We consider the challenge of efficiently detecting changes within a network of sensors, where we also need to minimise communication between sensors and the cloud. We propose an online, communication-efficient method to detect such changes. The procedure works by performing likelihood ratio tests at each time point, and two thresholds are chosen to filter unimportant test statistics and make decisions based on the aggregated test statistics respectively. We provide asymptotic theory concerning consistency and the asymptotic distribution if there are no changes. Simulation results suggest that our method can achieve similar performance to the idealised setting, where we have no constraints on communication between sensors, but substantially reduce the transmission costs.

翻译：我们研究在传感器网络中高效检测变化的挑战，同时需要最小化传感器与云端之间的通信。我们提出一种在线、通信高效的方法来检测此类变化。该方法在每个时间点执行似然比检验，并设置两个阈值：一个用于过滤不重要的检验统计量，另一个用于基于聚合的检验统计量做出决策。我们提供了关于一致性的渐近理论，以及在无变化情况下检验统计量的渐近分布。仿真结果表明，我们的方法能够达到与理想设定（即传感器间通信无限制）相似的效果，同时显著降低传输成本。