TY - GEN
T1 - Distributionally Robust Optimization for Peak Age of Information Minimization in E-Health IoT
AU - Ling, Zhuang
AU - Hu, Fengye
AU - Zhang, Hongliang
AU - Han, Zhu
AU - Poor, H. Vincent
N1 - Funding Information:
This research was supported in part by Jilin Provincial Science and Technology Department Key Scientific and Technological Project (No.20190302031GX), Changchun Scientific and Technological Innovation Double Ten Project (No.18SS010), National Natural Science Foundation of China (No.61671219), Jilin Province Development and Reform Commission Project (No.2017C046-3), in part by Chinese Scholarship Council (CSC), and in part by the U.S. National Science Foundation under Grant CCF–1908308.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - In this paper, we consider a real-time E-Health Internet of Things (IoT) system with the uncertainty of channel state information (CSI), in which a wearable device collects radio frequency (RF) energy from a Personal Digital Assistant (PDA), and then transmits healthcare data status updates to the corresponding PDA promptly. The Peak Age of Information (PAoI) is considered as a parameter to measure the freshness of information. Our goal is to minimize the average PAoI under non-convex constraints related to an uncertain CSI mismatch model. Only mean and variance information is specified in the distributional ambiguity set. This distributionally robust optimization problem is transformed into a tractable semi-definite programming (SDP) problem using the Conditional Value-at-Risk (CVaR) based method. To solve this NP-hard problem effectively, we decompose the PAoI minimization problem into two subproblems, and propose a low complexity iterative algorithm to derive a suboptimal solution. Simulation results show an average PAoI-energy tradeoff in the considered healthcare IoT, and the CVaR based method can achieve a better performance than a non-robust method.
AB - In this paper, we consider a real-time E-Health Internet of Things (IoT) system with the uncertainty of channel state information (CSI), in which a wearable device collects radio frequency (RF) energy from a Personal Digital Assistant (PDA), and then transmits healthcare data status updates to the corresponding PDA promptly. The Peak Age of Information (PAoI) is considered as a parameter to measure the freshness of information. Our goal is to minimize the average PAoI under non-convex constraints related to an uncertain CSI mismatch model. Only mean and variance information is specified in the distributional ambiguity set. This distributionally robust optimization problem is transformed into a tractable semi-definite programming (SDP) problem using the Conditional Value-at-Risk (CVaR) based method. To solve this NP-hard problem effectively, we decompose the PAoI minimization problem into two subproblems, and propose a low complexity iterative algorithm to derive a suboptimal solution. Simulation results show an average PAoI-energy tradeoff in the considered healthcare IoT, and the CVaR based method can achieve a better performance than a non-robust method.
UR - http://www.scopus.com/inward/record.url?scp=85115706026&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115706026&partnerID=8YFLogxK
U2 - 10.1109/ICC42927.2021.9500955
DO - 10.1109/ICC42927.2021.9500955
M3 - Conference contribution
AN - SCOPUS:85115706026
T3 - IEEE International Conference on Communications
BT - ICC 2021 - IEEE International Conference on Communications, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Communications, ICC 2021
Y2 - 14 June 2021 through 23 June 2021
ER -