Peak AoI Minimization at Wireless-Powered Network Edge: From the Perspective of Both Charging and Transmitting

Age of Information, which emerged as a new metric to quantify the freshness of information, has attracted increasing interests recently. To optimize the system AoI, most existing works try to compute an efficient schedule from the point of data transmission. Unfortunately, at wireless-powered network edge, the charging schedule of the source nodes also needs to be decided besides data transmission. Thus, in this paper, we investigate the joint scheduling problem of data transmission and energy replenishment to optimize the maximum peak AoI at network edge with directional chargers. To the best of our knowledge, this is the first work that considers such two problems simultaneously. Firstly, the theoretical bounds of the maximum peak AoI with respect to the charging latency are derived. Secondly, for the minimum peak AoI scheduling problem with a single charger, an optimal scheduling algorithm is proposed to minimize the charging latency, and then a data transmission scheduling strategy is also given to optimize the maximum peak AoI. The proposed algorithm is proved to have a constant approximation ratio of up to 1.5. As for the scenario with multiple chargers, an approximate algorithm is also proposed to minimize the charging latency and the maximum peak AoI. Additionally, when the network bandwidth constraint is considered, the algorithm which considers the parallelism of the charging process and data transmission process is also proposed to reduce the latency and the maximum peak AoI. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency and AoI.

Automated summary

This paper investigates the joint scheduling problem of data transmission and energy replenishment to optimize the maximum peak Age of Information (AoI) at the network edge with directional chargers. The theoretical bounds of the maximum peak AoI with respect to the charging latency are derived. Optimal and approximate scheduling algorithms are proposed to minimize the charging latency and the maximum peak AoI. The proposed algorithms have been shown to achieve high performance in terms of latency and AoI through theoretical analysis and simulation results.