1. Presentation of Master’s thesis
Simulation and Analysis of Wireless Mesh Network In Smart Grid / Advanced Metering Infrastructure
Philip Huynh

2. Outline of the Talk Introduction Related work
Real-time Smart Grid Meter Data Collection using Hybrid WiMAX/Wi-Fi Networks
Smart Grid Wireless Infrastructure Planning (SG-WIP) Tool.
Simulation Results of SGSim
Lesson Learned
Future Direction
Conclusion

3. Introduction
What is smart grid?
Smart grid conceptual framework

4. Introduction cont. The need to collect metering data in real-time
Save the material usage to generate the electric power by correctly predict the load demand and build the load profile
Meter Data Warehousing

5. Research opportunity & Goals
Academic researches provides the nominal evaluation of the performance of WMN. For example:
Commercial products in the smart grid field has not supported the real-time metering data collection. An important issue causes low performance in their WMN is the network architecture.
Research Opportunities
WMN architecture: high performance, low cost
Methodology for evaluating the performance of WMN in real-time metering data collection
Goals
Develop techniques and tools to evaluate the performance of WMN in smart grid / AMI

6. CSU AMI Infrastructure

7. Related Work “Wireless Mesh Networks: A Survey” [AWW05]
The author presented many open research issues needed to be solved such as scalability, self-organization and self-configuration, security, network integration. The critical factors influencing protocol design were discussed for improvement objectives.
“The Nominal Capacity of Wireless Mesh Networks” [JS03]
The authors shown that for WMNs the throughput of each node decreases as O(1/n), where n is total number of nodes in the network. Moreover, for a given topology and the set of active nodes, the upper bounds on the throughput of any node can be exactly calculated.
“Capacity of Grid-Oriented Wireless Mesh Networks” [ANMK08]
The author presented an analytical framework for determining the nominal capacity of multi-radio multi-channel Wireless Mesh Network (WMN). As the research conclusion, the effects of WMN design parameters such as network topology, network size, routing methods, channel assignment schemes etc. are interlinked and a judicious selection is essential to maximize capacity.

8. Related Work cont.
“Architecture and Algorithms for an IEEE based Multi-channel Wireless Mesh Network” [RC05]
The author proposed a novel multi-channel WMN architecture that effectively addresses the bandwidth problem by fully exploiting non-overlapped radio channels that the IEEE standards make available.
“Multi-Channel Mesh Networks: Challenges and Protocols” [KSCV06]
The authors considered the use of multi-channel to improve the throughput of Wireless Mesh Network (WMN). The main challenges were highlighted and two link-layer protocols were presented for utilizing multiple channels
“Coverage and capacity of a wireless mesh network” [HWC05]
The authors proposed a scalable multi-channel ring-based WMN architecture and developed an analytical framework to evaluate the capacity and coverage of such a network.

9. Related Work cont.
“The IEEE s Extended Service Set Mesh Networking Standard” [CK08]
The author presented how the developing IEEE s ESS Mesh Networking Standard draft addresses technical challenges of the pervasive development of wireless mesh networks (WMNs), the efficient allocation of mesh resources (routing and MAC layers), the protection of network resources (security and power savings), and the elimination of spatial bias (congestion control).
“An Improved IEEE WiMAX Module for the ns-3 Simulator” [IPGT10]
The authors presented the new features and enhancements that were integrated within the ns-3 WiMAX module. These proposed features can make easier and more realistic the evaluation and design of WiMAX systems.

10. HW2N for SG Meter Data Collection
Example of WAN topology (WiMAX)
Example of NAN topology (Wi-Fi)
Hybrid WiMAX/Wi-Fi Network Model

11. SG-WIP Planning Tool
Visually planning the Antenna mounting place for the WiMAX/Wi-Fi WMN
This is a mashup that overlays the Wireless Infrastructure, GIS data (street light poles, housing units) using the Google Maps.
Export the network topology as file for further research
Can be integrated to the network simulator

