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Data Aggregation Using Dynamic Selection of Aggregation Points Based on RSSI for Wireless Sensor Networks

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Abstract

In wireless sensor networks (WSNs), due to dense deployment, sensory data gathered by sensor nodes in close proximity tend to exhibit high correlation and therefore redundant. Transmitting such redundant data is not practical in the energy-constrained WSNs. Data aggregation offers a key solution to reduce such redundancy by allowing intermediate nodes to aggregate raw data streams before routing them toward a sink node. This in turn reduces transmission energy consumption. Prior work in data aggregation often rely on node’s location for selecting an aggregator node, a fusion point. In this work, we propose two data aggregation mechanisms where aggregator nodes are determined opportunistically without dependency on global knowledge of data flow, network topology and nodes’ geographical location. These mechanisms aggregate and route data packets based on Received Signal Strength Indicator (RSSI). An aggregation identification (Agg_ID) is associated with each data packet generated by a sensor node. The RSSI and Agg_ID are used in the RSSI-Based Fowarding for favoring nodes closer to sink to be an aggregator and also a relay node. We show via simulation the performance of the proposed mechanisms in terms of normalized number of transmissions, total number of packets transmissions and receptions, average energy consumed per data packet, network lifetime, end-to-end delay and packet loss probability.

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Notes

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    Choice of aggregation function depends on application under consideration. Some common aggregation function are summation (SUM), average (AVG), maximum (MAX), minimum (MIN), COUNT. Detailed discussion about aggregation function can be found in [9, 14, 24, 27].

References

  1. 1.

    (2013). CC2420 Data Sheet. http://focus.ti.com/docs/prod/folders/print/cc2420.html. Accessed April 12, 2013.

  2. 2.

    (2013). Wireless sensor networks simulator. http://castalia.research.nicta.com.au/index.php/en/. Accessed April 12, 2013.

  3. 3.

    Akyildiz, I., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002a). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.

  4. 4.

    Akyildiz, I., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002b). Wireless sensor networks: A survey. Computer Networks (Elsevier), 38(4), 393–422.

  5. 5.

    Awang, A. (2011). A cross-layer MAC/routing protocol for wireless sensor networks. PhD thesis, Institut TÉLÉCOM / TÉLÉCOM Bretagne, France.

  6. 6.

    Awang, A., Lagrange, X., & Ros, D. (2009). RSSI-based forwarding for multihop wireless sensor networks. In 15th Open European summer school and IFIP TC6.6 workshop (EUNICE2009). The internet of the future, LNCS (Vol. 5733, pp. 138–147).

  7. 7.

    Braginsky, D., & Estrin, D. (2002). Rumor routing algorithm for sensor networks. In 1st ACM international workshop on wireless sensor networks and applications (WSNA) (pp. 22–31). doi:10.1145/570738.570742.

  8. 8.

    Bulusu, N., Heidemann, J., Estrin, D., & Tran, T. (2004). Self-configuring localization systems: Design and experimental evaluation. ACM Transactions on Embedded Computing Systems, 3(1), 24–60.

  9. 9.

    Cohen, E., & Kaplan, H. (2004). Spatially-decaying aggregation over a network: Model and algorithms. In ACM SIGMOD international conference on management of data (pp. 707–718).

  10. 10.

    Culler, D., Estrin, D., & Strivastava, M. (2004). Overview of sensor networks. In IEEE Computer Society’04 (pp. 41–49).

  11. 11.

    Ding, M., Cheng, X., & Xue, G. (2003) Aggregation tree construction in sensor networks. In IEEE 58th vehicular technology conference (pp. 2168–2172).

  12. 12.

    Fan, K. W., Liu, S., & Sinha, P. (2007). Structure-free data aggregation in sensor networks. IEEE Transactions on Mobile Computing, 6(8), 929–942.

  13. 13.

    Fan, K. W., Liu, S., & Sinha, P. (2008). Dynamic forwarding over tree-on-DAG for scalable data aggregation in sensor networks. IEEE Transactions on Mobile Computing, 7(10), 1271–1284.

  14. 14.

    Fasolo, E., Rossi, M., Widmer, J., & Zorzi, M. (2007). In-network aggregation techniques for wireless sensor networks: A survey. IEEE Wireless Communications Magazine, 14(2), 70–87.

  15. 15.

    Guo, W., Xiong, N., Vasilakos, A., Chen, G., & Cheng, H. (2011). Multi-source temporal data aggregation in wireless sensor networks. Wireless Personal Communications, 56(3), 359–370.

  16. 16.

    Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.

  17. 17.

