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Neural network based instant parameter prediction for wireless sensor network optimization models

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Abstract

Optimal operation configuration of a Wireless Sensor Network (WSN) can be determined by utilizing exact mathematical programming techniques such as Mixed Integer Programming (MIP). However, computational complexities of such techniques are high. As a remedy, learning algorithms such as Neural Networks (NNs) can be utilized to predict the WSN settings with high accuracy with much lower computational cost than the MIP solutions. We focus on predicting network lifetime, transmission power level, and internode distance which are interrelated WSN parameters and are vital for optimal WSN operation. To facilitate an efficient solution for predicting these parameters without explicit optimizations, we built NN based models employing data obtained from an MIP model. The NN based scalable prediction model yields a maximum of 3% error for lifetime, 6% for transmission power level error, and internode distances within an accuracy of 3 m in prediction outcomes.

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References

  1. Ahad, N., Qadir, J., & Ahsan, N. (2016). Neural networks in wireless networks: Techniques, applications and guidelines. Journal of Network and Computer Applications, 68, 1–27.

    Article  Google Scholar 

  2. Akkaya, K., & Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 3(3), 325–349.

    Article  Google Scholar 

  3. Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H. P. (2014). Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Communications Surveys Tutorials, 16(4), 1996–2018.

    Article  Google Scholar 

  4. Alwadi, M. A. (2015). Energy efficient wireless sensor networks based on machine learning. Ph.D. thesis, University of Canberra

  5. Aram, S., Mesin, L., Pasero, E. (2014). Improving lifetime in wireless sensor networks using neural data prediction. In Proceedings of world symposium on computer applications & research (WSCAR) (pp. 4–6).

  6. Baesens, B., Gestel, T. V., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society, 54(6), 627–635.

    Article  MATH  Google Scholar 

  7. Bai, X., Liu, L., Cao, M., Panneerselvam, J., Sun, Q., & Wang, H. (2017). Collaborative actuation of wireless sensor and actuator networks for the agriculture industry. IEEE Access, 5, 13286–13296.

    Article  Google Scholar 

  8. Barve, Y. D. (2009). Neural network approach to the prediction of percentage data packet loss for wireless sensor networks. In Proceedings of southeastern symposium on system theory (pp. 103–106).

  9. Bhatia, T., Kansal, S., Goel, S., & Verma, A. (2016). A genetic algorithm based distance-aware routing protocol for wireless sensor networks. Computers & Electrical Engineering, 56, 441–455.

    Article  Google Scholar 

  10. Blue, J., Candela, G., Grother, P., Chellappa, R., & Wilson, C. (1994). Evaluation of pattern classifiers for fingerprint and ocr applications. Pattern Recognition, 27(4), 485–501.

    Article  Google Scholar 

  11. Cao, J., Ahmadi, M., & Shridhar, M. (1997). A hierarchical neural network architecture for handwritten numeral recognition. Pattern Recognition, 30(2), 289–294.

    Article  Google Scholar 

  12. Chen, L. S., Liu, C. H., & Chiu, H. J. (2011). A neural network based approach for sentiment classification in the blogosphere. Journal of Informetrics, 5(2), 313–322.

    Article  Google Scholar 

  13. Chincoli, M., den Boef, P., & Liotta, A. (2017). Cognitive channel selection for wireless sensor communications. In Proceedings of IEEE international conference on networking, sensing and control (ICNSC) (pp. 795–800).

  14. El-Hoiydi, A., & Decotignie, J. D. (2004). WiseMAC: An ultra low power MAC protocol for multi-hop wireless sensor networks. In S. E. Nikoletseas & J. D. P. Rolim (Eds.), Algorithmic aspects of wireless sensor networks. ALGOSENSORS 2004. Lecture Notes in Computer Science (Vol. 3121). Berlin, Heidelberg: Springer.

  15. Enami, N., Moghadam, R. A., & Haghighat, A. (2010). A survey on application of neural networks in energy conservation of wireless sensor networks. In Proceedings of international conference on recent trends in wireless and mobile networks (WiMo) (pp. 283–294).

  16. GAMS Development Corporation. (2013). General algebraic modeling system (GAMS) release 24.2.1. Washington, DC, USA. http://www.gams.com/. Accessed 01 Mar 2017.

  17. Gao, T., Chen, M., Gu, H., & Yin, C. (2017). Reinforcement learning based resource allocation in cache-enabled small cell networks with mobile users. In Proceedings IEEE/CIC international conference on communications in China (ICCC) (pp. 1–6).

  18. Gharghan, S. K., Nordin, R., Ismail, M., & Ali, J. A. (2016). Accurate wireless sensor localization technique based on hybrid PSO-ANN algorithm for indoor and outdoor track cycling. IEEE Sensors Journal, 16(2), 529–541.

