Research Of Dynamic Sampling Strategy For Wireless Sensor Networks Based On Temporal Correlation

The development of wireless sensor network technology makes large scale and long time signal measurement possible. Wireless sensor nodes are usually powered by batteries because of the special nature of their application environment. Energy efficiency is a key indicator of wireless sensor networks.The energy consumption of wireless sensor nodes focused on CPU, sensors and wireless transceivers. In general, energy consumption of the data transfer accounts for 80% of the entire sensor network. On the other hand, sensor sampling data transmission in wireless sensor networks containing a large amount of redundant information due to temporal correlation. Reduce transmission of redundant information can significantly improve the energy efficiency of wireless sensor networks.For narrow changing signal measurement, the dynamic sampling control strategy, DSI was proposed for wireless sensor nodes. By analyzing current and historical sampling data to dynamically determine the sampling interval of sensor nodes under negative feedback regulation. A lower sampling frequency and a lower data uploaded frequency are taken to meet the measurement requirements, thereby reducing energy consumption to improve the life of the sensor nodes. Simulation results demonstrate that the DSI strategy is obviously effective in comparison to the periodic strategy and the SOD strategy in saving energy consumption.On the basis of analysis of wireless sensor networks temporal correlation model, a new sampling strategy was proposed for wireless sensor networks based on sensor grid, which can effectively solve the problem of sampling delays when using the DSI strategy. The monitoring area is divided into a plurality of sensor grid, only the cluster head node within the grid remain active, when there is a sense of abnormal data and then activate other nodes in the current grid to obtain more detailed information. By reducing the same or similar sample data between the wireless sensor nodes uploaded, the strategy can reduce the transmission of redundant information. Simulation results demonstrate that this strategy significantly improves the energy efficiency of wireless sensor networks.