TY - GEN
T1 - AoI Minimization Charging at Wireless-Powered Network Edge
AU - Chen, Quan
AU - Guo, Song
AU - Xu, Wenchao
AU - Cai, Zhipeng
AU - Cheng, Lianglun
AU - Gao, Hong
N1 - Funding Information:
This work was supported by the Key-Area Research and Development Program of Guangdong Province (No. 2021B0101400003), the NSFC under Grant NOs. U20A6003, U1701262, the Hong Kong RGC Research Impact Fund (No. R5060-19), the General Research Fund (No. 152221/19E, 152203/20E, and 152244/21E), the National Natural Science Foundation of China (No. 61872310), the PSTP of Guangdong province under Grant NOs. 2022A1515011032, 2020A1515011132, the Shenzhen Science and Technology Innovation Commission (JCYJ20200109142008673), the Guangdong Provincial Key Laboratory of Cyber-Physical System under Grant 2020B1212060069 and supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (No. PolyU15222621).
Publisher Copyright:
© 2022 IEEE.
PY - 2022/7
Y1 - 2022/7
N2 - Age of Information (AoI) has emerged as a new metric to measure data freshness from the destination's perspective. The problem of optimizing AoI has been attracting extensive interests recently. However, existing works mainly focused on scheduling data transmission for AoI optimization. While at wireless-powered network edge, the charging plan of source nodes also requires to be computed in advance, which means the system AoI is determined by not only the data transmission decision but also the charging plan. Thus, in this paper, we investigate the first work to optimize the weighted peak AoI from the point of charging at wireless-powered network edge with a directional charger. Firstly, to minimize the weighted sum of average peak AoI, the AoI minimization problem is transformed to a charging time optimization problem with respect to the overlapped charging areas and average peak AoI, and an approximate algorithm is proposed to obtain the required charging time for each source node. Then, an age-based scheduling algorithm is proposed to compute the charging and data transmission decisions for each source node simultaneously, which can not only optimize the weighted sum of average peak AoI but also guarantee the maximum peak AoI for each source node. The proposed algorithm is proved to have an approximation ratio of up to (1+φ), where φ is a much smaller value related to the weight of each source node. Finally, the simulation results verify the high performance of proposed algorithms in terms of average and maximum peak AoI.
AB - Age of Information (AoI) has emerged as a new metric to measure data freshness from the destination's perspective. The problem of optimizing AoI has been attracting extensive interests recently. However, existing works mainly focused on scheduling data transmission for AoI optimization. While at wireless-powered network edge, the charging plan of source nodes also requires to be computed in advance, which means the system AoI is determined by not only the data transmission decision but also the charging plan. Thus, in this paper, we investigate the first work to optimize the weighted peak AoI from the point of charging at wireless-powered network edge with a directional charger. Firstly, to minimize the weighted sum of average peak AoI, the AoI minimization problem is transformed to a charging time optimization problem with respect to the overlapped charging areas and average peak AoI, and an approximate algorithm is proposed to obtain the required charging time for each source node. Then, an age-based scheduling algorithm is proposed to compute the charging and data transmission decisions for each source node simultaneously, which can not only optimize the weighted sum of average peak AoI but also guarantee the maximum peak AoI for each source node. The proposed algorithm is proved to have an approximation ratio of up to (1+φ), where φ is a much smaller value related to the weight of each source node. Finally, the simulation results verify the high performance of proposed algorithms in terms of average and maximum peak AoI.
UR - http://www.scopus.com/inward/record.url?scp=85140895665&partnerID=8YFLogxK
U2 - 10.1109/ICDCS54860.2022.00074
DO - 10.1109/ICDCS54860.2022.00074
M3 - Conference article published in proceeding or book
AN - SCOPUS:85140895665
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 713
EP - 723
BT - Proceedings - 2022 IEEE 42nd International Conference on Distributed Computing Systems, ICDCS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd IEEE International Conference on Distributed Computing Systems, ICDCS 2022
Y2 - 10 July 2022 through 13 July 2022
ER -