    Article  Google Scholar 

  19. Guo, L., Chen, F., Dai, Z., & Liu, Z. (2010). WSN cluster head selection algorithm based on neural network. In Proceedings of international conference on machine vision and human–machine interface (pp. 258–260).

  20. Guo, W., & Zhang, W. (2014). A survey on intelligent routing protocols in wireless sensor networks. Journal of Network and Computer Applications, 38, 185–201.

    Article  Google Scholar 

  21. Haykin, S. (1998). Neural networks: A comprehensive foundation (2nd ed.). Upper Saddle River: Prentice Hall PTR.

    MATH  Google Scholar 

  22. He, Y., Zhang, Z., Yu, F. R., Zhao, N., Yin, H., Leung, V. C. M., et al. (2017). Deep-reinforcement-learning-based optimization for cache-enabled opportunistic interference alignment wireless networks. IEEE Transactions on Vehicular Technology, 66(11), 10433–10445.

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K. C., & Hanzo, L. (2017). Machine learning paradigms for next-generation wireless networks. IEEE Wireless Communications, 24(2), 98–105.

    Article  Google Scholar 

  25. Karakaya, M., & Sevinç, E. (2017). An efficient genetic algorithm for routing multiple uavs under flight range and service time window constraints. Bilişim Teknolojileri Dergisi, 10(1), 113.

  26. Kaswan, A., Singh, V., & Jana, P. K. (2018). A multi-objective and PSO based energy efficient path design for mobile sink in wireless sensor networks. Pervasive and Mobile Computing, 46, 122–136.

    Article  Google Scholar 

  27. Kocer, H. E., & Cevik, K. K. (2011). Artificial neural networks based vehicle license plate recognition. Procedia Computer Science, 3, 1033–1037.

    Article  Google Scholar 

  28. Kulin, M., Fortuna, C., De Poorter, E., Deschrijver, D., & Moerman, I. (2016). Data-driven design of intelligent wireless networks: An overview and tutorial. Sensors, 16(6), 790.

    Article  Google Scholar 

  29. Kulin, M., Kazaz, T., Moerman, I., & Poorter, E. D. (2018). End-to-end learning from spectrum data: A deep learning approach for wireless signal identification in spectrum monitoring applications. IEEE Access, 6, 18484–18501.

    Article  Google Scholar 

  30. Kulin, M., de Poorter, E., Kazaz, T., & Moerman, I. (2017). Poster: Towards a cognitive MAC layer—Predicting the MAC-level performance in dynamic WSN using machine learning. In Proceedings of international conference on embedded wireless systems and networks (pp. 214–215).

  31. Kuzborskij, I., Gijsberts, A., & Caputo, B. (2012). On the challenge of classifying 52 hand movements from surface electromyography. In 2012 annual international conference of the IEEE engineering in medicine and biology society (pp. 4931–4937).

  32. Laganà, M., Glaropoulos, I., Fodor, V., & Petrioli, C. (2012). Modeling and estimation of partially observed wlan activity for cognitive WSNs. In Proceedings of IEEE wireless communications and networking conference (WCNC) (pp. 1526–1531).

  33. Li, H., Gao, H., Lv, T., & Lu, Y. (2018). Deep q-learning based dynamic resource allocation for self-powered ultra-dense networks. In Proceedings of IEEE international conference on communications workshops (ICC Workshops) (pp. 1–6).

  34. Li, J., & Liu, D. (2016). An energy aware distributed clustering routing protocol for energy harvesting wireless sensor networks. In Proceedings of IEEE/CIC international conference on communications in China (ICCC) (pp. 1–6).

  35. Liu, T., & Cerpa, A. E. (2014). Data-driven link quality prediction using link features. ACM Transactions on Sensor Networks, 10(2), 37:1–37:35.

    Article  Google Scholar 

  36. Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007). A review of classification algorithms for eeg-based brain–computer interfaces. Journal of Neural Engineering, 4(2), R1.

    Article  Google Scholar 

  37. Ma, Y., Peng, M., Xue, W., & Ji, X. (2013). A dynamic affinity propagation clustering algorithm for cell outage detection in self-healing networks. In Proceedings of IEEE wireless communications and networking conference (WCNC) (pp. 2266–2270).

  38. Mao, Q., Hu, F., Hao, Q. (2018). Deep learning for intelligent wireless networks: A comprehensive survey. IEEE Communications Surveys Tutorials. https://doi.org/10.1109/COMST.2018.2846401

  39. Martinez-Sala, A. S., Pardo, J. M. M. G., Egea-Lopez, E., Vales-Alonso, J., Juan-Llacer, L., & Garcia-Haro, J. (2005). An accurate radio channel model for wireless sensor networks simulation. Journal of Communications and Networks, 7(4), 401–407.

