关键词: 传感器, 时空序列, AoT, 有损压缩, 多维张量, 张量分解, CP分解, 张量重构

Abstract:

Array of Things (AoT) provides continuous and dynamic observations of urban systems through multiple sensors at a single location. How to utilize the limited computing resources to compress and transmit AoT sequence data becomes one of the key bottlenecks of the AoT application. Considering that most AoT sequence data are massive, high-dimensional and needed to be processed at the sensor side, a tensor decomposition method is introduced to the lossy compression for AoT sequence data in this work. This method first organizes the AoT sequence data as a high-dimensional tensor to preserve the multidimensional coupling relationship among the different dimensions. The CANDECOMP/PARAFAC (CP) decomposition, which has simple parameter, relatively simple principle and low algorithm complexity, is then utilized to decompose and extract the principal feature components in each dimension of AoT sequence data. Since these principal feature components are obtained by absorbing the multidimensional coupling relationship, they can be further combined with tensor reconstruction to approximate the original data accurately. Considering that the data approximation is obtained by removing the redundant information, it can achieve the data lossy compression with the feature preservation. The simulation experiment is conducted based on the acousto-optic electromagnetic data sensed within 24 hours in the downtown area of Chicago in the United States. The influences of different compression parameters on compression ratio, compression error, compression accuracy, compression time, memory usage under the conditions of different compression parameter are discussed. The experimental results show that, with the increase of compression parameter, the compression error obviously decreases and the memory occupation weakly increases, which demonstrates that tensor-based method can achieve lossy compression of AoT sequence data and both the memory occupation during the running process and the memory occupation of the final results can support the data compression of sensor segments. Compared with the original intensity of the light field, the compressed data maintain the spatio-temporal distribution characteristics of original data that would not affect the further data analysis. In addition, compared with the traditional vector quantization coding compression method, the compression ratio of this method is higher about 27%~76%, the compression time is less about 46%~73%, and the memory occupation of compression result is smaller about 17%~57%. Therefore, the tensor-based method has a higher compression ratio, less compression time and smaller memory occupation under the same compression accuracy. The tensor-based method can also be applied to the data with multidimensional features, such as spatial dimensions with different locations, time dimensions at different time nodes, and attribute dimensions of different variables (temperature, humidity, etc.), which could provide a feasible idea for large-scale lossy compression of massive multidimensional geographic sensor sequence data represented by AoT sequence data.

Key words: sensor, spatio-temporal sequence, Array of Things (AoT), lossy compression, multidimensional tensor, tensor decomposition, CANDECOMP/PARAFAC decomposition, tensor reconstruction