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A new lossy compression algorithm for wireless sensor networks using Bayesian predictive coding

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Abstract

Wireless sensor networks (WSNs) generate a variety of continuous data streams. To reduce data storage and transmission cost, compression is recommended to be applied to the data streams from every single sensor node. Local compression falls into two categories: lossless and lossy. Lossy compression techniques are generally preferable for sensors in commercial nodes than the lossless ones as they provide a better compression ratio at a lower computational cost. However, the traditional approaches for data compression in WSNs are sensitive to sensor accuracy. They are less efficient when there are abnormal and faulty measurements or missing data. This paper proposes a new lossy compression approach using the Bayesian predictive coding (BPC). Instead of the original signals, predictive coding transmits the error terms which are calculated by subtracting the predicted signals from the actual signals to the receiving node. Its compression performance depends on the accuracy of the adopted prediction technique. BPC combines the Bayesian inference with the predictive coding. Prediction is made by the Bayesian inference instead of regression models as in traditional predictive coding. In this way, it can utilize prior information and provide inferences that are conditional on the data without reliance on asymptotic approximation. Experimental tests show that the BPC is the same efficient as the linear predictive coding when handling independent signals which follow a stationary probability distribution. More than that, the BPC is more robust toward occasionally erroneous or missing sensor data. The proposed approach is based on the physical knowledge of the phenomenon in applications. It can be considered as a complementary approach to the existing lossy compression family for WSNs.

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Abbreviations

CR:

The compression ratio

NSPR:

The peak-signal-to-noise ratio

\( e\left( i \right) \) :

The residual

\( \hat{e}\left( i \right) \) :

The predicted residual

\( \left[ {\hat{e}\left( i \right)} \right] \) :

The approximated predicted residual

\( x\left( i \right) \) :

The signal

\( \left[ {x\left( i \right)} \right] \) :

The approximated signal

\( r\left( i \right) \) :

The reconstruction

\( q\left( i \right) \) :

The quantization index

\( J \) :

The number of samples in one data block

\( m \) :

The width of quantization level

\( \varepsilon \) :

The error margin

References

  1. Akansu, A. N., Serdijn, W. A., & Selesnick, I. W. (2010). Emerging applications of wavelets: A review. Physical Communication, 3(1), 1–18.

    Article  Google Scholar 

  2. Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2016). Rate-distortion balanced data compression for wireless sensor networks. IEEE Sensors Journal, 16(12), 5072–5083.

    Article  Google Scholar 

  3. Banerjee, R., & Bit, S. D. (2017). An energy efficient image compression scheme for wireless multimedia sensor network using curve fitting technique. Wireless Networks, 25(1), 167–183.

    Article  Google Scholar 

  4. Buratti, C., Conti, A., Dardari, D., & Verdone, R. (2009). An overview on wireless sensor networks technology and evolution. Sensors, 9, 6869–6896.

    Article  Google Scholar 

  5. Capo-Chichi, E.P., Guyennet, H., Friedt, J.-M. (2009) K-RLE: A new data compression algorithm for wireless sensor network. In 3rd international conference on sensor technologies and applications, Athens, Glyfada, Greece.

  6. Chen, S., Liu, J., Wang, K., & Wu, M. (2019). A hierarchical adaptive spatio-temporal data compression scheme for wireless sensor networks. Wireless Networks, 25(1), 429–438.

    Article  Google Scholar 

  7. Chen, Z., Guiling, S., Weixiang, L., Yi, G., & Lequn, L. (2009). Research on data compression algorithm based on prediction coding for wireless sensor network nodes. International Forum on Information Technology and Applications, 1, 283–286.

    Google Scholar 

  8. Dan, L., Qian, Z., Zhi, Z., & Baoling, L. (2016). Cluster-based energy-efficient transmission using a new hybrid compressed sensing in WSN. IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2016, 372–376.

    Article  Google Scholar 

  9. Dang, T., Bulusu, N., & Feng, W.-C. (2013). Robust data compression for irregular wireless sensor networks using logical mapping. London: Hindawi Publishing Corporation.

    Book  Google Scholar 

  10. Davenport, M. A., Laska, J. N., Treichler, J. R., & Baraniuk, R. G. (2012). The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding versus Dynamic Range. IEEE Transactions on Signal Processing, 60(9), 4628–4642.

    Article  MathSciNet  MATH  Google Scholar 

  11. Deligiannakis, A., & Kotidis, Y. (2011). Detecting proximity events in sensor networks. Information Systems, 36(7), 1044–1063.

    Article  Google Scholar 

  12. Diamond, S. M., & Ceruti, M. G. (2007). Application of wireless sensor network to military information integration. In 2007 5th IEEE international conference on industrial informatics, Vienna, Austria, 2007.