    Article  Google Scholar 

  40. McDanel, B., Teerapittayanon, S., & Kung, H. (2017). Embedded binarized neural networks. In Proceedings of international conference on embedded wireless systems and networks (EWSN) (pp. 168–173), Junction Publishing.

  41. Mishra, A., Chakraborty, S., Li, H., & Agrawal, D. P. (2014). Error minimization and energy conservation by predicting data in wireless body sensor networks using artificial neural network and analysis of error. In Proceedings of consumer communications and networking conference (CCNC) (pp. 177–182).

  42. Nie, L., Jiang, D., Yu, S., & Song, H. (2017). Network traffic prediction based on deep belief network in wireless mesh backbone networks. In Proceedings of IEEE wireless communications and networking conference (WCNC) (pp. 1–5).

  43. Ortiz, A., Al-Shatri, H., Li, X., Weber, T., & Klein, A. (2016). Reinforcement learning for energy harvesting point-to-point communications. In Proceedings of IEEE international conference on communications (ICC) (pp. 1–6).

  44. Ozbayoglu, A. M., & Yuksel, H. E. (2012). Analysis of gas–liquid behavior in eccentric horizontal annuli with image processing and artificial intelligence techniques. Journal of Petroleum Science and Engineering, 81, 31–40.

    Article  Google Scholar 

  45. Pal, V., Singh, Y. G., & Yadav, R. (2015). Cluster head selection optimization based on genetic algorithm to prolong lifetime of wireless sensor networks. Procedia Computer Science, 57, 1417–1423.

    Article  Google Scholar 

  46. Payal, A., Rai, C. S., & Reddy, B. V. R. (2014). Artificial neural networks for developing localization framework in wireless sensor networks. In Proceedings of international conference on data mining and intelligent computing (ICDMIC) (pp. 1–6).

  47. Peng, S., Jiang, H., Wang, H., Alwageed, H., & Yao, Y. D. (2017). Modulation classification using convolutional neural network based deep learning model. In Proceedings of wireless and optical communication conference (WOCC) (pp. 1–5).

  48. Pinto, A., Montez, C., Araújo, G., Vasques, F., & Portugal, P. (2014). An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms. Information Fusion, 15, 90–101.

    Article  Google Scholar 

  49. Qiu, M., Song, Y., & Akagi, F. (2016). Application of artificial neural network for the prediction of stock market returns: The case of the japanese stock market. Chaos, Solitons & Fractals, 85, 1–7.

    Article  MathSciNet  Google Scholar 

  50. Qiulan, W., Xia, G., Yong, L., & Hongli, X. (2013). Research on multi-sensor data fusion algorithm of soil carbon sink factors based on neural network. In Proceedings of international conference on mechatronic sciences, electric engineering and computer (MEC) (pp. 637–641).

  51. Rahat, A. A., Everson, R. M., & Fieldsend, J. E. (2016). Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks. Ad Hoc Networks, 52, 130–145.

    Article  Google Scholar 

  52. Rajendran, S., Meert, W., Giustiniano, D., Lenders, V., & Pollin, S. (2018). Deep learning models for wireless signal classification with distributed low-cost spectrum sensors. IEEE Transactions on Cognitive Communications and Networking.

  53. Rana, S. P., Dey, M., Siddiqui, H. U., Tiberi, G., Ghavami, M., & Dudley, S. (2017). UWB localization employing supervised learning method. In Proceedings of IEEE international conference on ubiquitous wireless broadband (ICUWB) (pp. 1–5).

  54. Rumelhart, D., Hinton, G., & Williams, R. (1986). Learning representations of back-propagation errors. Nature, 323, 533–536.

    Article  MATH  Google Scholar 

  55. Sabitha, R., Bhuma, K., & Thyagarajan, T. (2017). Transmission power control using state estimation-based received signal strength prediction for energy efficiency in wireless sensor networks. Turkish Journal of Electrical Engineering and Computer Sciences, 25(1), 591–604.

    Article  Google Scholar 

  56. Sarikan, S., Ozbayoglu, A., & Zilci, O. (2017). Automated vehicle classification with image processing and computational intelligence. Procedia Computer Science, 114, 515–522.

    Article  Google Scholar 

  57. Serin, G., Gudelek, U., Ozbayoglu, A., & Unver, H. (2017). Estimation of parameters for the free-form machining with deep neural network. In Proceedings of IEEE international conference on big data (pp. 2102–2111). IEEE.

  58. Sezer, O., Ozbayoglu, A., & Dogdu, E. (2017). An artificial neural network-based stock trading system using technical analysis and big data framework. In ACM southeast conference (pp. 223–226). ACM.

  59. Sha, M., Dor, R., Hackmann, G., Lu, C., Kim, T. S., & Park, T. (2013). Self-adapting MAC layer for wireless sensor networks. In Proceedings of IEEE real-time systems symposium (pp. 192–201).

