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Routing tree maintenance based on trajectory prediction in mobile sensor networks

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Abstract

With the wireless sensor networks (WSNs) becoming extremely widely used, mobile sensor networks (MSNs) have recently attracted more and more researchers’ attention. Existing routing tree maintenance methods used for query processing are based on static WSNs, most of which are not directly applicable to MSNs due to the unique characteristic of mobility. In particular, sensor nodes are always moving in real world, which seriously affects the stability of the routing tree. Therefore, in this paper, we propose a novel method, named routing tree maintenance based on trajectory prediction in mobile sensor networks (RTTP), to guarantee a long term stability of routing tree. At first, we establish a trajectory prediction model based on extreme learning machine (ELM), by which we can predict sensor node’s trajectory to choose an appropriate parent node for each non-effective node. Then, an Improved version of RTTP method (I-RTTP) that using probabilistic method to minimize the error and improve the accuracy is proposed, to improve the performance of RTTP. Therefore, the state of the routing tree in MSNs can be made more stable. Finally, extensive experimental results show that RTTP and I-RTTP can effectively improve the stability of routing tree and greatly reduce energy consumption of mobile sensor nodes.

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References

  1. Madden S, Franklin MJ, Hellerstein JM, Hong W (2002) TAG: a tiny aggregation service for ad-hoc sensor networks. In: Proceedings of 5th symposium operating systems design and implementation (OSDI’02), pp 131–146

  2. Considine J, Li F, Kollios G, Byers J (2004) Approximate aggregation techniques for sensor databases. In: Proceedings of the 20th international conference on data engineering (ICDE’04), pp 449–460

  3. Manjhi A, Nath S, Gibbons PB (2005) Tributaries and deltas: efficient and robust aggregation in sensor network streams. In: Proceedings of the 2005 ACM SIGMOD international conference on management of data (SIGMOD’05), pp 287–298

  4. Sharaf MA, Beaver J, Labrinidis A, Chrysanthis PK (2004) Balancing energy efficiency and quality of aggregate data in sensor networks. VLDB J (VLDBJ) 13(4):384–403

    Article  Google Scholar 

  5. Kusy B, Lee HJ, Wicke M, Milosavljevic N, Guibas L (2009) Predictive QOS routing to mobile sinks in wireless sensor networks. In: Proceedings of international conference on information processing in sensor networks (IPSN’09), pp 109–120

  6. Chang TJ, Wang K, Hsieh YL (2008) A color theory based energy efficient routing algorithm for mobile wireless sensor networks. Int J Comput Netw Commun 52:531–541

    Article  MATH  Google Scholar 

  7. Nguyen LT, Defago X, Beuran R, Shinoda Y (2008) Energy efficient routing scheme for mobile wireless sensor networks. In: Proceedings of IEEE international symposium on wireless communication systems (ISWCS’08), pp 568–572

  8. Luo J, Hubaux JP (2005) Joint mobility and routing for lifetime elongation in wireless sensor networks. In: Proceedings of the 24th IEEE international conference on computer communications (INFOCOM’05), pp 1735–1746

  9. Wang X, Jiang X, Lin J, Xiong J (2013) Prediction of moving object trajectory based on probabilistic suffix tree. J Comput Appl 33:3119–3101

    Google Scholar 

  10. Qiao S, Peng J, Li T, Zhu Y, Liu L (2012) Uncertain trajectory prediction of moving objects based on CTBN. J Univ Electron Sci Technol China 41(5):759–763

    Google Scholar 

  11. Guo L, Ding Z, Hu Z, Chen C (2010) Uncertain path prediction of moving objects on road networks. J Comput Res Dev 47:104–112

    Google Scholar 

  12. Zhang F, Jin B, Wang Z, Hu J, Zhang L (2015) A routing mechanism over bus-based VANETs by mining trajectories. Chin J Comput 38(3):648–662

    MathSciNet  Google Scholar 

  13. Feng C, Li A, Jiang S (2015) Data aggregation scheduling on wireless mobile sensor networks. Chin J Comput 38(3):685–700

    MathSciNet  Google Scholar 

  14. Qiao S, Shen D, Wang X, Han N, Zhu W (2015) A self-adaptive parameter selection trajectory prediction approach via hidden Markov models. IEEE Trans Intell Transp Syst 16(1):284–296

    Article  Google Scholar 

  15. Huang G, Huang G-B, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48

    Article  MATH  Google Scholar 

  16. Singh M, Sethi M, Lal N (2010) A tree based routing protocol for mobile sensor networks. Int J Comput Sci Eng 2(S1):55–60

    Google Scholar 

  17. Cao J, Lin Z (2015) Extreme learning machine on high dimensional and large data applications: a survey. Math Probl Eng 2015:1–12 (Article ID 103796)

  18. Cao J, Zhao Y, Lai X, Ong M, Yin C, Koh Z, Liu N (2015) Landmark recognition with sparse representation classification and extreme learning machine. J Frankl Inst 352(10):4528–4545

    Article  MathSciNet  Google Scholar 

  19. Cao J, Chen T, Fan J (2015) Landmark recognition with compact BoW histogram and ensemble ELM. Multimed Tools Appl. doi:10.1007/s11042-014-2424-1

    Google Scholar 

  20. Nekkaa M, Boughaci D (2015) A memetic algorithm with support vector machine for feature selection and classification. Memet Comput 7(1):59–73

    Article  Google Scholar 

  21. Kraipeerapun P, Nakkrasae S, Fung CC, Amornsamankul S (2010) Solving regression problem with complementary neural Networks and an adjusted averaging technique. Memet Comput 2(4):249–257

    Article  Google Scholar 

  22. Thammano A, Ruxpakawong P (2010) Nonlinear dynamic system identification using recurrent neural network with multi-segment piecewise-linear connection weight. Memet Comput 2(4):273–282

    Article  Google Scholar 

  23. Cacciola M, Megali G, Fiasch M, Versaci M, Morabito FC (2010) A comparison between neural networks and K-nearest neighbours for blood cells taxonomy. Memet Comput 2(3):237–246

    Article  Google Scholar 

  24. Salama MA, Hassanien AE, Revett K (2013) Employment of neural network and rough set in meta-learning. Memet Comput 5(3):165–177

    Article  Google Scholar 

  25. Huang G-B, Zhu Q, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  26. Huang G-B, Chen L, Siew C-K (2006) Universal approximation usingincremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892

    Article  Google Scholar 

  27. Huang G-B, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70(16–18):3056–3062

    Article  Google Scholar 

  28. Huang G-B, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71(16–18):3460–3468

    Article  Google Scholar 

  29. Huang G-B, Ding X, Zhou H (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74(1–3):155–163

    Article  Google Scholar 

  30. Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 42(2):513–529

    Article  Google Scholar 

Download references

Acknowledgments

This research was partially supported by the National Natural Science Foundation of China under Grant Nos. 61472069 and 61402089; And the Fundamental Research Funds for the Central Universities under Grant No. N150408001; And Natural Science Foundation of Liaoning Province under Grant No. 2015020553.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Junchang Xin, Teng Li & Pei Wang

  2. Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China

    Zhiqiong Wang

Authors
  1. Junchang Xin
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  2. Teng Li
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  3. Pei Wang
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  4. Zhiqiong Wang
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Correspondence to Junchang Xin.

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Xin, J., Li, T., Wang, P. et al. Routing tree maintenance based on trajectory prediction in mobile sensor networks. Memetic Comp. 9, 109–120 (2017). https://doi.org/10.1007/s12293-016-0184-3

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  • DOI: https://doi.org/10.1007/s12293-016-0184-3

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