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Hybrid ARIMA and Neural Network Model for Measurement Estimation in Energy-Efficient Wireless Sensor Networks

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Informatics Engineering and Information Science (ICIEIS 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 253))

Abstract

Wireless Sensor Networks (WSNs) are composed of many sensor nodes using limited power resources. Therefore efficient power consumption is the most important issue in such networks. One way to reduce power consumption of sensor nodes is reducing the number of wireless communication between nodes by dual prediction. In this approach, the sink node instead of direct communication, exploits a time series model to predict local readings of sensor nodes with certain accuracy. There are different linear and non-linear models for time series forecasting. In this paper we will introduce a hybrid prediction model that is created from combination of ARIMA model as linear prediction model and neural network that is a non-linear model. Then, we will present a comparison between effectiveness of our approach and previous hybrid models. Experimental results show that the proposed method can be an effective way to reduce data transmission compared with existing hybrid models and also either of the components models used individually.

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References

  1. Ning Xu, A.: Survey of Sensor Network Applications. IEEE Communications Magazine 40 (2002)

    Google Scholar 

  2. Culler, D., Estrin, D., Srivastava, M.: Overview of Sensor Networks. Computer 37(8), 41–49 (2004)

    Article  Google Scholar 

  3. Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A Survey on Sensor Networks. IEEE Communications Magazine, 102–114 (2002)

    Google Scholar 

  4. Anastasi, G., Conti, M., Di Francesco, M., Passarella, A.: Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks 7, 537–568 (2009)

    Article  Google Scholar 

  5. Tharini, C., Vanaja Ranjan, P.: An Energy Efficient Spatial Correlation Based Data Gathering Algorithm for Wireless Sensor Networks. International Journal of Distributed and Parallel Systems 2(3), 16–24 (2011)

    Article  Google Scholar 

  6. Park, I., Mirikitani, D.T.: Energy Reduction in Wireless Sensor Networks through Measurement Estimation with Second Order Recurrent Neural Networks. In: Third International Conference on Networking and Services (ICNS 2007), pp. 103–103 (2007)

    Google Scholar 

  7. Intel Lab Data, http://db.csail.mit.edu/labdata/labdata.html

  8. Le Borgne, Y.-A., Santini, S., Bontempi, G.: Adaptive model selection for time series prediction in wireless sensor networks. Signal Processing 87(12) (2007)

    Google Scholar 

  9. Li, M., Ganesan, D., Shenoy, P.: PRESTO: Feedback-driven Data Management in Sensor Networks. IEEE/ACM Transactions on Networking 17(4), 1256–1269 (2009)

    Article  Google Scholar 

  10. Kim, W.-j., Ji, K., Srivastava, A.: Network-Based Control with Real-Time Prediction of Delayed/Lost Sensor Data. IEEE Transactions on Control Systems Technology 14(1), 182–185 (2006)

    Article  Google Scholar 

  11. Mukhopadhyay, S., Schurgers, C., Panigrahi, D., Dey, S.: Model-Based Techniques for Data Reliability in Wireless Sensor Networks. IEEE Transactions on Mobile Computing 8(4), 528–543 (2009)

    Article  Google Scholar 

  12. Arici, T., Akgun, T., Altunbasak, Y.: A Prediction Error-Based Hypothesis Testing Method for Sensor Data Acquisition. ACM Transactions on Sensor Networks (TOSN) 2(4) (2006)

    Google Scholar 

  13. Ling, Q., Tian, Z., Yin, Y., Li, Y.: Localized Structural Health Monitoring Using Energy-Efficient Wireless Sensor Networks. IEEE Sensors Journal 9(11), 1596–1604 (2009)

    Article  Google Scholar 

  14. Jiang, H., Jin, S., Wang, C.: Prediction or Not? An Energy-Efficient Framework for Clustering-Based Data Collection in Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems 22(6), 1064–1071 (2011)

    Article  Google Scholar 

  15. Peter Zhang, G.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)

    Article  MATH  Google Scholar 

  16. Peter Zhang, G., Eddy Patuwo, B., Hu, M.Y.: A simulation study of artificial neural networks for nonlinear time-series forecasting. Computers and Operations Research 28(4), 381–396 (2001)

    Article  MATH  Google Scholar 

  17. Mishra, A.K., Desai, V.R.: Drought forecasting using feed-forward recursive neural network. Ecological Modelling 198, 127–138 (2006)

