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Modelling short-range path loss in smart meter mesh networks

University of British Columbia

Electrical power utilities have begun to deploy IPv6-based Low power and Lossy wireless mesh Networks (LLNs) in suburban areas to support smart metering, distribution automation, interaction with customers, and demand response. To date, most insights concerning network performance have been obtained through simulations based upon simplistic channel models and measurements conducted using testbeds of limited size and extent. Here, we seek to overcome the limitations of previous work and develop better insights into the factors that affect LLN performance by analyzing data collected from BC Hydro’s 1.9-million-node Multi-Service Grid Network (MSGN). The network includes both device-to-device (D2D) and device-to-infrastructure (D2I) links. First, we review the essential aspects of the MSGN and propose an incremental strategy based on using network performance data to: 1) develop measurement-based short-range path loss models that will make network simulations more accurate, 2) identify correlations between network layout and performance, and 3) develop schemes for optimizing network performance through infilling relay nodes when the node density is low and transmit power adjustment when the node density is high. Second, we conclude that because the links are obstructed, power law path loss models generally apply. We show how distance errors or finite distance spans may degrade estimates of the model parameters in typical environments and should be accounted for in the development of path loss models for LLNs. Third, after developing procedures for managing and preparing such data for analysis, we reduce path loss data collected in three representative areas with hilly terrain and light vegetation, hilly terrain and heavy foliage, and flat terrain and light foliage and compare the results to previous work. The results show that: 1) modelling path loss using data collected over short distance spans and with substantial distance errors is challenging but can be accomplished with appropriate care, 2) the path loss experienced over such short D2D and D2I links is fairly independent of terrain and foliage density, and 3) data analytics can greatly improve modelling efficiency and support network tuning and optimization. The results serve as an important foundation for the remainder of our proposed strategy.

Text

2016-05-03

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58047

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/