Energy-Efficient Data Gathering in Wireless Sensor Networks with Asynchronous Sampling
A low sampling rate leads to reduced congestion and hence energy consumption in the resource-constrained wireless sensor networks. In this article, we propose asynchronous sampling that shifts the sampling time instances of sensor nodes from each other. For lossy data gathering scenarios, the proposed approach provides more information about the physical phenomena in terms of increased entropy at a low sampling rate. For lossless data gathering scenarios, on the other hand, the sampling rate is lowered without sacrificing critical knowledge required for signal reconstruction. As lower sampling rates lead to smaller energy consumption for processing and transmitting the collected sensory data, the proposed asynchronous sampling strategies are capable of achieving a better trade-off between the lifetime of the network and the quality of collected information. In addition to mathematical analysis, simulation results based on real data also verify the benefits of our asynchronous sampling.
data gathering wireless sensor networks asynchronous sampling algorithms models temporal-spatial correlation energy efficiency