Compressive sensing of wind speed data of large-scale spatial structures with dedicated dictionary using time-shift strategy

Abstract

The real-time wind monitoring is widely used to evaluate the wind effect on the large-scale spatial structures. Wireless sensor network (WSN) is usually the first choice for the large-scale spatial structures to collect wind monitoring data because of its super-large size. Compressive sensing (CS) has great potential in solving the energy problem of WSN and reduces the difficulty in transmission of massive data based on sparsity. However, wind signals are often not naturally sparse on the traditional bases (e.g., Fourier basis). This paper proposes a new method of constructing a dedicated dictionary for wind speed signals using the time-shift strategy. With this proposed dictionary, the signals can be compressed by random sampling and recovered by ℓ1-norm sparse regularization. The performance of the improved CS methodology is evaluated using two large-scale spatial structures. The results show that the proposed CS methodology has better performance than the traditional CS algorithm with the Fourier basis and the linear interpolation method. Furthermore, the influences of the relevant critical parameters (regularization parameter, lag, sliding window size, and compression ratio) of the improved CS methodology are comprehensively explored.