karaki, J.N., Ul-Mustafa, R., Kamal, A.E.: Data Aggregation in Wireless Sensor Networks - Exact and Approximate Algorithms. In: The Proceedings the International Workshop on High-Performance Switching and Routing, Phoenix, pp. 241–245 (April 2004)Google Scholar
  8. 8.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Computer Engineering Department, Erciyes University, Turkey (2005)Google Scholar
  9. 9.
    Misra, R., Mandal, C.: Ant-aggregation: Ant Colony Algorithm for optimal data aggregation in Wireless Sensor Networks. In: IFIP International Conference on Wireless and Optical Communications Networks, 5pages (2006)Google Scholar
  10. 10.
    Basturk, B., Karaboga, D.: An artificial bee colony (ABC) algorithm for numeric function optimization. In: Proceedings of the IEEE Swarm Intelligence Symposium, pp. 12–14 (May 2006)Google Scholar
  11. 11.
    Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 459–471 (2007)Google Scholar
  12. 12.
    Liao, W.-H., Kao, Y., Fan, C.-M.: An Ant Colony Algorithm for Data Aggregation in Wireless Sensor Networks. In: International Conference on Sensor Technologies and Applications, pp. 101–106 (2007)Google Scholar
  13. 13.
    Karaboga, D., Basturk, B.: On the performance of artificial bee colony(ABC) algorithm. Applied Soft Computing, 687–697 (2008)Google Scholar
  14. 14.
    Udgata, S.K., Sabat, S.L., Mini, S.: Sensor deployment in irregular terrain using artificial bee colony algorithm. In: World Congress on Nature and Biologically Inspired Computing, pp. 1308–1313 (2009)Google Scholar
  15. 15.
    Sabat, S.L., Kumar, K.S., Udgata, S.K.: Differential evolution and swarm intelligence techniques for analog circuit synthesis. In: World Congress on Nature and Biologically Inspired Computing, pp. 468–473 (2009)Google Scholar
  16. 16.
    Karim, L., Nasser, N., Abdulsalam, H., Moukadem, I.: An Efficient Data Aggregation Approach for Large Scale Wireless Sensor Networks. In: IEEE Global Telecommunications Conference, pp. 1–6 (2010)Google Scholar
  17. 17.
    Mini, S., Udgata, S.K., Sabat, S.L.: Sensor deployment in 3-D terrain using artificial bee colony algorithm. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds.) SEMCCO 2010. LNCS, vol. 6466, pp. 424–431. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Sabat, S.L., Udgata, S.K., Abraham, A.: Artificial bee colony algorithm for small signal model parameter extraction of MESFET. Eng Appl. Artif. Intell., 689–694 (2010)Google Scholar
  19. 19.
    Muhammad, S., Di Caro, G.A., Farooq, M.: Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions. In: Information Sciences, vol. 181, pp. 4597–4624. Elsevier (2011)Google Scholar
  20. 20.
    Okdem, S., Karaboga, D., Ozturk, C.: An Application of Wireless Sensor Network Routing based on Artificial Bee Colony Algorithm. In: IEEE Congress on Evolutionary Computation (CEC), pp. 326–330 (2011)Google Scholar
  21. 21.
    Mini, S., Udgata, S.K., Sabat, S.L.: Artificial bee colony based sensor deployment algorithm for target coverage problem in 3-D terrain. In: Natarajan, R., Ojo, A. (eds.) ICDCIT 2011. LNCS, vol. 6536, pp. 313–324. Springer, Heidelberg (2011)CrossRefGoogle Scholar

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© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Nagarjuna Reddy Busireddy
    • 1
  • Siba K. Udgata
    • 1
  1. 1.School of Computer and Information SciencesUniversity of HyderabadIndia

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