    Intanagonwiwat, C., Govindan, R., Estrin, D., Heidemann, J., & Silva, F. (2003). Directed diffusion for wireless sensor networking. IEEE/ACM Transactions on Networking, 11(1), 2–16.

  18. 18.

    Karl, H., & Willig, A. (2005). Protocols and architectures for wireless sensor networks. New York: Wiley.

  19. 19.

    Kulik, J., Heinzelman, W., & Balakrishnan, H. (2002). Negotiation-based protocols for disseminating information in wireless sensor networks. Wireless Networks, 8(2/3), 169–185.

  20. 20.

    Lindsey, S., Raghavendra, C., & Sivalingam, K. (2002). Data gathering algorithms in sensor networks using energy metrics. IEEE Transactions on Parallel and Distributed Systems, 13(9), 924–935.

  21. 21.

    Liu, Y., Yang, Z., Wang, X., & Jian, L. (2010). Location, localization, and localizability. Journal of Computer Science and Technology, 25(2), 274–297.

  22. 22.

    Luo, H., Liu, Y., & Das, S. (2007). Routing correlated data in wireless sensor networks: A survey. IEEE Networks, 21(6), 40–47.

  23. 23.

    Madden, S., Franklin, M. J., Hellerstein, J. M., & Hong, W. (2002). TAG: A Tiny AGgregation service for ad-hoc sensor networks. ACM SIGOPS Operating Systems Review, 36(SI), 131–146.

  24. 24.

    Nath, S., Gibbons, P., Seshan, S., & Anderson, Z. (2004). Synopsis diffusion for robust aggregation in sensor networks. In 2nd international conference on embedded networked sensor systems (pp. 250–262).

  25. 25.

    Qin, M., & Zimmermann, R. (2007). VCA: An energy-efficient voting-based clustering algorithm for sensor networks. Journal of Universal Computer Science, 13(1), 87–109.

  26. 26.

    Rajagopalan, R., & Varshney, P. (2006). Data-aggregation techniques in sensor networks: A survey. IEEE Communications Surveys and Tutorials, 8(4), 48–63.

  27. 27.

    Sharaf, A., Beaver, J., Labrinidis, A., & Chrysanthis, K. (2004). Balancing energy efficiency and quality of aggregated data in sensor networks. The VLDB Journal, 13(4), 384–403.

  28. 28.

    Tan, H. O., & Körpeoǧlu, I. (2003). Power efficient data gathering and aggregation in wireless sensor networks. ACM Newsletter SIGMOD Record, 32(4), 66–71.

  29. 29.

    Tanenbaum, A. (2002). Computer networks (4th ed.). Upper Saddle River: Prentice Hall Professional Technical Reference.

  30. 30.

    Villas, L., Guidoni, D., Boukerche, A., Araujo, R., & Loureiro, A. A. F. (2011). Dynamic and scalable routing to perform efficient data aggregation in WSNs. In IEEE international conference on communications (ICC) (pp. 1–5).

  31. 31.

    Wang, Q., & Zhang, T. (2009). Bottleneck zone analysis in energy-constrained wireless sensor networks. IEEE Communications Letters, 13(6), 423–425.

  32. 32.

    Wei, G., Ling, Y., Guo, B., Xiao, B., & Vasilakos, A. V. (2011). Prediction-based data aggregation in wireless sensor networks: Combining grey model and Kalman filter. Computer Communications, 34(6), 793–802.

  33. 33.

    Xiangning, F., & Yulin, S. (2007). Improvement on LEACH protocol of wireless sensor network. In International conference on sensor technologies and applications (pp. 260–264).

  34. 34.

    Yassein, M., Al-Zou’bi, A., Khamayseh, Y., & Mardini, W. (2009). Improvement on LEACH protocol of wireless sensor networks (VLEACH). International Journal of Digital Content Technology and its Applications, 3(2), 132–136.

  35. 35.

    Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.

  36. 36.

    Zorzi, M., & Rao, R. (2003). Geographic random forwarding (GeRaF) for ad hoc and sensor networks: Energy and latency performance. IEEE Transactions on Mobile Computing, 2(4), 349–365.

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Acknowledgments

This work has been supported by Universiti Teknologi PETRONAS, Malaysia under the Short Term Internal Research (STIRF) grant (STIRF Code No: 64/2011).

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Correspondence to Azlan Awang.

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Awang, A., Agarwal, S. Data Aggregation Using Dynamic Selection of Aggregation Points Based on RSSI for Wireless Sensor Networks. Wireless Pers Commun 80, 611–633 (2015). https://doi.org/10.1007/s11277-014-2031-5

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Keywords

  • RSSI
  • Data aggregation
  • CTS response
  • Structure-free
  • Cross-layer
  • Network lifetime