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Energy-efficient data aggregation and transfer in periodic sensor networks

Energy-efficient data aggregation and transfer in periodic sensor networks

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Limited battery power and high transmission energy consumption in wireless sensor networks make in-network aggregation and prediction a challenging area for researchers. The most energy consumable operation is transmitting data by a sensor node, comparing it with the energy consumption of in-network computation which is negligible. The energy trade-off between communication and computation provides applications benefit when processing the data at the network side rather than simply transmitting sensor data. In this study, the authors consider a cluster-based technique with which data is sent periodically from sensor nodes to their appropriate cluster-heads (CH). The proposed technique manages energy efficiency in periodic sensor network and it consists of two phases: ‘aggregation phase and adaptation phase’. The aggregation phase is used to find similarities between data (measurements captured during a period p) in order to eliminate redundancy from raw data, thus reducing the amount of data-sets sent to the CH. The adaptation phase provides sensors the ability to identify duplicate data-sets captured among successive periods, using the sets-similarity joins functions. To evaluate the performance of the proposed technique, experiments on real sensor data have been conducted. Results show that the proposed technique is effective in term of energy consumption and quality of data.

References

    1. 1)
      • 1. Bahi, J.M., Makhoul, A., Medlej, M.: ‘A two tiers data aggregation scheme for periodic sensor networks’, Ad Hoc Sensor Wirel. Netw., 2014, 21, (1-2), pp. 77100.
    2. 2)
    3. 3)
      • 3. Dagar, M., Mahajan, S.: ‘Data aggregation in wireless sensor network: a survey’, Int. J. Inf. Comput. Technol. (IJICT), 2013, 3, (3), pp. 167174.
    4. 4)
      • 4. Mishra, S., Thakkar, H.: ‘Features of wsn and data aggregation techniques in wsn: a survey’, Int. J. Eng. Innov. Technol. (IJEIT), 2012, 1, (4), pp. 264273.
    5. 5)
      • 5. Kumar, D.: ‘Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks’, IET Wirel. Sensor Syst., 2014, 4, (1), pp. 916.
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
      • 10. Shah, R.C., Rabaey, J.M.: ‘Energy aware routing for low energy ad hoc sensor networks’. IEEE Wireless Communications and Networking Conf. (WCNC2002), 2002, pp. 350355.
    11. 11)
    12. 12)
      • 12. Prakash, G.L., Thejaswini, M., Manjula, S.H., Venugopal, K.R., Patnaik, L.M.: ‘Tree-on dag for data aggregation in sensor networks’. Proc. of World Academy of Science, Engineering and Technology, 2009, 37.
    13. 13)
      • 13. Zheng, Y., Chen, K., Qiu, W.: ‘Building representative based data aggregation tree in wireless sensor networks’, Math. Probl. Eng., 2010, 2010, p. 11.
    14. 14)
    15. 15)
      • 15. Bahi, J.M., Makhoul, A., Medlej, M.: ‘Energy efficient in-sensor data cleaning for mining frequent itemsets’, Sensors Transducers J., 2012, 14, (2), pp. 6478.
    16. 16)
      • 16. Bahi, J.M., Makhoul, A., Medlej, M.: ‘An optimized in-network aggregation scheme for data collection in periodic sensor networks’. Proc. of the 11th Int. Conf. on Ad Hoc Networks and Wireless (ADHOC-NOW 2012), Belgrade, Serbia, July 2012, pp. 153166.
    17. 17)
      • 17. Bahi, J.M., Makhoul, A., Medlej, M.: ‘Energy efficient 2-tiers weighted in-sensor data cleaning’. Proc. of 5th Int. Conf. on Sensor Technologies and Applications (SENSORCOMM11), Nice, France, August 2011, pp. 197202.
    18. 18)
      • 18. Bahi, J.M., Makhoul, A., Medlej, M.: ‘Data aggregation for periodic sensor networks using sets similarity functions’. Seventh IEEE Int. Wireless Communications and Mobile Computing Conf. (IWCMC 2011), Istanbul, Turkey, July 2011, pp. 559564.
    19. 19)
      • 19. Henzinger, M.: ‘Finding near-duplicate web pages: a large-scale evaluation of algorithms’. Proc. of the 29-th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, Seattle, WA, USA, 2006, pp. 284291.
    20. 20)
    21. 21)
    22. 22)
      • 22. Bayardo, R.J., Ma, Y., Srikant, R.: ‘Scaling up all pairs similarity search’. Proc. of the 16th Int. Conf. on World Wide Web (WWW07), Banff, Canada, 2007, pp. 131140.
    23. 23)
      • 23. Arasu, A., Ganti, V., Kaushik, R.: ‘Efficient exact set-similarity joins’. Proc. of the 32nd Int. Conf. on Very Large Data Bases (VLDB 2006), Seoul, Korea, 2006, pp. 918929.
    24. 24)
      • 24. Madden, S.: ‘Intel Berkeley Research lab’. Available at http://db.csail.mit.edu/labdata/labdata.html, 2004.
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