  13. Dumas, T., Roumy, A., & Guillemot, C. (2020). Context-Adaptive Neural Network-Based Prediction for Image Compression. IEEE Transactions on Image Processing, 29, 679–693.

    Article  MathSciNet  Google Scholar 

  14. Fu, H., Liang, F., Lei, B., Bian, N., Zhang, Q., Akbari, M., et al. (2020). Improved hybrid layered image compression using deep learning and traditional codecs. Signal Processing: Image Communication, 82, 115774.

    Google Scholar 

  15. Hollmig, G., Horne, M., Leimkühler, S., Schöll, F., Strunk, C., Englhardt, A., et al. (2017). An evaluation of combinations of lossy compression and change-detection approaches for time-series data. Information Systems, 65, 65–77.

    Article  Google Scholar 

  16. Hosseini-Nejad, H., Jannesari, A., & Sodagar, A. M. (2012). Data compression based on discrete cosine transform for implantable neural recording microsystems. IEEE International Conference on Circuits and Systems (ICCAS), 2012, 209–213.

    Article  Google Scholar 

  17. Huffman, D. A. (1952). A method for the construction of minimum-redundancy codes. Proceedings of the IRE, 40(9), 1098–1101.

    Article  MATH  Google Scholar 

  18. Iqbal, S., Abdullah, A. H., Ahsan, F., & Qureshi, K. N. (2017). Critical link identification and prioritization using Bayesian theorem for dynamic channel assignment in wireless mesh networks. Wireless Networks, 24, 2685–2697.

    Article  Google Scholar 

  19. Karson, M. (2014). Handbook of methods of applied statistics. Volume I: Techniques of computation descriptive methods, and statistical inference. Volume II: Planning of surveys and experiments. I. M. Chakravarti, R. G. Laha, and J. Roy, New York, John Wiley; 1967, $9.00, Publications of the American Statistical Association 63(323), 1047–1049.

  20. Kaushik, C. S. H., Gautam, T., & Elamaran, V. (2014). A tutorial review on discrete fourier transform with data compression application. International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), 2014, 1–6.

    Google Scholar 

  21. Kemda, L. E., & Huang, C.-K. (2015). Value-at-risk for the USD/ZAR exchange rate: The Variance-Gamma model. South African Journal of Economic and Management Sciences, 18(4), 551–566.

    Article  Google Scholar 

  22. Kim, A., Han, J., Yu, T., & Kim, D. S. (2015). Hybrid wireless sensor network for building energy management systems based on the 2.4 GHz and 400 MHz bands. Information Systems, 48, 320–326.

    Article  Google Scholar 

  23. Li, Y., & Liang, Y. (2016). Temporal lossless and lossy compression in wireless sensor networks. ACM Transactions on Sensor Networks, 12, 37. https://doi.org/10.1145/2990196.

    Article  Google Scholar 

  24. Liu, L., & Shimamura, T. (2013). A noise compensation LPC method based on pitch synchronous analysis for speech. Journal of Signal Processing, 17(6), 283–292.

    Article  Google Scholar 

  25. Luo, C., Wu, F., Sun, J., & Chen, C.W. (2009). Compressive data gathering for large-scale wireless sensor networks. In Proceedings of the 15th annual international conference on mobile computing and networking, Beijing, China (pp. 145–156).

  26. Luo, J., Xiang, L., & Rosenberg, C. (2010). Does compressed sensing improve the throughput of wireless sensor networks?. In 2010 IEEE international conference on communications, IEEE, pp. 1–6.

  27. Makhoul, J. (1975). Linear prediction: A tutorial review. Proceedings of the IEEE, 63, 561–580.

    Article  Google Scholar 

  28. Marcelloni, F., & Vecchio, M. (2010). Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization. Information Sciences, 180, 1924–1941.

    Article  Google Scholar 

  29. Marcelloni, F., & Vecchio, M. (2010). A two-objective evolutionary approach to design lossy compression algorithms for tiny nodes of wireless sensor networks. Evolutionary Intelligence, 3, 137–153.

    Article  Google Scholar 

  30. Noel, A. B., & Abdaoui, A. (2017). Structural health monitoring using wireless sensor networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 19(3), 1403–1423.

    Article  Google Scholar 

  31. Pan, Y., Zhang, L., Wu, X., Zhang, K., & Skibniewski, M. J. (2019). Structural health monitoring and assessment using wavelet packet energy spectrum. Safety Science, 120, 652–665.

    Article  Google Scholar 

  32. Rajarshi Middya, N. C. M. K. N. (2017). Compressive sensing in wireless sensor networks - a survey. IETE Technical Review, 34(6), 642–654.

