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Enabling remote-control for the power sub-stations over LTE-A networks

  • MHD Nour Hindia
  • Faizan Qamar
  • Mohammad B. Majed
  • Tharek Abd Rahman
  • Iraj S. Amiri
Article
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Abstract

In recent years, smart grid (SG) applications have been proven a sophisticated technology of immense aptitude, comfort and efficiency not only for the power generation sectors but also for other industrial purposes. The term SG is used to describe a set of systems customized to rapidly and automatically monitor user demand, restore power, isolate faults and maintain stability for more efficient transmission, generation and delivery of electric power. Nevertheless, the quality of service (QoS) guarantee is essential to maintain the networking technology used in different stages and communication of the SG for efficient distribution, which may be drastically obstructed as the sensors of the application increases. Undoubtedly, receiving and transmitting of this information requires two-way, high speed, reliable and secure communication infrastructure. In this paper, we have proposed a scheduling approach guarantees the efficient utilization of existing network resources that satisfy the sensors’ demands sufficiently. The proposed approach is based on hierarchical adaptive weighting method, which helps to overcome the issues of studied scheduling approach and intended to aid SG sensors applications, based on its QoS demands. We have employed four enabler SG applications for remote power control, namely demand response, advanced metering infrastructure, video surveillance and wide area situational awareness applications for the implementation of the remote-power substation controlling. Moreover, the cooperative game theory technique has been incorporated into a solution for the optimal estimation and allocation of bandwidth among different sensors. The results have been evaluated in terms of throughput, fairness index and spectral efficiency and results have been compared with the well-known scheduling approaches such as exponential/proportional fairness (EXP/PF), best channel quality indicator (Best-CQI) and exponential rules (EXP-Rule). The results demonstrated that the proposed approach is providing a better performance in terms fairness index by 25, 66 and 68% compared to EXP/PF, EXP/RULE and Best-CQI, respectively.

Keywords

Smart grid LTE-A Demand response Smart metering infrastructure Video surveillance and wide area situational awareness 

References

  1. 1.
    Yates, R. D. (1995). A framework for uplink power control in cellular radio systems. IEEE Journal on Selected Areas in Communications, 13, 1341–1347.CrossRefGoogle Scholar
  2. 2.
    Hu, J., Leung, V. C., Yang, K., Zhang, Y., Gao, J., & Yang, S. (2016). Smart grid inspired future technologies. New York: Springer.Google Scholar
  3. 3.
    Tuballa, M. L., & Abundo, M. L. (2016). A review of the development of Smart Grid technologies. Renewable and Sustainable Energy Reviews, 59, 710–725.CrossRefGoogle Scholar
  4. 4.
    Garau, M., Anedda, M., Desogus, C., Ghiani, E., Murroni, M., & Celli, G. (2017). A 5G cellular technology for distributed monitoring and control in smart grid. In 2017 IEEE international symposium on broadband multimedia systems and broadcasting (BMSB) (pp. 1–6).Google Scholar
  5. 5.
    Pandey, R. K., & Misra, M. (2016). Cyber security threats—Smart grid infrastructure. In Power systems conference (NPSC), 2016 national (pp. 1–6).Google Scholar
  6. 6.
    Garau, M., Celli, G., Ghiani, E., Pilo, F., & Corti, S. (2017). Evaluation of smart grid communication technologies with a co-simulation platform. IEEE Wireless Communications, 24, 42–49.CrossRefGoogle Scholar
  7. 7.
    Qamar, F., Abbas, T., Hindia, M. N., Dimyati, K. B., Noordin, K. A. B., & Ahmed, I. (2017). Characterization of MIMO propagation channel at 15 GHz for the 5G spectrum. In 2017 IEEE 13th Malaysia international conference on communications (MICC) (pp. 265–270).Google Scholar
  8. 8.
    Hajjawi, A., Ismail, M., Abdullah, N. F., & Ramli, N. (2015). A novel scheduling algorithm based class-service using game theory for LTE network. In 2015 IEEE 12th Malaysia international conference on communications (MICC) (pp. 351–355).Google Scholar
  9. 9.
    Webster, R., Munasinghe, K., & Jamalipour, A. (2016). Optimized resource allocation in LTE networks incorporating delay-sensitive Smart Grid traffic. In 2016 IEEE international conference on smart grid communications (SmartGridComm) (pp. 423–428).Google Scholar
  10. 10.
