Optimizing Information Freshness in Computing enabled IoT Networks

TL;DR

This paper develops an analytical framework to optimize information freshness in computing-enabled IoT networks by modeling computing and transmission as a tandem queue, deriving PAoI expressions, and proposing an algorithm for optimal update frequency.

Contribution

It introduces a novel analytical model for PAoI in computing-enabled IoT systems and proposes a derivative-free optimization algorithm for minimizing maximum PAoI.

Findings

Analytical expressions for average PAoI across sensors.

A min-max optimization framework for update frequency.

Validation of analysis and algorithm effectiveness through simulations.

Abstract

Internet of Things (IoT) has emerged as one of the key features of the next generation wireless networks, where timely delivery of status update packets is essential for many real-time IoT applications. To provide users with context-aware services and lighten the transmission burden, the raw data usually needs to be preprocessed before being transmitted to the destination. However, the effect of computing on the overall information freshness is not well understood. In this work, we first develop an analytical framework to investigate the information freshness, in terms of peak age of information (PAoI), of a computing enabled IoT system with multiple sensors. Specifically, we model the procedure of computing and transmission as a tandem queue, and derive the analytical expressions of the average PAoI for different sensors. Based on the theoretical results, we formulate a min-max optimization problem to minimize the maximum average PAoI of different sensors. We further design a derivative-free algorithm to find the optimal updating frequency, with which the complexity for checking the convexity of the formulated problem or obtaining the derivatives of the object function can be largely reduced. The accuracy of our analysis and effectiveness of the proposed algorithm are verified with extensive simulation results.