TY - GEN
T1 - Delay Sensitivity-Aware Aggregation of Smart Microgrid Data over Heterogeneous Networks
AU - Omara, Ahmed
AU - Kantarci, Burak
AU - Nogueira, Michele
AU - Erol-Kantarci, Melike
AU - Wu, Lei
AU - Li, Jie
N1 - Funding Information:
This work was supported in part by the U.S. National Science Foundation under Grant CNS-1647135, in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) under the DISCOVERY Program, and in part by the Visiting Researcher Program at the University of Ottawa. The authors would like to thank Wendong Yuan for his inputs to the earlier version of the simulation environment.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Smart grids require high reliability and sufficient bandwidth from wireless networks to support critical real-time applications and massive smart microgrid data. In general, smart microgrids need to guarantee delays at the order of a few μs for highly delay-sensitive data delivery; as well as delays within few seconds for regular data delivery. This paper presents a framework and its performance analysis for microgrid data aggregation where the microgrid is served by a wireless heterogeneous network. Using unsupervised machine learning, the framework introduces a multi-class and delay sensitivity-aware aggregation of microgrid data within the small cells of the heterogeneous network to ensure that clustering reduces the processing time for highly delay-sensitive messages. Thus, at each Transmission Time Interval (TTI), if there is queued delay-sensitive data, they are dequeued ahead of the delay-tolerant data at the scheduler. Through simulations, we show that the proposed approach successfully reduces the queuing delay by 93% for the packets of delay-sensitive (urgent) messages and the Packet Loss Rate (PLR) by 7% when compared to the benchmark where no aggregation mechanism exists prior to the small cell base stations.
AB - Smart grids require high reliability and sufficient bandwidth from wireless networks to support critical real-time applications and massive smart microgrid data. In general, smart microgrids need to guarantee delays at the order of a few μs for highly delay-sensitive data delivery; as well as delays within few seconds for regular data delivery. This paper presents a framework and its performance analysis for microgrid data aggregation where the microgrid is served by a wireless heterogeneous network. Using unsupervised machine learning, the framework introduces a multi-class and delay sensitivity-aware aggregation of microgrid data within the small cells of the heterogeneous network to ensure that clustering reduces the processing time for highly delay-sensitive messages. Thus, at each Transmission Time Interval (TTI), if there is queued delay-sensitive data, they are dequeued ahead of the delay-tolerant data at the scheduler. Through simulations, we show that the proposed approach successfully reduces the queuing delay by 93% for the packets of delay-sensitive (urgent) messages and the Packet Loss Rate (PLR) by 7% when compared to the benchmark where no aggregation mechanism exists prior to the small cell base stations.
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U2 - 10.1109/ICC.2019.8761083
DO - 10.1109/ICC.2019.8761083
M3 - Conference contribution
AN - SCOPUS:85070202963
T3 - IEEE International Conference on Communications
BT - 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Communications, ICC 2019
Y2 - 20 May 2019 through 24 May 2019
ER -