Matched Channel Allocation for Advanced Metering Infrastructure based on Cognitive Mobile Virtual Network Operator

This paper analyses a matched channel allocation algorithm for Advanced Metering Infrastructure (AMI) for Smart Grid (SG) with connectivity provided by a Cognitive Mobile Virtual Network Operator (C-MVNO). The proposed algorithm studies the behavior of secondary-spectrum channels assigned to the AMI using the cellular network. In this work, we consider the Smart Meter (SM) as a device-cell without mobility with capabilities of connecting and disconnecting the output electric power. According to the efficiency and reliability requirements of Smart Grid, we consider the spectrum reuse in the MVNO to avoid the need of deployment the communications infrastructure of the AMI from cero. Here, we optimize the dynamic channel assignment by taking advantage of white holes present in the spectrum using a cognitive radio. This work, articulates the coexistence of different types of uplink channels, such as random, reserved and fixed, and a strategic model with C-MVNO, MVNO, based on traditional cellular networks.

[1]  Loretta Mastroeni,et al.  Spectrum reservation options for Mobile Virtual Network Operators , 2010, 6th EURO-NGI Conference on Next Generation Internet.

[2]  Jian Tang,et al.  QoS Routing in Wireless Mesh Networks with Cognitive Radios , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[3]  Hamid Gharavi,et al.  Traffic Scheduling Technique for Smart Grid Advanced Metering Applications , 2012, IEEE Transactions on Communications.

[4]  D. F. R. Hincapie,et al.  Evaluation of mesh-under and route-over routing strategies in AMI systems , 2012, 2012 IEEE Colombian Communications Conference (COLCOM).

[5]  Mustafa ElNainay,et al.  A cooperative spectrum sensing scheme based on task assignment algorithm for cognitive radio networks , 2014, 2014 International Wireless Communications and Mobile Computing Conference (IWCMC).

[6]  Li Zhang,et al.  Efficient Recovery Algorithms for Wireless Mesh Networks with Cognitive Radios , 2009, 2009 IEEE International Conference on Communications.

[7]  Esteban Inga,et al.  Optimal deployment of cellular networks for Advanced Measurement Infrastructure in Smart Grid , 2014, 2014 IEEE Colombian Conference on Communications and Computing (COLCOM).

[8]  Shuqin Li,et al.  Dynamic Profit Maximization of Cognitive Mobile Virtual Network Operator , 2012, IEEE Transactions on Mobile Computing.

[9]  Weihua Zhuang,et al.  Capacity of multi-hop wireless network with frequency agile software defined radio , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[10]  Mohammed Abdel-Hafez,et al.  Spectrum sharing with quality of service assurance in cognitive radio networks , 2014, 2014 10th International Conference on Innovations in Information Technology (IIT).

[11]  Mourad Debbabi,et al.  Communication security for smart grid distribution networks , 2013, IEEE Communications Magazine.

[12]  Elias Yaacoub,et al.  Automatic meter reading in the smart grid using contention based random access over the free cellular spectrum , 2014, Comput. Networks.

[13]  Kranthimanoj Nagothu,et al.  Persistent Net-AMI for Microgrid Infrastructure Using Cognitive Radio on Cloud Data Centers , 2012, IEEE Systems Journal.

[14]  Jian Tang,et al.  Fair Bandwidth Allocation in Wireless Mesh Networks With Cognitive Radios , 2010, IEEE Transactions on Vehicular Technology.

[15]  Jianwei Huang,et al.  Cognitive Mobile Virtual Network Operator: Investment and Pricing with Supply Uncertainty , 2009, 2010 Proceedings IEEE INFOCOM.

[16]  Esteban Inga,et al.  Shortest path for optimal routing on Advanced Metering Infrastructure using cellular networks , 2015, IEEE Colombian Conference on Communication and Computing (IEEE COLCOM 2015).

[17]  Mohsen Guizani,et al.  Cognitive radio based hierarchical communications infrastructure for smart grid , 2011, IEEE Network.

[18]  Daniel Ospina,et al.  Opportunistic Spectrum Access approach in heterogeneous wireless scenario , 2010, 2010 IEEE Latin-American Conference on Communications.

[19]  Esteban Inga Redes de Comunicación en Smart Grid , 2012 .

[20]  Frank Y. Li,et al.  On the Performance of Channel Assembling and Fragmentation in Cognitive Radio Networks , 2014, IEEE Transactions on Wireless Communications.

[21]  Brian M. Sadler,et al.  Opportunistic Spectrum Access via Periodic Channel Sensing , 2008, IEEE Transactions on Signal Processing.

[22]  Tetsuo Otani,et al.  Characteristics of AMI using DLMS/COSEM and IEEE 802.15.4g multi-hop wireless communication , 2013, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[23]  Sangjae Lee,et al.  Implementation of IEEE 802.15.4g wireless communication platform for smart utility service , 2013, 2013 IEEE Third International Conference on Consumer Electronics ¿ Berlin (ICCE-Berlin).

[24]  Kyung-Geun Lee,et al.  IMS: Interference minimization scheme for cognitive radio networks using Hungarian algorithm , 2012, The First International Conference on Future Generation Communication Technologies.

[25]  Amitava Ghosh,et al.  Performance of Low-Cost LTE Devices for Advanced Metering Infrastructure , 2013, 2013 IEEE 77th Vehicular Technology Conference (VTC Spring).

[26]  Siba K. Udgata,et al.  Swarm intelligence based Resource Allocation Algorithm for cognitive radio network , 2010, 2010 First International Conference On Parallel, Distributed and Grid Computing (PDGC 2010).

[27]  Chonggang Wang,et al.  Leveraging load migration and basestaion consolidation for green communications in virtualized Cognitive Radio Networks , 2013, 2013 Proceedings IEEE INFOCOM.

[28]  Rui Zhang,et al.  On Design of Opportunistic Spectrum Access in the Presence of Reactive Primary Users , 2013, IEEE Transactions on Communications.

[29]  Jamil Y. Khan,et al.  A comprehensive review of the application characteristics and traffic requirements of a smart grid communications network , 2013, Comput. Networks.

[30]  Esteban Inga,et al.  Optimum deployment of FiWi Networks using wireless sensors based on Universal Data Aggregation Points , 2015, IEEE Colombian Conference on Communication and Computing (IEEE COLCOM 2015).

[31]  Lutz H.-J. Lampe,et al.  Cost-efficient data aggregation point placement for advanced metering infrastructure , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).