    Trabelsi, S., Belghith, A., Zarai, F., & Obaidat, M. S. (2015). Performance evaluation of a decoupled-level with QoS-aware downlink scheduling algorithm for LTE networks. In 2015 IEEE international conference on data science and data intensive systems (DSDIS). (pp. 696–704).Google Scholar
  11. 11.
    Iosif, O., & Banica, I. (2011). On the analysis of packet scheduling in downlink 3GPP LTE system. CTRQ, 2011, 106.Google Scholar
  12. 12.
    Qamar, F., Siddiqui, M. H. S., Dimyati, K., Noordin, K. A. B., & Majed, M. B. (2017). Channel characterization of 28 and 38 GHz MM-wave frequency band spectrum for the future 5G network. In 2017 IEEE 15th student conference on research and development (SCOReD) (pp. 291–296).Google Scholar
  13. 13.
    Mushtaq, A.-S., Haider, A.-Z., Orest, L., & Mykhailo, K. (2015). Improving QoS in MAX C/I scheduling using resource allocation type 1 of LTE. In 2015 13th international conference on experience of designing and application of CAD systems in microelectronics (CADSM) (pp. 12–14).Google Scholar
  14. 14.
    Miki, N., & Takemoto, T. (2015). Investigation on resource selection scheme based on proportional fair criteria. In 2015 international conference on information and communication technology convergence (ICTC) (pp. 220–223).Google Scholar
  15. 15.
    Hajjawi, A., & Ismail, M. (2015). A scheduling algorithm based self-learning technique for smart grid communications over 4G networks. Journal of Communications, 10, 876–881.Google Scholar
  16. 16.
    Iturralde, M., Yahiya, T. A., Wei, A., & Beylot, A.-L. (2011). Performance study of multimedia services using virtual token mechanism for resource allocation in LTE networks. In 2011 IEEE vehicular technology conference (VTC Fall) (pp. 1–5).Google Scholar
  17. 17.
    Nasralla, M. M., & Martini, M. G. (2013). A downlink scheduling approach for balancing QoS in LTE wireless networks. In 2013 IEEE 24th international symposium on personal indoor and mobile radio communications (PIMRC) (pp. 1571–1575).Google Scholar
  18. 18.
    Samia, D., & Ridha, B. (2015). A new scheduling algorithm for real-time communication in LTE networks. In 2015 IEEE 29th international conference on advanced information networking and applications workshops (WAINA) (pp. 267–271).Google Scholar
  19. 19.
    Li, Y.-P., Hu, B.-J., Zhu, H., Wei, Z.-H., & Gao, W. (2016). A delay priority scheduling algorithm for downlink real-time traffic in LTE networks. In Information technology, networking, electronic and automation control conference, IEEE, 2016 (pp. 706–709).Google Scholar
  20. 20.
    Mohsenian-Rad, A.-H., Wong, V. W., Jatskevich, J., Schober, R., & Leon-Garcia, A. (2010). Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Transactions on Smart Grid, 1, 320–331.CrossRefGoogle Scholar
  21. 21.
    Gungor, V. C., Sahin, D., Kocak, T., Ergut, S., Buccella, C., Cecati, C., et al. (2011). Smart grid technologies: Communication technologies and standards. IEEE Transactions on Industrial Informatics, 7, 529–539.CrossRefGoogle Scholar
  22. 22.
    Jorguseski, L., Zhang, H., Chrysalos, M., Golinski, M., & Toh, Y. (2017). LTE delay assessment for real-time management of future smart grids. In Smart grid inspired future technologies: First international conference, SmartGIFT 2016, Liverpool, UK, May 19–20, 2016, revised selected papers (pp. 204–213).Google Scholar
  23. 23.
    Gungor, V. C., Lu, B., & Hancke, G. P. (2010). Opportunities and challenges of wireless sensor networks in smart grid. IEEE Transactions on Industrial Electronics, 57, 3557–3564.CrossRefGoogle Scholar
  24. 24.
    Markkula, J., & Haapola, J. (2017). Ad hoc LTE method for resilient smart grid communications. Wireless Personal Communications, 98(4), 3355–3375.CrossRefGoogle Scholar
  25. 25.
    Ajiboye, S. O., Birch, P., Chatwin, C., & Young, R. (2015). Hierarchical video surveillance architecture: A chassis for video big data analytics and exploration. In IS&T/SPIE Electronic Imaging, 2015. (pp. 94070k-1–9040k-10).Google Scholar
  26. 26.