12. SG-WIP Tool

13. SG-WIP Tool cont. WAN Grid 10x10 Topology, 10 km x 10 km (WxH)
NAN Grid 10x10 Topology, 1 km x 1 km (WxH)

14. SG-WIP Tool cont. LAN Square Topology, 100 m x 100 m (WxH)
Topology is exported as XML file

15. SG-WIP Tool cont. Before changing the WiMAX antennae
After changing the WiMAX antennae

16. SG-WIP Tool cont.
Google Maps mashup code

17. SG-SIM Simulator
Implement the proposed hybrid WiMAX/Wi-Fi Network Model in NS-3 Platform
Tools used (created by others…; give credits to them)
Parameters of the Simulator
Network types: WAN, MAN, NAN, LAN
Number of nodes, Transmission Rate
Others: network initialization time,…

18. SG-SIM Simulator cont.
NS-3 simulator code

19. Simulation Experiments
Experiment Design Vision
Evaluate the performance of AMI Infrastructure
Trade-off between Scalability and Performance
Confirm to smart meter density analysis (using SG-WIP)

20. LAN Simulation Results
Tx packets = Rx packets = 50 (each sec)
Delay time converges to 10 msecs
Total processing delay converges to 10 msecs
Tx packets = Rx packets
Total processing delay increases linearly with the number of smart meter

21. NAN Simulation Results
Tx packets = Rx packets = 450 (each sec)
Delay time converges to 200 usecs
Total processing delay converges to 10 msecs
Tx packets = Rx packets
Total processing delay increases rapidly with the number of mesh routers

22. MAN Simulation Results
Tx packets = 1,800 (each sec)
Rx packets = Tx ± 5 (each sec)
Delay time converges to 5.5 msecs
Total processing delay converges to 930 msecs
Tx packets = Rx packets
Total processing delay is in narrow range [930, 960] msecs. It confirms to 5 msecs fixed frame time in std.

23. MAN Simulation Results cont.
Impact On The Network Performance By Aggregating Meter Data
Impact On The Network Performance By Aggregating Meter Data
MAN Simulation Results cont.
Impact on the network performance by aggregating meter data at the gateway
Tx packets = Rx packets when number of meter data packets < 16
Tx packets > Rx packets when number of meter data packets >= 16 (overloaded)
Total processing delay increases linearly with the number of meter packets

24. WAN Simulation Results

25. WAN Simulation Results cont.

26. Google Maps service + GIS infrastructure information
Lessons Learned
A useful mashup for visual planning AMI infrastructure
Google Maps service + GIS infrastructure information
Development of SG-WIP Tool
Challenges in testing and debugging source code for Web application (used PHP/JavaScript)
GIS Information Acquisition: time consuming process
Development of SG-SIM Simulator
Found the bug in NS-3 WiMAX module that can affect the simulation results and reported to NS-3 community at:
Simulation Experiments in NS-3
The initialization phase of wireless networks

27. Future work Fully integrate the SG-WIP tool with SG-SIM simulator
Improve the antenna placement algorithm
Increase availability of wireless networks
Database systems for storing the real-time meter data

28. Conclusion
The proposed WiMAX/Wi-Fi WMN can transport the meter data from 160,000 smart meters in the CSU service areas to the data center in one second.
The high scalability property of WiMAX/Wi-Fi WMN helps flexibly extend the coverage area of the AMI wireless infrastructure without degrading the network performance.
The proposed WiMAX/Wi-Fi infrastructure allows the utilities deploying an AMI wireless communication infrastructure not only at low cost of installation and maintenance but also with high performance, scalability, and security.

29. Demo Illustrate network topology planning with SG-WIP Tool
Some demonstrations of SG-SIM simulator

30. References
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[NIST10] National Institute of Standards and Technology, “NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 1.0”, Jan
[NETL08] National Energy Technology Laboratory, white paper “Advanced Metering infrastructure”, February 2008.
[Chow09] Edward Chow, Lecture “Secure Smart Grids”, Department of Computer Science, University of Colorado at Colorado Springs, 2009.
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[PSC06] Michael Purvis, Jeffrey Sambells, and Cameron Turner, “Beginning Google Maps Applications with PHP and Ajax”, Apress, 2006.
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