    Article  Google Scholar 

  18. Lee Giles, C., Lawrence, S., Chung Tsoi, A.: Noisy Time Series Prediction using a Recurrent Neural Network and Grammatical Inference. Machine Learning, 161–183 (2001)

    Google Scholar 

  19. Frank, R.J., Davey, N., Hunt, S.P.: Time Series Prediction and Neural Networks. Journal of Intelligent and Robotic Systems 31, 91–103 (2001)

    Article  MATH  Google Scholar 

  20. Bodyanskiy, Y., Popov, S.: Neural network approach to forecasting of quasiperiodic financial time series. European Journal of Operational Research 175(3), 1357–1366 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  21. Chen, Y., Yang, B., Dong, J.: Time-series prediction using a local linear wavelet neural network. Neurocomputing 69(4-6), 449–465 (2006)

    Article  Google Scholar 

  22. Giordano, F., La Rocca, M., Perna, C.: Forecasting nonlinear time series with neural network sieve bootstrap. Computational Statistics & Data Analysis 51(8), 3871–3884 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  23. Peter Zhang, G., Kline, D.M.: Quarterly Time-Series Forecasting With Neural Networks. IEEE Transactions on Neural Networks 18(6) (2007)

    Google Scholar 

  24. Hussain, A.J., Knowles, A., Lisboa, P.J.G., El-Deredy, W.: Financial time series prediction using polynomial pipelined neural networks. Expert Systems with Applications 35(3), 1186–1199 (2008)

    Article  Google Scholar 

  25. Han, M., Wang, Y.: Analysis and modeling of multivariate chaotic time series based on neural network. Expert Systems with Applications 36(2), Part 1,1280–1290 (2009)

    Article  Google Scholar 

  26. Yu, Z.: Research Of Time Series Finding Algorithm Based On Artificial Neural Network. In: 2009 World Congress on Computer Science and Information Engineering, Los Angeles, CA, vol. 4, pp. 400–403 (2009)

    Google Scholar 

  27. Mandal, S., Saha, D., Banerjee, T.: A neural network based prediction model for flood in a disaster management system with sensor networks. In: 2005 International Conference on Intelligent Sensing and Information Processing, pp. 78–82 (2005)

    Google Scholar 

  28. Areekul, P., Senjyu, T., Toyama, H., Yona, A.: A Hybrid ARIMA and Neural Network Model for Short-Term Price Forecasting in Deregulated Market. IEEE Transactions on Power Systems 25(1), 524–530 (2010)

    Article  Google Scholar 

  29. Faruk, D.O.: A hybrid neural network and ARIMA model for water quality time series prediction. Engineering Applications of Artificial Intelligence 23(4), 586–594 (2010)

    Article  Google Scholar 

  30. Sterba, J., Hilovska, K.: The Implementation Of Hybrid Arima-Neural Network Prediction Model For Agregate Water Consumtion Prediction. Journal of Applied Mathematics 3 (2010)

    Google Scholar 

  31. Zeng, D., Xu, J., Gu, J., Liu, L., Xu, G.: Short Term Traffic Flow Prediction Using Hybrid ARIMA and ANN Models. In: 2008 Workshop on Power Electronics and Intelligent Transportation System (2008)

    Google Scholar 

  32. Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analyses Forecasting and Control, 3rd edn. Prentice Hall (1994)

    Google Scholar 

  33. Koskela, T., Lehtokangas, M., Saarinen, J., Kaski, K.: Time Series Prediction with Multilayer Perceptron, FIR and Elman Neural Networks. In: Proceedings of the World Congress on Neural Networks, pp. 491–496 (1996)

    Google Scholar 

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Askari Moghadam, R., Keshmirpour, M. (2011). Hybrid ARIMA and Neural Network Model for Measurement Estimation in Energy-Efficient Wireless Sensor Networks. In: Abd Manaf, A., Sahibuddin, S., Ahmad, R., Mohd Daud, S., El-Qawasmeh, E. (eds) Informatics Engineering and Information Science. ICIEIS 2011. Communications in Computer and Information Science, vol 253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25462-8_4

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  • DOI: https://doi.org/10.1007/978-3-642-25462-8_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25461-1

  • Online ISBN: 978-3-642-25462-8

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