Science & Engineering Research Support soCiety (SERSC)
Wireless Sensor Networks (WSNs) have characteristics of large size, limited resources, large amount of transmission data and so on. In order to reduce the redundancy of sensed data and decrease network data traffic. The authors applied CS (Compressed Sensing) to clustered structure proposed Low-Latency Compressed Sensing model (LLCS) which is based on the spatial-temporal correlation of sensed data, the model is also capable of processing sparse abnormal events which is a crucial feature in WSNs. They analyzed the relationship between compression ratio and sampling rounds and verified the abnormal event processing method.