Quickest Change Detection in Anonymous Heterogeneous Sensor Networks

• Published in: IEEE transactions on signal processing Vol. 70; pp. 1041 - 1055
• Main Authors: Sun, Zhongchang, Zou, Shaofeng, Zhang, Ruizhi, Li, Qunwei
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Batteries; Change detection; Computational modeling; computationally efficient; False alarms; Hidden Markov models; Hypothesis testing; Likelihood ratio; mixture CuSum; Networks; optimal; Optimization; Sensors; sequential change detection; Signal processing algorithms; Sun; Testing; Trajectory
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2022.3148535

The problem of quickest change detection (QCD) in anonymous heterogeneous sensor networks is studied. There are <inline-formula><tex-math notation="LaTeX">n</tex-math></inline-formula> heterogeneous sensors and a fusion center. The sensors are clustered into <inline-formula><tex-math notation="LaTeX">K</tex-math></inline-formula> groups, and different groups follow different data-generating distributions. At some unknown time, an event occurs in the network and changes the data-generating distribution of the sensors. The goal is to detect the change as quickly as possible, subject to false alarm constraints. The anonymous setting is studied, where at each time step, the fusion center receives <inline-formula><tex-math notation="LaTeX">n</tex-math></inline-formula> unordered samples, and the fusion center does not know which sensor each sample comes from, and thus does not know its exact distribution. A simple optimality proof is first derived for the mixture likelihood ratio test, which was constructed and proved to be optimal for the non-sequential anonymous setting in (Chen et al. , 2019). For the QCD problem, a mixture CuSum algorithm is further constructed, and is further shown to be optimal under Lorden's criterion. For large networks, a computationally efficient test is proposed and a novel theoretical characterization of its false alarm rate is developed. Numerical results are provided to validate the theoretical results.