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

TL;DR

This paper develops a dynamic control algorithm to minimize power consumption in wireless sensor networks while maintaining a maximum age of information, using Lyapunov optimization and a low-complexity sub-optimal solution.

Contribution

It introduces a joint optimization framework for sampling, power allocation, and channel assignment under AoI constraints, with a novel Lyapunov-based dynamic control algorithm and a practical sub-optimal solution.

Findings

Achieves over 60% power savings compared to baseline policies.

Provides a low-complexity near-optimal solution for real-time implementation.

Demonstrates effectiveness through numerical simulations under various system parameters.

Abstract

We consider a system where multiple sensors communicate timely information about various random processes to a sink. The sensors share orthogonal sub-channels to transmit such information in the form of status update packets. A central controller can control the sampling actions of the sensors to trade-off between the transmit power consumption and information freshness which is quantified by the Age of Information (AoI). We jointly optimize the sampling action of each sensor, the transmit power allocation, and the sub-channel assignment to minimize the average total transmit power of all sensors subject to a maximum average AoI constraint for each sensor. To solve the problem, we develop a dynamic control algorithm using the Lyapunov drift-plus-penalty method and provide optimality analysis of the algorithm. According to the Lyapunov drift-plus-penalty method, to solve the main problem we need to solve an optimization problem in each time slot which is a mixed integer non-convex optimization problem. We propose a low-complexity sub-optimal solution for this per-slot optimization problem that provides near-optimal performance and we evaluate the computational complexity of the solution. Numerical results illustrate the performance of the proposed dynamic control algorithm and the performance of the sub-optimal solution for the per-slot optimization problems versus the different parameters of the system. The results show that the proposed dynamic control algorithm achieves more than $60~\%$ saving in the average total transmit power compared to a baseline policy.