Compressed data-stream protocol: an energy-efficient compressed data-stream protocol for wireless sensor networks

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Compressed data-stream protocol: an energy-efficient compressed data-stream protocol for wireless sensor networks

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In this study, the authors present an energy-efficient data compression protocol for data collection in wireless sensor networks (WSNs). WSNs are essentially constrained by motes’ limited battery power and networks bandwidth. The authors focus on data compression algorithms and protocol development to effectively support data compression for data gathering in WSNs. Their design of compressed data-stream protocol (CDP) is generic in the sense that other lossless or lossy compression algorithms can be easily ‘plugged’ into the proposed protocol system without any changes to the rest of the CDP. This design intends to support various different WSN applications where users may prefer more specific compression algorithms, tailored to the sensing data characteristics in question, to their general algorithm. CDP is not only able to significantly reduce energy consumptions of data gathering in multi-hop WSNs, but also able to reduce sensor network traffic and thus avoid congestion accordingly. The proposed CDP is implemented on the tinyOS platform using the nesC programming language. To evaluate their work, the authors conduct simulations via TOSSIM and PowerTOSSIM-z with real-world sensor data. The results demonstrate the significance of CDP.

Inspec keywords: wireless sensor networks; protocols; energy conservation; data compression

Other keywords: PowerTOSSIM-z; data collection; lossless compression; energy-efficient compressed data-stream protocol; energy-efficient data compression protocol; sensor network traffic; lossy compression; wireless sensor networks; protocol development; multihop WSN; nesC programming language

Subjects: Protocols; Wireless sensor networks

References

    1. 1)
      • Paek, J., Chintalapudi, K., Cafferey, J., Govindan, R., Masri, S.: `A wireless sensor network for structural health monitoring: performance and experience', Proc. Second IEEE Workshop Embedded Networked Sensors, May 2005, p. 1–10.
    2. 2)
      • Y. Liang , W. Peng . Minimizing energy consumptions in wireless sensor networks via two-modal transmission. ACM SIGACOMM Comput. Commun. Rev. , 1 , 13 - 18
    3. 3)
      • Perla, E., O Cathain, A., Carbajo, R.S., Huggard, M., Mc Goldrick, C.: `PowerTOSSIM z: realistic energy modelling for wireless sensor network environments', Proc. Third ACM Workshop Performance Monitoring and Measurement of Heterogeneous Wireless and Wired Networks, 2008, p. 35–42.
    4. 4)
      • Werner-Allen, G., Lorincz, K., Johnson, J., Lees, J., Welsh, M.: `Fidelity and yield in a volcano monitoring sensor network', Proc. ACM Symp. Operating System Design and Implementation, 2006, p. 381–396.
    5. 5)
      • Levis, P., Lee, N., Welsh, M., Culler, D.: `TOSSIM: accurate and scalable simulation of entire TinyOS applications', Proc. First Int. Conf. Embedded Networked Sensor Systems, November 2003, Los Angeles, CA, USA, p. 126–137.
    6. 6)
      • Gnawali, O., Fonseca, R., Jamieson, K., Moss, D., Levis, P.: `Collection tree protocol', Proc. Seventh ACM Conf. on Embedded Network Sensor Systems, 2009, p. 1–14.
    7. 7)
    8. 8)
      • Gay, D., Levis, P., Behren, R., Welsh, M., Brewer, E., Culler, D.: `The nesC language: a holistic approach to networked embedded systems', Proc. ACM SIGPLAN 2003 Conf. Programming Language Design and Implementation, 2003, p. 1–11.
    9. 9)
      • Kim, S., Pakzad, S., Culler, D.: `Health monitoring of civil infrastructures using wireless sensor networks', Proc. Sixth Int. Conf. Information Processing in Sensor Networks, April 2007, Cambridge, MA, USA, p. 254–263.
    10. 10)
      • Schoellhammer, T., Osterweil, E., Greenstein, B., Wimbrow, M., Estrin, D.: `Lightweight temporal compression of microclimate datasets', Proc. 29th Annual IEEE Int. Conf. Local Computer Networks, 2004, p. 516–524.
    11. 11)
      • http://sensorscope.epfl.ch/index.php/Main_Page, accessed July 2011.
    12. 12)
    13. 13)
    14. 14)
    15. 15)
      • Huang, F., Liang, Y.: `Towards energy optimization in environmental wireless sensor networks for lossless and reliable data gathering', IEEE Int. Conf. Mobile Ad hoc and Sensor Systems (MASS), October 2007, Pisa, Italy, p. 1–6.
    16. 16)
      • , : `TinyOS 2.0', Proc. Third Int. Conf. Embedded Networked Sensor Systems, November 2005, San Diego, CA, USA.
    17. 17)
    18. 18)
    19. 19)
      • Kim, S., Fonseca, R., Dutta, P.: `Flush: a reliable bulk transport protocol for multihop wireless networks', Proc. Fifth Int. Conf. Embedded Networked Sensor Systems, November 2007, Sydney, Australia, p. 351–365.
    20. 20)
    21. 21)
      • R. Fonseca , O. Gnawali , K. Jamieson , S. Kim , P. Levis , A. Woo . The collection tree protocol.
    22. 22)
      • Shnayder, V., Hempstead, M., Chen, B., Allen, G.W., Welsh, M.: `Simulating the power consumption of large-scale sensor network applications', Proc. Second Int. Conf. Embedded Networked Sensor Systems, November 2004, Baltimore, MD, USA, p. 188–200.
    23. 23)
      • Sadler, C.M., Martonosi, M.: `Data compression algorithms for energy-constrained devices in delay tolerant networks', Proc. Fourth ACM Int. Conf. Embedded Networked Sensor Systems, 2006, p. 265–278.
    24. 24)
      • Lee, H.J., Cerpa, A., Levis, P.: `Improving wireless simulation through noise modelling', Proc. Sixth Int. Conf. Information Processing in Sensor Networks, April 2007, Cambridge, MA, USA, p. 21–30.
    25. 25)
      • Liang, Y.: `Efficient temporal compression in wireless sensor networks', 36thIEEE Conf. Local Computer Networks (LCN), 2011, 470–478.
    26. 26)
      • Hull, B., Jamieson, K., Balakrishnan, H.: `Mitigating congestion in wireless sensor networks', Proc. Second Int. Conf. Embedded Networked Sensor Systems, November 2004, Baltimore, MD, USA, p. 134–137.
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