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Power Minimization for Age of Information Constrained Dynamic Control in\n Wireless Sensor Networks

2020/07/09 by Mohammad Moltafet, Markus Leinonen, Moltafet, Mohammad +5 · 1 citation
Computer Science · Engineering · Medicine · #Age of Information Optimization #Congenital Heart Disease Studies #FOS: Computer and information sciences #Information Theory (cs.IT) #IoT Networks and Protocols

paper · pdf · doi:10.48550/arxiv.2007.05364

openalex publication_date 2020/07/09 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28

Abstract

We consider a system where multiple sensors communicate timely information\nabout various random processes to a sink. The sensors share orthogonal\nsub-channels to transmit such information in the form of status update packets.\nA central controller can control the sampling actions of the sensors to\ntrade-off between the transmit power consumption and information freshness\nwhich is quantified by the Age of Information (AoI). We jointly optimize the\nsampling action of each sensor, the transmit power allocation, and the\nsub-channel assignment to minimize the average total transmit power of all\nsensors subject to a maximum average AoI constraint for each sensor. To solve\nthe problem, we develop a dynamic control algorithm using the Lyapunov\ndrift-plus-penalty method and provide optimality analysis of the algorithm.\nAccording to the Lyapunov drift-plus-penalty method, to solve the main problem\nwe need to solve an optimization problem in each time slot which is a mixed\ninteger non-convex optimization problem. We propose a low-complexity\nsub-optimal solution for this per-slot optimization problem that provides\nnear-optimal performance and we evaluate the computational complexity of the\nsolution. Numerical results illustrate the performance of the proposed dynamic\ncontrol algorithm and the performance of the sub-optimal solution for the\nper-slot optimization problems versus the different parameters of the system.\nThe results show that the proposed dynamic control algorithm achieves more than\n60~ % saving in the average total transmit power compared to a baseline\npolicy.\n

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