2014/03/12 by Ashutosh Nayyar, Nayyar, Ashutosh, Demosthenis Teneketzis +1
Computer Science · Decision Sciences · #Advanced Statistical Process Monitoring #Distributed Sensor Networks and Detection Algorithms #FOS: Electrical engineering #Systems and Control (eess.SY) #Target Tracking and Data Fusion in Sensor Networks #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1403.3126
openalex publication_date 2014/03/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Sequential detection problems in sensor networks are considered. The true state of nature/true hypothesis is modeled as a binary random variable H with known prior distribution. There are N sensors making noisy observations about the hypothesis; N =\1,2,…,N\ denotes the set of sensors. Sensor i can receive messages from a subset Pi ⊂ N of sensors and send a message to a subset Ci ⊂ N. Each sensor is faced with a stopping problem. At each time t, based on the observations it has taken so far and the messages it may have received, sensor i can decide to stop and communicate a binary decision to the sensors in Ci, or it can continue taking observations and receiving messages. After sensor i's binary decision has been sent, it becomes inactive. Sensors incur operational costs (cost of taking observations, communication costs etc.) while they are active. In addition, the system incurs a terminal cost that depends on the true hypothesis H, the sensors' binary decisions and their stopping times. The objective is to determine decision strategies for all sensors to minimize the total expected cost.