    Chiu, A., Ipakchi, A., Chuang, A., Qiu, B., Brooks, D., & Koch, E., et al. (2009). Framework for integrated demand response (DR) and distributed energy resources (DER) models. NAESB and UCAIug.Google Scholar
  27. 27.
    Mohagheghi, S., Stoupis, J., Wang, Z., Li, Z., & Kazemzadeh, H. (2010). Demand response architecture: Integration into the distribution management system. In 2010 first IEEE international conference on smart grid communications (SmartGridComm) (pp. 501–506).Google Scholar
  28. 28.
    Reid, M., Levy, R., & Silverstein, A. (2010). Coordination of energy efficiency and demand response. Ernest Orlando Lawrence Berkeley National Laboratory, Charles Goldman.Google Scholar
  29. 29.
    Motegi, N., Piette, M. A., Watson, D. S., Kiliccote, S. & Xu, P. (2007). Introduction to commercial building control strategies and techniques for demand response. Lawrence Berkeley National Laboratory LBNL-59975.Google Scholar
  30. 30.
    Elma, O., & Selamoğullari, U. S. (2017). An overview of demand response applications under smart grid concept. In 2017 4th international conference on electrical and electronic engineering (ICEEE) (pp. 104–107).Google Scholar
  31. 31.
    Siano, P. (2014). Demand response and smart grids: A survey. Renewable and Sustainable Energy Reviews, 30, 461–478.CrossRefGoogle Scholar
  32. 32.
    Alcaraz, C., & Lopez, J. (2014). WASAM: A dynamic wide-area situational awareness model for critical domains in Smart Grids. Future Generation Computer Systems, 30, 146–154.CrossRefGoogle Scholar
  33. 33.
    Mohassel, R. R., Fung, A., Mohammadi, F., & Raahemifar, K. (2014). A survey on advanced metering infrastructure. International Journal of Electrical Power and Energy Systems, 63, 473–484.CrossRefGoogle Scholar
  34. 34.
    Abbas, T., Qamar, F., Ahmed, I., Dimyati, K., & Majed, M. B. (2017). Propagation channel characterization for 28 and 73 GHz millimeter-wave 5G frequency band. In 2017 IEEE 15th student conference on research and development (SCOReD) (pp. 297–302).Google Scholar
  35. 35.
    Erol-Kantarci, M., & Mouftah, H. T. (2015). Energy-efficient information and communication infrastructures in the smart grid: A survey on interactions and open issues. IEEE Communications Surveys and Tutorials, 17, 179–197.CrossRefGoogle Scholar
  36. 36.
    Yigit, M., Gungor, V. C., Tuna, G., Rangoussi, M., & Fadel, E. (2014). Power line communication technologies for smart grid applications: A review of advances and challenges. Computer Networks, 70, 366–383.CrossRefGoogle Scholar
  37. 37.
    He, W., & Da Xu, L. (2014). Integration of distributed enterprise applications: A survey. IEEE Transactions on Industrial Informatics, 10, 35–42.CrossRefGoogle Scholar
  38. 38.
    Kuzlu, M., Pipattanasomporn, M., & Rahman, S. (2014). Communication network requirements for major smart grid applications in HAN, NAN and WAN. Computer Networks, 67, 74–88.CrossRefGoogle Scholar
  39. 39.
    Chakir, M., Kamwa, I., & Le Huy, H. (2014). Extended C37. 118.1 PMU algorithms for joint tracking of fundamental and harmonic phasors in stressed power systems and microgrids. IEEE Transactions on Power Delivery, 29, 1465–1480.CrossRefGoogle Scholar
  40. 40.
    Hindia, M. N., Reza, A. W., Noordin, K. A., & Chayon, M. H. R. (2015). A novel LTE scheduling algorithm for green technology in smart grid. PLoS ONE, 10, e0121901.CrossRefGoogle Scholar
  41. 41.
    Usman, A., & Shami, S. H. (2013). Evolution of communication technologies for smart grid applications. Renewable and Sustainable Energy Reviews, 19, 191–199.CrossRefGoogle Scholar
  42. 42.
    Hu, H., Kaleshi, D., Doufexi, A., & Li, L. (2015). Performance analysis of IEEE 802.11 af standard based neighbourhood area network for smart grid applications. In 2015 IEEE 81st vehicular technology conference (VTC Spring) (pp. 1–5).Google Scholar
  43. 43.