  60. Shahamiri, S. R., & Salim, S. S. B. (2014). Real-time frequency-based noise-robust automatic speech recognition using multi-nets artificial neural networks: A multi-views multi-learners approach. Neurocomputing, 129, 199–207.

    Article  Google Scholar 

  61. Singh, P., & Agrawal, S. (2013). Node localization in wireless sensor networks using the M5P tree and SMOreg algorithms. In Proceedings of international conference on computational intelligence and communication networks (pp. 104–104).

  62. Singh, P., & Agrawal, S. (2013). TDOA based node localization in WSN using neural networks. In Proceedings of international conference on communication systems and network technologies (pp. 400–404).

  63. Stallkamp, J., Schlipsing, M., Salmen, J., & Igel, C. (2012). Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition. Neural Networks, 32, 323–332.

    Article  Google Scholar 

  64. Subha, C. P., Malarkan, S., & Vaithinathan, K. (2013). A survey on energy efficient neural network based clustering models in wireless sensor networks. In Proceedings of international conference on emerging trends in VLSI, embedded system, nano electronics and telecommunication system (ICEVENT) (pp. 1–6).

  65. Sun, H., Chen, X., Shi, Q., Hong, M., Fu, X., & Sidiropoulos, N. D. (2017). Learning to optimize: Training deep neural networks for wireless resource management. In Proceedings of IEEE international workshop on signal processing advances in wireless communications (SPAWC) (pp. 1–6).

  66. Sun, W., Lu, W., Li, Q., Chen, L., Mu, D., & Yuan, X. (2017). WNN-LQE: Wavelet-neural-network-based link quality estimation for smart grid WSNs. IEEE Access, 5, 12788–12797.

    Article  Google Scholar 

  67. The MathWorks Inc.: MATLAB neural networks toolbox release 2017b. Natick, Massachusetts, United States (2017). https://www.mathworks.com/products/neural-network.html. Accessed 01 Mar 2017.

  68. Valadarsky, A., Schapira, M., Shahaf, D., & Tamar, A. (2017). A machine learning approach to routing. CoRR arxiv: abs/1708.03074.

  69. Wolsey, L. (1998). Integer programming. Hoboken: Wiley.

    MATH  Google Scholar 

  70. Yetgin, H., Cheung, K. T. K., El-Hajjar, M., & Hanzo, L. H. (2017). A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Communications Surveys Tutorials, 19(2), 828–854.

    Article  Google Scholar 

  71. Yildiz, H. U., Tavli, B., & Yanikomeroglu, H. (2015). Transmission power control for link-level handshaking in wireless sensor networks. IEEE Sensors Journal, 16, 561–576.

    Article  Google Scholar 

  72. Yoon, S., & Shahabi, C. (2007). The Clustered AGgregation (CAG) technique leveraging spatial and temporal correlations in wireless sensor networks. ACM Transactions on Sensor Networks, 3(1), 3.

    Article  Google Scholar 

  73. Yue, H., & Xuedong, G. (2014). Clustering on heterogeneous networks. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(3), 213–233.

    Google Scholar 

  74. Zhang, R. (2010). On active learning and supervised transmission of spectrum sharing based cognitive radios by exploiting hidden primary radio feedback. IEEE Transactions on Communications, 58(10), 2960–2970.

    Article  Google Scholar 

  75. Zimmerling, M., Ferrari, F., Mottola, L., Voigt, T., & Thiele, L. (2012). pTUNES: Runtime parameter adaptation for low-power MAC protocols. In Proceedings of ACM/IEEE international conference on information processing in sensor networks (IPSN) (pp. 173–184).

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Authors and Affiliations

  1. Department of Computer Engineering, University of Turkish Aeronautical Association, 06790, Ankara, Turkey

    Ayhan Akbas

  2. Department of Electrical and Electronics Engineering, TED University, 06420, Ankara, Turkey

    Huseyin Ugur Yildiz

  3. Department of Computer Engineering, TOBB University of Economics and Technology, 06560, Ankara, Turkey

    Ahmet Murat Ozbayoglu

  4. Department of Electrical and Electronics Engineering, TOBB University of Economics and Technology, 06560, Ankara, Turkey

    Bulent Tavli

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  1. Ayhan Akbas
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  2. Huseyin Ugur Yildiz
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  3. Ahmet Murat Ozbayoglu
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  4. Bulent Tavli
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Correspondence to Ahmet Murat Ozbayoglu.

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Akbas, A., Yildiz, H., Ozbayoglu, A. et al. Neural network based instant parameter prediction for wireless sensor network optimization models. Wireless Netw 25, 3405–3418 (2019). https://doi.org/10.1007/s11276-018-1808-y

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