It is hard to obtain a satisfactory reconstructive quality while compressing time-varying signals monitored by Wireless Sensor Network (WSN) using Compressive Sensing (CS), therefore a novel dynamic sampling method based on data prediction and sampling rate feedback control was proposed. Firstly, the sink node acquired the changing trend by analyzing the liner degree differences between current reconstructed data and last reconstructed data. Then the sink node calculated the suitable sampling rate according to the changing trend and fed back the result to sensors to dynamically adjust their sampling process. The experimental results show that the proposed dynamic sampling method can acquire higher reconstructed data accuracy than the CS data gathering method based on static sampling rate for WSN.

Single-variable model is generally used for Apache Web server. It has poor scalability when it is used to handle multiple priorities delay guarantee. A Multi-Input Multi-Output (MIMO) system model for Web server was proposed with decentralized self-tuning controllers based on decentralized control theory and Self-Tuning Control (STC) theory. These controllers could dynamically adjust the number of worker threads, which handled different priority requests to ensure that the higher priority requests were processed faster and the average delay ratio of different priority was maintained the same as settings. The parameters of model and controllers were updated according to the result of online identification. The results of simulation tests indicate that proportional delay guarantee can be maintained on the Web server in the closed-loop system even if the number of concurrent client connections change abruptly under overload conditions.