access icon free Survey of data aggregation techniques using soft computing in wireless sensor networks

© The Institution of Engineering and Technology
Received 22/09/2012, Accepted 17/12/2012, Revised 15/12/2012, Published

In wireless sensor networks (WSN), data aggregation using soft computing methods is a challenging issue because of the security factors. When a node is compromised, it is easy for an adversary to inject false data and mislead the aggregator to accept false readings. Therefore there is a need for secure data aggregation. Although sufficient works on the survey of data aggregation in WSNs are done, it seems less satisfactory in terms of maintaining a secured data aggregation, and measuring accurate values. This study presents an up to date survey of major contributions to the security solutions in data aggregation which mainly use soft computing techniques. Here, classification of protocols is done according to the soft computing technique as: fuzzy logic, swarm intelligence, genetic algorithm and neural networks. Accuracy, energy consumption, cost reduction and security measures are the metrics used for the classification. Finally, the authors provide a comparative study of all aggregation techniques.

Inspec keywords: wireless sensor networks; data handling; uncertainty handling; telecommunication security; telecommunication computing

Other keywords: data aggregation techniques; data aggregation security; swarm intelligence; neural networks; wireless sensor networks; fuzzy logic; WSN; security factors; genetic algorithm; soft computing methods

Subjects: Communications computing; Knowledge engineering techniques; Wireless sensor networks; Data handling techniques

