Open Access Open Access  Restricted Access Subscription or Fee Access

An Energy Efficient Network Architecture and Spectrum Sharing Technique for Cognitive Radio Based Smart Grid Communications

(*) Corresponding author

Authors' affiliations



With the introduction of an automated electric power system known as smart grid, issues relating to power quality, shortages and inefficiency have been solved. Two vital foundations necessary for the sustainability and the stability of Smart Grids (SG) are a resourceful and reliable communication between users in a smart grid network and an energy efficient Smart Grid Communication Network (SGCN) architecture. On the other hand, Cognitive radios (CR) are equipped with the capability of exploiting unused licensed spectrum resources for effective spectrum utilization. The objective of this paper is to integrate cognitive radio into smart grid communication network in order to make the network a lot smarter and provide spectrum sharing solutions in SGCN. In this paper, an energy efficient SGCN architecture that is layered into the Home Area Network (HAN), Neighbourhood Area Network (HAN) and the Wide Area Network (WAN) has been proposed. Furthermore, in order to achieve a fair spectrum sharing amongst users in the network, CR users in the HANs estimate the need of its neighbouring users to transmit data. Then it uses this information to predict the vacant channels that will be utilized by any node in the network using a Markov chain process. In the NANs, a model is also proposed where every user sensor node access the spectrum based on the buffer occupancy estimates of its neighboring nodes. Simulation results show that there is a higher throughput in the network and also an increase in the quantity of data packets successfully transmitted using the proposed model.
Copyright © 2020 Praise Worthy Prize - All rights reserved.


Smart Grid; Communication Network; Cognitive Radio; Spectrum Sensing; Energy Efficiency

Full Text:



Yu, R., Zhang, Y., Gjessing, S., Yuen, C., Xie, S., Guizani, M., Cognitive radio based hierarchical communications infrastructure for smart grid. (2011), IEEE Networks, 25(1), pp. 6–14.

Diovu, R., Agee, J., Smart Grid Advanced Metering Infrastructure: Overview of Cloud-Based Cyber Security Solutions, (2018) International Journal on Communications Antenna and Propagation (IRECAP), 8 (4), pp. 302-314.

Ananthavijayan, R., Karthikeyan Shanmugam, P., Padmanaban, S., Holm-Nielsen, J., Blaabjerg, F., & Fedak, V. (2019). Software Architectures for Smart Grid System—A Bibliographical Survey. Energies, 12(6), 1183.

Report of the Unlicensed Devices and Experimental Licenses Working Group Federal Communications Commission Spectrum Policy Task Force November 15, 2002. Available online: (Accessed on 12 May, 2019).

Haykin, S. Cognitive radio: brain-empowered wireless communications,. (2005). IEEE Journal on Selected Areas in Communications,. 23(12), pp. 201–220.

Li, F., Lam, K.-Y., Sheng, Z., Zhang, X., Zhao, K., & Wang, L. (2018). Q-Learning-Based Dynamic Spectrum Access in Cognitive Industrial Internet of Things. Mobile Networks and Applications.

Orumwense, E., Afullo, T., and Srivastava, V., Energy efficiency in cognitive radio networks: A holistic overview, (2016) International Journal of Communication Networks and Information Security, 8(2), pp. 75-85.

Akyildiz, I.F.,. Lee, W.-Y., Vuran, M. C., and Mohanty, S. NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey,. (2006). Computer Networks,.50(13), pp. 2127–2159.

Murat, K., Manisa, P., Saifur, R. Communication network requirements for major smart grid applications in HAN, NAN and WAN (2014). Computing Networks, 67(4), pp. 74- 88.

Li, Z., & Liang, Q. Capacity Optimization in Heterogeneous Home Area Networks With Application to Smart Grid. (2016). IEEE Transactions on Vehicular Technology, 65(2), pp. 699–706.

Deng, X., Peng, Q., He, L., & He, T. Interference-aware QoS routing for neighbourhood area network in smart grid. (2017). IET Communications, 11(5), 756–764.

Alam, S., Sohail, M., Ghauri, S., Qureshi, I., Aqdas, N. Cognitive based smart grid communication network, (2017). Renewable and Sustainable Energy Reviews,. 72(1), pp. 535-548.

Tariq F. and Dooley. L., Smart grid communications and networking technologies: Recent developments and future challenges. (2012). Green Energy and Technology, Springer Verlag London. pp. 199-213.

Soufiane, Z., Abdeslam, E., & Slimane, B. A new communication architecture model for smart grid (2018). International Journal of Computer Science and Information Security, 16(7) pp.129-143.

Uddin, F. Energy-Aware Optimal Data Aggregation in Smart Grid Wireless Communication Networks. (2017). IEEE Transactions on Green Communications and Networking, 1(3), pp. 358–371.

Biagi, M., Greco, S., & Lampe, L. (2018). Geo-Routing Algorithms and Protocols for Power Line Communications in Smart Grids. IEEE Transactions on Smart Grid, 9(2), 1472–1481.

Sarkis, G., Georges, S., Slaoui, F., A Novel Algorithm for Smart Grids-Optimal Load Scheduling, (2018) International Review on Modelling and Simulations (IREMOS), 11 (2), pp. 67-75.

