Spatial-temporal Basis of Sensory Data for 5G Internet of Things Networks

5G Internet of Things (IoT) networks have wideband radio frequency spectrum utility and are therefore of primary importance for sensory data transmission. However, in the 5G era, the big date seriously affects the network’s performance, namely, the high energy- consumption is the focus issue. Compressed sensing (CS) is utilized to promote the performance. In general, sensory data possess spatial and temporal correlation features, so that they are capable of capturing to design sparse basis to relieve transportation cost. In the paper, the spatial-temporal basis is designed and the optimal orthogonal basis selection algorithm based on energy is constructed. Different numerical sparse methods are introduced to appraise sparse performance of the algorithm. Besides, by means of the extensive experiments on real sensory data, we demonstrate that the proposed technique is able to sparest representing original sensory data.