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 - 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.
UR - http://www.scopus.com/inward/record.url?scp=85070202963&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070202963&partnerID=8YFLogxK
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
T2 - 2019 IEEE International Conference on Communications, ICC 2019
Y2 - 20 May 2019 through 24 May 2019
ER -