    Aldhaibani, J. A., Yahya, A., Ahmad, R., Omar, N., & Ali, Z. G. (2013). Effect of relay location on two-way DF and AF relay for multi-user system in LTE-A cellular networks. In Business engineering and industrial applications colloquium (BEIAC), 2013 IEEE (pp. 380–385).Google Scholar
  44. 44.
    Scheme, B. T. (2009). LTE: The evolution of mobile broadband. IEEE Communications Magazine, 45, 44–51.Google Scholar
  45. 45.
    Qamar, F., Dimyati, K. B., Hindia, M. N., Noordin, K. A. B., & Al-Samman, A. M. (2017). A comprehensive review on coordinated multi-point operation for LTE-A. Computer Networks, 123, 19–37.CrossRefGoogle Scholar
  46. 46.
    Lee, S.-B., Pefkianakis, I., Meyerson, A., Xu, S., & Lu, S. (2009). Proportional fair frequency-domain packet scheduling for 3GPP LTE uplink. In INFOCOM 2009, IEEE (pp. 2611–2615).Google Scholar
  47. 47.
    Hajjawi, A., Ismail, M., & Yuwono, T. (2015). Implementation of three scheduling algorithms in the smart grid communications over 4G networks. In 2015 international conference on space science and communication (IconSpace) (pp. 28–32).Google Scholar
  48. 48.
    Kalalas, C., Thrybom, L., & Alonso-Zarate, J. (2016). Cellular communications for smart grid neighborhood area networks: A survey. IEEE Access, 4, 1469–1493.CrossRefGoogle Scholar
  49. 49.
    Feng, F., Peng, F., Yan, B., Lin, S., & Zhang, J. (2017) QoS-based LTE downlink scheduling algorithm for smart grid communication. In 2017 IEEE 9th international conference on communication software and networks (ICCSN) (pp. 548–552).Google Scholar
  50. 50.
    Capozzi, F., Piro, G., Grieco, L. A., Boggia, G., & Camarda, P. (2013). Downlink packet scheduling in LTE cellular networks: Key design issues and a survey. IEEE Communications Surveys and Tutorials, 15, 678–700.CrossRefGoogle Scholar
  51. 51.
    Udeshi, D., & Qamar, F. (2014). Quality analysis of epon network for uplink and downlink design. Asian Journal of Engineering, Sciences and Technology, 4, 10–17.Google Scholar
  52. 52.
    Rebekka, B., & Malarkodi, B. (2014). Performance evaluation of resource allocation schemes in LTE downlink. In 2014 International conference on electronics and communication systems (ICECS) (pp. 1–4).Google Scholar
  53. 53.
    Basukala, R., Ramli, H. M., & Sandrasegaran, K. (2009) Performance analysis of EXP, PF and M-LWDF in downlink 3GPP LTE system. In First Asian Himalayas international conference on internet, 2009. AH-ICI 2009 (pp. 1–5).Google Scholar
  54. 54.
    Hindia, M. N., Reza, A. W., & Noordin, K. A. (2015). A novel scheduling algorithm based on game theory and multicriteria decision making in LTE network. International Journal of Distributed Sensor Networks, 11, 604752.CrossRefGoogle Scholar
  55. 55.
    Wang, J., Xia, C., Wang, Y., Ding, S., & Sun, J. (2012). Spatial prisoner’s dilemma games with increasing size of the interaction neighborhood on regular lattices. Chinese Science Bulletin, 57, 724–728.CrossRefGoogle Scholar
  56. 56.
    Ma, Z.-Q., Xia, C.-Y., Sun, S.-W., Wang, L., Wang, H.-B., & Wang, J. (2011). Heterogeneous link weight promotes the cooperation in spatial prisoner’s dilemma. International Journal of Modern Physics C, 22, 1257–1268.CrossRefGoogle Scholar
  57. 57.
    Iturralde, M., Wei, A., Ali-Yahiya, T., & Beylot, A.-L. (2013). Resource allocation for real time services in LTE networks: Resource allocation using cooperative game theory and virtual token mechanism. Wireless Personal Communications, 72, 1415–1435.CrossRefGoogle Scholar
  58. 58.
    Hajjawi, A., Ismail, M., & Abdullah, N. F. (2016). A scheduling scheme for smart grid and mobile users over LTE networks. In International conference on advances in electrical, electronic and systems engineering (ICAEES) (pp. 421-426).Google Scholar
  59. 59.