Elwekeil, M., Abdalzaher, M. S., & Seddik, K. Prolonging Smart Grid Network Lifetime through Optimising Number of Sensor Nodes and Packet Length. (2019). IET Communications, 13(16), pp. 2478–2484.

Ghassemi, A., Bavarian, S., and Lampe, L. “Cognitive radio for Smart Grid communications. Proceedings of the 1st IEEE International Conference on Smart Grid Communications. Gaithersburg, USA, October 2010, pp. 297 – 302.

Ma, K., Liu, P., Yang, J., Wei, X., & Dou, C.-X. (2018). Spectrum Allocation and Power Optimization for Demand-Side Cooperative and Cognitive Communications in Smart Grid. IEEE Transactions on Industrial Informatics, 1–1.

Fathi, M. (2018). A Spectrum Allocation Scheme Between Smart Grid Communication and Nehighbor Communication Networks. IEEE Systems Journal, 12(1), 465–472.

Shah, G.A., Gungor, V.C., and Akan. O.B., A cross-layer design for qos support in cognitive radio sensor networks for smart grid applications. In Proceedings of the IEEE International Conference on Communications (ICC), Otawwa, Canada June 10-15, 2012.

Shah, G.S., Gungor, V.C., and Akan, O.B., A cross-layer qos-aware communication framework in cognitive radio sensor networks for smart grid applications. (2013). IEEE Transactions on Industrial Informatics, 9(1), pp. 1477-1485.

N. Yardav, R. Misra, S. Bhagat. Spectrum access in cognitive smart-grid communication system with prioritized traffic. Ad-Hoc Networks, 65(1), pp. 38-54, 2017.

Huang, J., Wang, H., Qian, Y., and Wang, C. Priority-based traffic scheduling and utility optimization for cognitive radio communication infrastructure-based smart grid. 2013. IEEE Transactions on Smart Grid,. 4(1).

Alam, S., Malik, A.N., Qureshi, I., Ghauri, S. and Safraz M. Clustering-based channel allocation scheme for Neigbourhood Area Network in a cognitive radio based smart grid communication. (2018). IEEE Access,. Volume. 6, pp.25773-25784.

Erol-Kantarci M, Mouftah HT. “Energy-efficient Information and communication Infrastructures in the Smart grid: A survey on interactions and open issues (2005). Commun Surv Tutor, 17(1), pp. 179–97.

Orumwense, E., Oyerinde, O, and Mneney, S. Improving trustworthiness amongst nodes in cognitive radio networks, In the Proceedings of Southern Africa Telecommunications Networks and Applications (SATNAC 2014). Port Elizabeth, South Africa, August, 2014. pp. 401 – 406.

Ahmed, E. Gani, A., Abolfazli, S., Jie Yao, L., Khan, S. Channel assignment algorithms in cognitive radio networks: Taxonomy, open issues, and challenges (2016). IEEE Communications Survey & Tutorials, 18(1).

Cormio C. and Chowdhury, K. A survey on mac protocols for cognitive radio networks (2009) Ad Hoc Networks, 7(7). pp. 1315-1329.

Levorato, M and Urbashi M. Optimal allocation of heterogeneous smart grid traffic to heterogeneous networks. (2011). In Proceedings of the IEEE International Conference on Smart Grid Communications (SmartGridComm), Brussels, Belgium, 17-20.

Sahu N. and Dehalwar, V. Intelligent machine to machine communication in home area network for smart grid. In Proceedings of the International Conference on Computing, Communication and Networking Technology (ICCCNT), Coimbatore, India, pp. 1-6, July, 2012.

Rehmani, M.H., A. Viana, C., Khalife, H., and Serge, F. Surf: A distributed channel selection strategy for data dissemination in multihop cognitive radio networks, (2013). Computer Communications,. 36(10), pp. 1172-1185.

V. C. Gungor, B. Lu, and G. P. Hancke. Opportunities and challenges of wireless sensor networks in smart grid. (2010). IEEE Transactions on Industrial Electronics,. 57(1), pp. 3557-3564.

Alnabelsi, S., Finding an Immuned Path Against Single Primary User Activity in Cognitive Radio Networks, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (7), pp. 562-571.

Jhajj, H., Garg, R., Saluja, N., Efficient Spectrum Sensing in Cognitive Radio Networks Using Hybridized Particle Swarm Intelligence and Ant Colony Algorithm, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (7), pp. 586-593.

Zambrano, D., Salcedo, O., Espitia, M., Modelling and Predicting the Behaviour of a Secondary User in Cognitive Radio Using Artificial Intelligence Techniques, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (4), pp. 348-355.

Menniti, D., Sorrentino, N., Pinnarelli, A., Brusco, G., Barone, G., Motta, M., Burgio, A., A Laboratory-Scale Prototype of a Smart User Network with DBS Control, (2017) International Review of Electrical Engineering (IREE), 12 (6), pp. 485-497.

Zambrano, A., Rosero Garcia, J., Assessment of the Impact of Dynamic Rating on Reliability Indices of Level II Systems, (2018) International Review of Electrical Engineering (IREE), 13 (2), pp. 165-171.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize