Robust data collection for energy-harvesting wireless sensor networks

Ren Shiou Liu, Yen Chen Chen

研究成果: Article同行評審

11 引文 斯高帕斯(Scopus)


Energy-harvesting wireless sensor networks (EHWSN) have drawn much attention in recent years because the capability of collecting ambient energy enables the perpetual operations of sensor nodes. However, the instability of renewable energy sources has also imposed new challenges to data collection in EHWSNs. In order to achieve perpetual operation, many studies have proposed adjusting the sensors sampling rates or reconfiguring the underlying routing structure to counter the effects of these challenges. However, the performance of the former is constrained and sensitive to the routing structure used, while the latter requires global signaling, which can interrupt network operations. In this paper, we propose to address the dynamics of renewable energy with a two-stage approach. In the network planning stage, we make use of the primal cut method to solve a two-stage robust optimization (RO) problem and construct a data collection tree that works well under all worst-case scenarios. While in the operational stage of the network, we propose another algorithm that can lexicographically maximize the sampling rates of sensor nodes according to the observed recharging rates with minimal overheads. This avoids reconfiguring the routing structure during the operational phase of the network while simultaneously maximizes the performance of the network under the uncertainty of renewable energy. Numerical results are presented to show the effectiveness and robustness of the proposed method in dealing with the variability of renewable energy.

期刊Computer Networks
出版狀態Published - 2020 2月 11

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信


深入研究「Robust data collection for energy-harvesting wireless sensor networks」主題。共同形成了獨特的指紋。