Enhancing Wi-SUN AMI Network Resilience by using Emergency Gateway with Optimal Placement

Enhancing Wi-SUN AMI Network Resilience by using Emergency Gateway with Optimal Placement
Title:
Enhancing Wi-SUN AMI Network Resilience by using Emergency Gateway with Optimal Placement
Other Titles:
2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)
Keywords:
Publication Date:
15 June 2021
Citation:
Boonkajay, A., Hui Tan, P., Kee Goh, L., Naveen Altaf Ahmed, S., & Sun, S. (2021). Enhancing Wi-SUN AMI Network Resilience by using Emergency Gateway with Optimal Placement. 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring). doi:10.1109/vtc2021-spring51267.2021.9449024
Abstract:
Radio interference or jamming can cause isolated area in advanced metering infrastructure (AMI) based on Wireless smart utility network (Wi-SUN), in which conventional recovery techniques cannot cope with. In this paper, we deploy narrowband internet-of-things (NB-IoT) interface to some smart meters to act as emergency gateway, called ResiLite. An optimal ResiLite placement algorithm for enhancing network resilience is proposed. We define an implicit resilience metric based on path diversity and cluster closeness. The defined metric is used to form an integer linear programming (ILP) problem, then we solve the ILP to obtain optimal ResiLite placement. Simulation results show that the optimal ResiLite placement improves Wi-SUN AMI network resilience (defined as the number of surviving nodes with packet delivery ratio (PDR) above 99% under disturbance) by up to 167% compared to an AMI without ResiLite, and up to 29% compared to uniformly random ResiLite placement, respectively.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation, Singapore - Energy Programme
Grant Reference no. : NRF2017EWT-EP003-047
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
9781728189642
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