    O’Neill, B. (1982). A problem of rights arbitration from the Talmud. Mathematical Social Sciences, 2, 345–371.CrossRefGoogle Scholar
  60. 60.
    Procaccia, A. D., Shah, N., & Tucker, M. L. (2014). On the structure of synergies in cooperative games. In AAAI (pp. 763–769).Google Scholar
  61. 61.
    Andrews, J. G., Gupta, A. K., & Dhillon, H. S. (2016) A primer on cellular network analysis using stochastic geometry. arXiv preprint arXiv:1604.03183.
  62. 62.
    Taylor, H. M., & Karlin, S. (2014). An introduction to stochastic modeling. New York: Academic Press.Google Scholar
  63. 63.
    Dargie, W., & Schill, A. (2011). Stability and performance analysis of randomly deployed wireless networks. Journal of Computer and System Sciences, 77, 852–860.CrossRefGoogle Scholar
  64. 64.
    ElSawy, H., Hossain, E., & Haenggi, M. (2013). Stochastic geometry for modeling, analysis, and design of multi-tier and cognitive cellular wireless networks: A survey. IEEE Communications Surveys and Tutorials, 15, 996–1019.CrossRefGoogle Scholar
  65. 65.
    Dobkin, D., & Silver, D. (1990). Applied computational geometry: Towards robust solutions of basic problems. Journal of Computer and System Sciences, 40, 70–87.CrossRefGoogle Scholar
  66. 66.
    Lucarini, V. (2009). Symmetry-break in Voronoi tessellations. Symmetry, 1, 21–54.CrossRefGoogle Scholar
  67. 67.
    Guo, X., & Song, P. (2010). Simulink based LTE system simulator, M. Sci. thesis, Chalmers University of Technology, Goteborg, Sweden.Google Scholar
  68. 68.
    Kauser, N., Saw, J., & Gelbman, P. (2011). System and method for cell planning in a wireless communication network. Google Patents.Google Scholar
  69. 69.
    Shi, W., Zhu, Z., Zhang, M., & Ansari, N. (2013). On the effect of bandwidth fragmentation on blocking probability in elastic optical networks. IEEE Transactions on Communications, 61, 2970–2978.CrossRefGoogle Scholar
  70. 70.
    ETSI, T. (2000). 125 211 V3. 1.1 universal mobile telecommunications system (UMTS). Physical channels and mapping of transport channels onto physical channels (FDD) (3GPP TS 25.211 version 6.1. 0 Release 6) (pp. 0000–0001).Google Scholar
  71. 71.
    Singh, Y. (2012). Comparison of okumura, hata and cost-231 models on the basis of path loss and signal strength. International Journal of Computer Applications, 59, 37–41.CrossRefGoogle Scholar
  72. 72.
    Nguyen, S. C., Sandrasegaran, K., & Madani, F. M. J. (2011). Modeling and simulation of packet scheduling in the downlink LTE-advanced. In 2011 17th Asia-Pacific conference on communications (APCC) (pp. 53–57).Google Scholar
  73. 73.
    Sandrasegaran, K., Patachaianand, R., & Madani, F. M. (2010). Joint delay-aware opportunistic scheduling algorithm with reduced feedback to exploit multiuser diversity. In 2010 International conference on computer applications and industrial electronics (ICCAIE) (pp. 432–437).Google Scholar
  74. 74.
    Chung, W. G., Lim, E., Yook, J. G., & Park, H. K. (2007). Calculation of spectral efficiency for estimating spectrum requirements of IMT-advanced in Korean mobile communication environments. ETRI Journal, 29, 153–161.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • MHD Nour Hindia
    • 1
  • Faizan Qamar
    • 1
  • Mohammad B. Majed
    • 2
    • 3
  • Tharek Abd Rahman
    • 2
  • Iraj S. Amiri
    • 4
    • 5
  1. 1.Department of Electrical Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia
  2. 2.Wireless Communication Centre, Faculty of Electrical EngineeringUniversiti Teknologi MalaysiaJohor BahruMalaysia
  3. 3.College of Science and TechnologyUniversity of Human Development (UHD)AssulaymaniyahIraq
  4. 4.Computational Optics Research Group, Advanced Institute of Materials ScienceTon Duc Thang UniversityHo Chi Minh CityVietnam
  5. 5.Faculty of Applied SciencesTon Duc Thang UniversityHo Chi Minh CityVietnam

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