References

    1. 1)
      • 14. Jalil, M., Chaturvedi, A.: ‘Robust energy management routing in WSN using neural networks’, Int. J. Adv. Netw. Appl., 2011, 2, (4), pp. 788–791.
    2. 2)
      • 9. Gowrishankar, S., Basavaraju, T., Manjaiah, D., Sarkar, S.: ‘Issues in wireless sensor networks’. Proc. World Congress on Engineering (WCE'08), 2008, vol. 1, pp. 176–187.
    3. 3)
      • 15. Handayani, P.: ‘Data aggregation in wireless sensor networks using fuzzy logic’, Universitas Pelita Harapan, Seminar Ilmiah Ilmu Komputer Nasional, (SILICON), 2006, 1, pp. 162–203.
    4. 4)
      • 8. Renner, B.: ‘Energy-efficient TDMA schedules for data-gathering in wireless sensor networks’. Technical Report, Institute of Telematics Hamburg University of Technology, (TUHH), 2008.
    5. 5)
      • 24. Réda, A., Aoued, B.: ‘Artificial neural network-based face recognition’. IEEE First Int. Symp. on Control, Communications and Signal Processing, 2004, pp. 439–442.
    6. 6)
      • 27. Jakhar, N., Nandal, R.: ‘A secure data aggregation approach in WSN Using ANN’, Int. J. Res. Eng. Appl. Sci., 2012, 2, (2), pp. 2249–3905.
    7. 7)
      • 4. Patil, N., Patil, P.: ‘Data aggregation in wireless sensor network’. IEEE Int. Conf. on Computational Intelligence and Computing Research, 2010.
    8. 8)
      • 25. Jaleel, U., Chaturvedi, A.: ‘GHHECR protocol for energy conserving routing in sensor networks’, Int. J. Comput. Appl., 2011, 13, (3), pp. 1–4.
    9. 9)
      • 6. Mohanty, P., Panigrahi, S., Sarma, N., Satapathy, S.: ‘Security issues in wireless sensor network data gathering protocols: a survey’, J. Theor. Appl. Inf. Technol., 2010, 13, pp. 14–27.
    10. 10)
      • 26. Kumar, N., Kumar, M., Patel, R.: ‘Coverage and connectivity aware neural network based energy efficient routing in wireless sensor networks’, Int. J. Appl. Graph Theory Wirel. Ad Hoc Netw. Sens. Netw., 2010, 2, pp. 45–49 (doi: 10.5121/jgraphhoc.2010.2105).
    11. 11)
      • 32. Al-Karaki, J., Ul-Mustafa, R., Kamal, E.A.: ‘Data aggregation and routing in wireless sensor networks: optimal and heuristic algorithms’, Int. J. Comput. Telecommun. Netw., 2009, 53, (7), pp. 945–960 (doi: 10.1016/j.comnet.2008.12.001).
    12. 12)
      • 13. Islam, O., Hussain, S.: ‘Genetic algorithm for data aggregation trees in wireless sensor networks’. IEEE Third IET Int. Conf. on Intelligent Environments, 2007, pp. 312–316.
    13. 13)
      • 33. Norouzi, A., Babamir, F., Orman, Z.: ‘A tree based data aggregation scheme for wireless sensor networks using GA’, Sci. Res., Wirel. Sens. Netw., 2012, 4, pp. 191–196.
    14. 14)
      • 29. Shihab, K.: ‘A backpropagation neural network for computer network security’, J. Comput. Sci., Science Publ., 2006, 2, pp. 710–715.
    15. 15)
      • 21. Selvakennedy, S., Sinnappan, S., Shang, Y.: ‘T-ANT: a nature-inspired data gathering protocol for wireless sensor networks’, J. Commun., 2006, 1, (2), pp. 22–29.
    16. 16)
      • 16. Croce, S., Marcelloni, F., Vecchio, M.: ‘Reducing power consumption in wireless sensor networks using a novel approach to data aggregation’, Comput. J., 2008, 51, (2), pp. 227–239 (doi: 10.1093/comjnl/bxm046).
    17. 17)
      • 23. Abraham, A.: ‘Artificial neural networks’. Oklahoma State University, Stillwater, OK, USA, DOI: 10.1002/0471497398.mm421, ISBN: 9780471497394, Presented in book: Handbook of Measuring System Design, 2005.
    18. 18)
      • 7. Ramamohanarao, K., Kulik, L., Selvadurai, S., et al.: ‘A survey on data processing issues in wireless sensor networks for enterprise information infrastructure’. EII Network Taskforce on Wireless Sensor Networks, Survey Technical Report, 2006.
    19. 19)
      • 18. Guo, Y., Hong, F., Guo, Z., Jin, Z., Feng, Y.: ‘EDA: event-oriented data aggregation in sensor networks’. IEEE 28th Int. Conf. on Performance Computing and Communications Conf., 2010, pp. 25–32.
    20. 20)
      • 10. Valls, A., Torra, V., Domingo-Ferrer, J.: ‘Aggregation methods to evaluate multiple protected versions of the same confidential data set’. First Int. Workshop on Soft Methods in Probability and Statistics, 2002, pp. 355–362.
    21. 21)
      • 2. Huang, Q., Liu, X., Guo, C.: ‘Reliable aggregation routing for wireless sensor networks based on game theory’, in Huang, Q. (Ed.): ‘Game theory’, InTech, ISBN:978-953-307-132-9, http://www.intechopen.com/books/game-theory/reliable-aggregation-routing-for-wireless-sensor-networks-based-on-game-theory, 2010, pp. 59–82.
    22. 22)
      • 12. Su, W., Bougiouklis, T.: ‘Data fusion algorithms in cluster-based wireless sensor networks using fuzzy logic theory’. Proc. 11th WSEAS Int. Conf. on Communications, Agios Nikolaos, Crete Island, Greece, July 2007, pp. 26–28.
    23. 23)
      • 30. Chakraborthy, R.: ‘Basics of soft computing’. Soft Computing Course Lecture, www.myreaders.info/html/soft_computing.html, 2010.
    24. 24)
      • 17. De Cristofaro, E., Bohli, J., Westhoff, D.: ‘FAIR: fuzzy-based aggregation providing in-network resilience for real-time wireless sensor networks’. Proc. of the Second ACM Conf. on Wireless Network Security (WiSec'09), 2009, vol. 16, pp. 253–260.
    25. 25)
      • 22. Juan, L., Chen, S., Chao, Z.: ‘Ant system based anycast routing in wireless sensor networks’. IEEE Int. Conf. on Wireless Communications, Networking and Mobile Computing, (WiCom'07), September 2007, pp. 2420–2423.
    26. 26)
      • 5. Watfa, M., Daher, W., Azar, H.: ‘A sensor network data aggregation technique’, Int. J. Comput. Theory Eng., 2009, 1, pp. 19–26 (doi: 10.7763/IJCTE.2009.V1.4).
    27. 27)
      • 28. Darougaran, L., Shahinzadeh, H., Ghotb, H., Ramezanpour, L.: ‘Simulated annealing algorithm for data aggregation trees in wireless sensor networks and comparison with genetic algorithm’, Int. J. Electron. Electr. Eng., 2012, 62, pp. 59–62.
    28. 28)
      • 3. Zhao, J., Erdogan, A., Arslan, T.: ‘A novel application specific network protocol for wireless sensor networks’. IEEE Int. Symp. on Circuits and Systems (ISCAS'05), 2005, vol. 6, pp. 5894–5897.
    29. 29)
      • 11. Yang, J., Zhao, W., Xu, M., Xu, B.: ‘A multipath routing protocol based on clustering and ant colony optimization for wireless sensor networks’, Int. J. Comput. Netw. Inf. Secur., 2009, 1, pp. 49–59.
    30. 30)
      • 31. Mehrjoo, S., Aghaee, H., Karimi, H.: ‘Novel hybrid GA-ABC based energy efficient clustering in wireless sensor network’, Can. J. Multimedia Wirel. Netw., 2011, 2, (2), pp. 41–45.
    31. 31)
      • 34. Rajagopalan, R., Varshney, P.: ‘Data aggregation techniques in sensor networks: a survey’, IEEE Commun. Surv. Tutor., 2006, 8, (4), pp. 48–63 (doi: 10.1109/COMST.2006.283821).
    32. 32)
      • 19. Huang, S.: ‘Enhancement of hydroelectric generation scheduling using ant colony system based optimization approaches’, IEEE Trans. Energy Convers., 2001, 16, (3), pp. 296–301 (doi: 10.1109/60.937211).
    33. 33)
      • 20. Nagarajan, V.: ‘Anycast routing in wireless sensor networks’, Int. J. Eng. Sci. Technol., 2010, 2, pp. 878–885.
    34. 34)
      • 1. Maraiya, K., Kant, K., Gupta, N.: ‘Architectural based data aggregation techniques in wireless sensor network: a comparative study’, Int. J. Comput. Sci. Eng. (IJCSE), 2011, 3, pp. 1131–1138.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ifs.2012.0292
Loading

Related content

content/journals/10.1049/iet-ifs.2012.0292
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading