یک طرح چند دروازهای جهت بهبود CORPL تحت بار ترافیکی در شبکههای هوشمند انرژی مبتنی بر رادیو شناختگر با معماری مش
محورهای موضوعی : مهندسی برق و کامپیوترسیدعلی هاشمیان 1 * , وحید طباطباوکیلی 2
1 - دانشگاه علم و صنعت ايران
2 - دانشگاه علم و صنعت ايران
کلید واژه: شبکههای هوشمندرادیوی شناختگرارسال مجدد فرصتطلبانهتحلیل تأخیرمسیریابیشبکههای مشچنددروازهای,
چکیده مقاله :
شبکه قدرت فعلی اشکالات زیادی دارد. اخیراً یک شبکه جدید و هوشمند برای رفع کاستیهای شبکه فعلی معرفی شده که از آن با عنوان شبکه هوشمند انرژی یاد میشود. شبکه هوشمندی که بخواهد شبکه قدرت را بهطور کارامدی مدیریت کند، به یک زیرساخت مخابراتی برای برقراری ارتباط بین اجزای شبکه نیاز دارد. فناوری مخابراتی رادیو شناختگر بهمنظور بهرهبرداری کارامدتر از منابع طیفی رادیویی معرفی شده است. مسیریابی در این شبکهها باید توسط پروتکلی انجام شود که در مقابله با مشکلات ایجادشده توسط رادیو شناختگر گذردهی را حداکثر کند و تأخیر بستهها در آن حداقل و مناسب کاربرد شبکه هوشمند انرژی باشد. CORPL به عنوان پروتکلی که بخشی از این اهداف را محقق میسازد معرفی شده است. در این مقاله پروتکل CORPL تحت بار ترافیکی برست و پواسون بررسی میگردد و نشان داده میشود که با افزایش کاربران فعال در شبکه عملکرد CORPL افت پیدا خواهد کرد. سپس با استفاده از روابط ریاضی کران بالای میانگین تأخیر در پروتکل CORPL مدل شده و برای کاهش آن یک طرح چنددروازهای ارائه میگردد.
The conventional power grid has several drawbacks and a new powerful smart grid perspective has been recently introduced. The smart grid principle, allowing to efficiently manage an electrical grid network, needs to exploit a communication network for interconnecting the Smart Grid devices. An increasing interest is toward wireless communications due to their higher flexibility. Within this context cognitive radio (CR) techniques has been introduced aiming to exploit more efficiently the radio spectrum resources. In neighborhood area network (NAN), mesh grids can be considered as one of possible network topologies. In such networks no base station is required and data will be sent to gateway by means of nodes themselves. Hence, routing is one of the main issues in such networks. Routing in such networks should be done by a protocol which maximizes throughput against cognitive radio drawbacks and Packets delay in such protocol needs to be minimum and suitable for smart grids applications. CORPL has been introduced as a routing protocol to meet some of these goals. In this paper by CORPL functionality would be evaluated under burst and poisson traffic. It will be shown that by increasing active nodes, CORPL functionality would be decreased. Then average upper limit for delay would be mathematically modeled and to reduce that a multigate scheme would be introduced.
[1] Y. Yan, Y. Qian, H. Sharif, and D. Tipper, "A survey on smart grid communication infrastructures: motivations, requirements and challenges," IEEE Commun. Surv. Tutorials, vol. 15, no. 1, pp. 5-20, Feb. 2013.
[2] Q. D. Ho, Y. Gao, G. Rajalingham, and T. Le-Ngoc, Wireless Communications Networks for the Smart Grid, Cham: Springer International Publishing, 2014.
[3] S. Haykin, "Cognitive radio: brain-empowered wireless communications," IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201-220, Feb. 2005.
[4] S. Hu, Y. Yao, and Z. Yang, "MAC protocol identification using support vector machines for cognitive radio networks," IEEE Wirel. Commun., vol. 21, no. 1, pp. 52-60, Feb. 2014.
[5] Y. Zhang, R. Yu, M. Nekovee, Y. Liu, S. Xie, and S. Gjessing, "Cognitive machine-to-machine communications: visions and potentials for the smart grid," IEEE Netw., vol. 26, no. 3, pp. 6-13, May 2012.
[6] V. Gungor and D. Sahin, "Cognitive radio networks for smart grid applications: a promising technology to overcome spectrum inefficiency," IEEE Veh. Technol. Mag., vol. 7, no. 2, pp. 41-46, Jun. 2012.
[7] R. Deng, et al., "Sensing-delay tradeoff for communication in cognitive radio enabled smart grid," in Proc. IEEE Int. Conf. on Smart Grid Communications, SmartGridComm'11, pp. 155-160, Brussels, Belgium, 17-20 Oct. 2011.
[8] R. Deng, J. Chen, X. Cao, Y. Zhang, S. Maharjan, and S. Gjessing, "Sensing-performance tradeoff in cognitive radio enabled smart grid," IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 302-310, Mar. 2013.
[9] J. Huang, H. Wang, Y. Qian, and C. Wang, "Priority-based traffic scheduling and utility optimization for cognitive radio communication infrastructure-based smart grid," IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 78-86, Mar. 2013.
[10] X. Liu, X. Li, Y. Li, M. Zhao, and J. Wang, "A new TDD scheme and interference-aware precoding for device-to-device underlay massive MIMO," China Commun., vol. 13, no. 2, pp. 100-108, 2016.
[11] A. A. El-Sherif and A. Mohamed, "Decentralized throughput maximization in cognitive radio wireless mesh networks," IEEE Trans. Mob. Comput., vol. 13, no. 9, pp. 1967-1980, Sep. 2014.
[12] D. S. J. De Couto, D. Aguayo, J. Bicket, and R. Morris, "A high-throughput path metric for multi-hop wireless routing," Wirel. Networks, vol. 11, no. 4, pp. 419-434, Jul. 2005.
[13] A. Aijaz, H. Su, and A. Aghvami, "CORPL: a routing protocol for cognitive radio enabled AMI networks," IEEE Trans. Smart Grid, vol. 6, no. 1, pp. 477-485, Jan. 2015.
[14] Z. Liang, S. Feng, D. Zhao, and X. S. Shen, "Delay performance analysis for supporting real-time traffic in a cognitive radio sensor network," IEEE Trans. Wirel. Commun., vol. 10, no. 1, pp. 325-335, Jan. 2011.
[15] O. Al-Khatib, W. Hardjawana, and B. Vucetic, "Traffic modeling and optimization in public and private wireless access networks for smart grids," IEEE Trans. Smart Grid, vol. 5, no. 4, pp. 1949-1960, Jul. 2014.
[16] H. Gharavi and B. Hu, "Multigate communication network for smart grid," Proceedings of the IEEE, vol. 99, no. 6, pp. 1028-1045, Jun. 2011.
[17] H. Gharavi and C. Xu, "Traffic scheduling technique for smart grid advanced metering applications," IEEE Trans. Commun., vol. 60, no. 6, pp. 1646-1658, Jun. 2012.
[18] W. E. Leland, M. S. Taqqu, W. Willinger, and D. V. Wilson, "On the self-similar nature of ethernet traffic (extended version)," IEEE/ACM Trans. Netw., vol. 2, no. 1, pp. 1-15, Feb. 1994.
[19] R. Deng, J. Chen, C. Yuen, P. Cheng, and Y. Sun, "Energy-efficient cooperative spectrum sensing by optimal scheduling in sensor-aided cognitive radio networks," IEEE Trans. Veh. Technol., vol. 61, no. 2, pp. 716-725, Feb. 2012.
[20] S. Biswas and R. Morris, "ExOR : opportunistic multi-hop routing for wireless networks," in Proc. of the Conf. on Applications, Technologies, Architectures, and Protocols for Computer Communications-SIGCOMM'05, vol. 35, no. 4, pp. 133-144, Aug. 2005.
[21] W. Y. Lee and I. F. Akyildiz, "Optimal spectrum sensing framework for cognitive radio networks," IEEE Trans. Wirel. Commun., vol. 7, no. 10, pp. 3845-3857, Oct. 2008.
[22] G. Schaefer, F. Ingelrest, and M. Vetterli, "Potentials of opportunistic routing in energy-constrained wireless sensor networks," in [1] Y. Yan, Y. Qian, H. Sharif, and D. Tipper, “A Survey on Smart Grid Communication Infrastructures: Motivations, Requirements and Challenges,” IEEE Commun. Surv. Tutorials, vol. 15, no. 1, pp. 5–20,Feb.2013.
[23] [5] Y. Zhang, R. Yu, M. Nekovee, Y. Liu, S. Xie, and S. Gjessing, “Cognitive machine-to-machine communications: visions and potentials for the smart grid,” IEEE Netw., vol. 26, no. 3, pp. 6–13, May.2012.
[24] [8] R. Deng, J. Chen, X. Cao, Y. Zhang, S. Maharjan, and S. Gjessing, “Sensing-Performance Tradeoff in Cognitive Radio Enabled Smart Grid,” IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 302–310, Mar. 2013.
[25] [9] J. Huang, H. Wang, Y. Qian, and C. Wang, “Priority-Based Traffic Scheduling and Utility Optimization for Cognitive Radio Communication Infrastructure-Based Smart Grid,” IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 78–86, Mar. 2013.
[26] [10] X. Liu, X. Li, Y. Li, M. Zhao, and J. Wang, “A new TDD scheme and interference-aware precoding for device-to-device underlay massive MIMO,” China Commun., vol. 13, no. Supplement2, pp. 100–108, 2016.
[27] [12] D. S. J. De Couto, D. Aguayo, J. Bicket, and R. Morris, “A high-throughput path metric for multi-hop wireless routing,” Wireless Networks, vol. 11, no. 4, pp. 419-434, Jul. 2005.
[28] [15] O. Al-Khatib, W. Hardjawana, and B. Vucetic, “Traffic Modeling and Optimization in Public and Private Wireless Access Networks for Smart Grids,” IEEE Trans. Smart Grid, vol. 5, no. 4, pp. 1949–1960, Jul. 2014.
[29] [18] W. E. Leland, M. S. Taqqu, W. Willinger, and D. V. Wilson, “On the self-similar nature of Ethernet traffic (extended version),” IEEE/ACM Trans. Netw., vol. 2, no. 1, pp. 1–15, Feb. 1994.
[30] [20] S. Biswas and R. Morris, “ExOR: opportunistic multi-hop routing for wireless networks,” in Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications - SIGCOMM ’05, vol. 35, no. 4, pp. 133-144, Aug. 2005.
[31] [21] Won-Yeol Lee and I. F. Akyildiz, “Optimal spectrum sensing framework for cognitive radio networks,” IEEE Trans. Wirel. Commun., vol. 7, no. 10, pp. 3845–3857, Oct. 2008.
[32] [22] G. Schaefer, F. Ingelrest, and M. Vetterli, “Potentials of Opportunistic Routing in Energy-Constrained Wireless Sensor Networks,” in Roedig and C. J. Sreenan, Eds., EWSN 2009: Wireless Sensor Networks, vol. 5432, vol. 5432, Berlin, Heidelberg: Springer, 2009, pp. 118-133,
[33] P. Embrechts and M. Maejima, Selfsimilar Processes, Princeton, N.J.: Princeton University Press, 2002.
[34] A. Papoulis, Probability, Random Variables, and Stochastic Processes, 1990.