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Cross Layer Optimization for ZigBee-Based Transmission Line Monitoring and Data Collection

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Abstract

A hybrid network architecture comprised of wireless sensor networks and cellular network is considered to monitor the high voltage transmission lines (HVTL). Corona noise is one of the sources of electromagnetic interference in HVTL. Automatic repeat request (ARQ) is utilized to increase the system reliability and to reduce the errors due to impulsive Corona noise. By using ARQ, link reliability is increased at the expense of communication delay. In this paper, the objective is to find the optimum location of ZigBee gateways such that reliability and latency constraints are satisfied. A cross-layer optimization technique is used to illustrate the effects of physical and MAC layer parameters on the QoS of the data collection.

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Notes

  1. Based on the OpenSG the maximum accepted packet latency is 4 s, however since we do not take the latency of networking and application layer into account, we chose the maximum latency of 1 s.

References

  1. Gungora, V. C., & Lambert, F. C. (2006). A survey on communication networks for electric system automation (pp. 877–897). Amsterdam: Elsevier.

    Google Scholar 

  2. Yi Yang, D. D., & Lambert, F. (2007). A survey on technologies for implementing sensor networks for power delivery systems. In IEEE power engineering society general meeting (pp. 1–8).

  3. Guo, Z., et al. (2014). A wireless sensor network for monitoring smart grid transmission lines. In 23rd international conference on computer communication and networks (ICCCN) (pp. 1–6).

  4. Leoni, J. L., et al. (2014). Real-time monitoring of transmission lines using wireless sensor networks. In Transmission and distribution conference and exposition-latin America (PES T&D-LA) (pp. 1–6).

  5. Venkatasubramani, K., & Karthikeyan, R. (2014). Monitoring of transmission line parameters using wireless networks in smart grid. In International conference on intelligent computing applications (ICICA) (pp. 330–334).

  6. Hung, W. L. K., et al. (2010). On wireless sensors communication for overhead transmission line monitoring in power delivery systems. In IEEE smart grid comm (pp. 309–314).

  7. Wu, Y.-C., Cheung, L.-F., et al. (2012). Efficient communication of sensors monitoring overhead transmission lines. IEEE Transaction on Smart Grid, 3, 1130–1136.

    Article  Google Scholar 

  8. Lin, J., Zhu, B., et al. (2014). Monitoring power transmission lines using a wireless sensor network. Wireless Communications and Mobile Computing.

  9. Li, P., & Guo, S. (2013). Delay minimization for reliable data collection on overhead transmission lines in smart grid. In Computing, communications and IT applications conference (pp. 147–152).

  10. Akyildiz, I. F., & Vuran, M. C. (2010). Wireless sensor networks. Wiley.

  11. Dowlatdad, F., et al. (2015). A Markov–Middleton model for corona noise in the wsn transmission line monitoring. In IEEE electrical power and energy conference (EPEC’15).

  12. Fateh, B., Govindarasu, M., & Ajjarapu, V. (2013). Wireless network design for transmission line monitoring in smart grid. IEEE Transaction on Smart Grid, 4(2), 1076–1086.

    Article  Google Scholar 

  13. Wang, K., Qiu, X., et al. (2015). Fault tolerance oriented sensors relay monitoring mechanism for overhead transmission line in smart grid. IEEE Sensors Journal, 15(3), 1982–1991.

    Article  Google Scholar 

  14. Alam, M. M., Razzaque, M., Mamun-Or-Rashid, M., Hong, C. S., et al. (2009). Energy-aware qos provisioning for wireless sensor networks: Analysis and protocol. Journal of Communications and Networks, 11(4), 390–405.

    Article  Google Scholar 

  15. Hossain, E., Poor, H. V., & Han, Z. (2012). Smart grid communications and networking. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  16. Madi, G., et al. (2011). Impacts of impulsive noise from partial discharges on wireless systems performance: Application to MIMO precoders. EURASIP Journal on Wireless Communications and Networking, 2011(1), 186.

    Article  Google Scholar 

  17. Issariyakul, T., & Hossain, E. (2006). Performance modeling and analysis of a class of ARQ protocols in multi-hop wireless networks. IEEE Transaction on Wireless Communication, 5(12), 3460–3468.

  18. Abouei, J., Dehkordy, S.F., et al. (2011). Raptor codes in wireless body area networks. In IEEE international symposium on personal, indoor and mobile radio communications (PIMRC’11).

  19. Batur, O. Z., Koca, M., & Dundar, G. (2008). Measurements of impulsive noise in broad-band wireless communication channels. In Research in microelectronics and electronics (pp. 233–236).

  20. Ndo, G., Labeau, F., & Kassouf, M. (2013). A Markov–Middleton model for bursty impulsive noise: Modeling and receiver design. IEEE Transaction on Power Delivery, 28, 2317–2325.

    Article  Google Scholar 

  21. Al-Anbagi, I., Erol-Kantarci, M., & Mouftah, H. T. (2014). Priority-and delay-aware medium access for wireless sensor networks in the smart grid. IEEE Systems Journal, 8(2), 608–618.

    Article  Google Scholar 

  22. Kleinrock, L. (1975). Theory, Volume 1, Queueing Systems. Hoboken: Wiley.

    MATH  Google Scholar 

  23. Audet, C., & Dennis, J. (2006). Mesh adaptive direct search algorithms for constrained optimization. SIAM Journal on Optimization, 7(1), 188–217.

  24. McKay, M. D., Beckman, R. J., & Conover, W. J. (2000). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 42(1), 55–61.

    Article  MATH  Google Scholar 

  25. Lusa, A., & Potts, C. N. (2008). A variable neighbourhood search algorithm for the constrained task allocation problem. Journal of the Operational Research Society, 59(6), 812–822.

    Article  MATH  Google Scholar 

  26. Digabel, S. L. (2011). Algorithm xxx: NOMAD: Nonlinear optimization with the MADS algorithm. In ACM transactions on mathematical software (pp. 1–21).

  27. Hussain, F., et al. (2016). Multi-objective resource allocation in interference-limited M2M communication networks. IJCNDS, 16(3), 297–313.

    Article  Google Scholar 

  28. Sebastien Le Digabel, C. T., & Audet, C. NOMAD User Guide Version 3.7.2. http://www.gerad.ca.

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Correspondence to Alagan Anpalagan.

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Dowlatdad, F., Hussain, F., Naeem, M. et al. Cross Layer Optimization for ZigBee-Based Transmission Line Monitoring and Data Collection. Wireless Pers Commun 98, 1413–1433 (2018). https://doi.org/10.1007/s11277-017-4924-6

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  • DOI: https://doi.org/10.1007/s11277-017-4924-6

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