2021/08/17 by Chen–Feng Liu, Mehdi Bennis, Liu, Chen-Feng +1
Computer Science · Engineering · #Age of Information Optimization #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #IoT Networks and Protocols #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI)
paper · pdf · doi:10.48550/arxiv.2108.07504
openalex publication_date 2021/08/17 · openalex created_date 2021/08/30 · openalex updated_date 2026/07/28
While information delivery in industrial Internet of things demands reliability and latency guarantees, the freshness of the controller's available information, measured by the age of information (AoI), is paramount for high-performing industrial automation. The problem in this work is cast as a sensor's transmit power minimization subject to the peak-AoI requirement and a probabilistic constraint on queuing latency. We further characterize the tail behavior of the latency by a generalized Pareto distribution (GPD) for solving the power allocation problem through Lyapunov optimization. As each sensor utilizes its own data to locally train the GPD model, we incorporate federated learning and propose a local-model selection approach which accounts for correlation among the sensor's training data. Numerical results show the tradeoff between the transmit power, peak AoI, and delay's tail distribution. Furthermore, we verify the superiority of the proposed correlation-aware approach for selecting the local models in federated learning over an existing baseline.