Random node sampling approach for energy efficient data gathering in wireless sensor networks

This article proposes a novel data gathering scheme for wireless sensor networks to reduce energy consumption for a prolonged network lifetime. Data gathering is modeled as random node sampling method, where fusion center collects data from arbitrarily deployed randomly selected nodes and takes the global decision. We evaluate performance of our method with metrics: energy consumption and error performance of the global decision. The results of extensive simulations on synthetic and real data sets show that at an average our proposed method can save up to 57.38% energy with 50.23% error performance gain compared to auction-based data gathering method.