    Article  Google Scholar 

  33. Razavi, S.A., Ollila, E., & Koivunen, V. (2012). Robust greedy algorithms for compressed sensing. In 2012 Proceedings of the 20th European signal processing conference (EUSIPCO) (pp. 969–973).

  34. Rissanen, J., & Langdon, G. G. (1979). Arithmetic coding. IBM Journal of Research and Development, 23(2), 149–162.

    Article  MathSciNet  MATH  Google Scholar 

  35. Schoellhammer, T., Greenstein, B., Osterweil, E., Wimbrow, M. & Estrin, D. (2004). Lightweight temporal compression of microclimate datasets. In 29th annual IEEE international conference on local computer networks, Tampa, FL, USA.

  36. Sharma, M. (2010). Compression using Huffman coding. International Journal of Computer Science and Network Security, 10(5), 133–141.

    Google Scholar 

  37. Sheltami, T., Musaddiq, M., & Shakshuki, E. (2016). Data compression techniques in wireless sensor networks. Future Generation Computer Systems, 64, 151–162.

    Article  Google Scholar 

  38. Srisooksai, T., Keamarungsi, K., Lamsrichan, P., & Araki, K. (2012). Practical data compression in wireless sensor networks: A survey. Journal of Network and Computer Applications, 35, 37–59.

    Article  Google Scholar 

  39. Tandon, P., Huggins, P., Maclachlan, R., Dubrawski, A., Nelson, K., & Labov, S. (2016). Detection of radioactive sources in urban scenes using Bayesian aggregation of data from mobile spectrometers. Information Systems, 57, 195–206.

    Article  Google Scholar 

  40. Uthayakumar, J., Vengattaraman, T., & Dhavachelvan, P. (2018). A survey on data compression techniques: From the perspective of data quality, coding schemes, data type and applications. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2018.05.006.

  41. Welch, A. (1984). Technique for high-performance data compression. Computer, 17(6), 8–19.

    Article  Google Scholar 

  42. Wheeler, A. (2007). Commercial applications of wireless sensor networks using ZigBee. IEEE Communications Magazine, 45(4), 70–77.

    Article  Google Scholar 

  43. Wu, X., Liu, H., Zhang, L., Skibniewski, M. J., Deng, Q., & Teng, J. (2015). A dynamic Bayesian network based approach to safety decision support in tunnel construction. Reliability Engineering and System Safety, 134, 157–168.

    Article  Google Scholar 

  44. Xiong, K., Zhao, G., Shi, G., & Wang, Y. (2019). A convex optimization algorithm for compressed sensing in a complex domain: The complex-valued split bregman method. Sensors Basel, Switzerlad, 19(20), 4540.

    Article  Google Scholar 

  45. Xu, X., Chen, H., Lian, C., & Li, D. (2018). Learning-based predictive control for discrete-time nonlinear systems with stochastic disturbances. IEEE Transactions on Neural Networks and Learning Systems, 29(12), 6202–6213.

    Article  MathSciNet  Google Scholar 

  46. Zhan, L.-T., Chen, C., Wang, Y., & Chen, Y.-M. (2017). Failure probability assessment and parameter sensitivity analysis of a contaminant’s transit time through a compacted clay liner. Computers and Geotechnics, 86, 230–242.

    Article  Google Scholar 

  47. Zhang, L., Ekyalimpa, R., Hague, S., Werner, M., & AbouRizk, S. (2015) Updating geological conditions using Bayes theorem and Markov chain. In 2015 winter simulation conference (WSC), IEEE (pp. 3367–3378).

  48. Zhang, L., Wen, M., & Ashuri, B. (2018). BIM log mining: Measuring design productivity. Journal of Computing in Civil Engineering, 32(1), 04017071.

    Article  Google Scholar 

  49. Zhang, L., Wu, X., Qin, Y., Skibniewski, M. J., & Liu, W. (2016). Towards a fuzzy Bayesian network based approach for safety risk analysis of tunnel-induced pipeline damage. Risk Analysis, 36(2), 278–301.

    Article  Google Scholar 

  50. Ziv, J., & Lempel, A. (1978). Compression of individual sequences via variable-rate coding. IEEE Transactions on Information Theory, 24(5), 530–536.

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The Start-Up Grant at Nanyang Technological University, Singapore (No. M4082160.030) and the Ministry of Education Tier 1 Grant, Singapore (No. M4011971.030) are acknowledged for their financial support of this research.

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  1. School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    Chen Chen, Limao Zhang & Robert Lee Kong Tiong

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  1. Chen Chen
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Chen, C., Zhang, L. & Tiong, R.L.K. A new lossy compression algorithm for wireless sensor networks using Bayesian predictive coding. Wireless Netw 26, 5981–5995 (2020). https://doi.org/10.1007/s11276-020-02425-w

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