Lagrange Index based Scheduling for Minimizing Age of Updates from Heterogeneous Sources

TL;DR

This paper introduces a Lagrange index heuristic for scheduling in heterogeneous sensing systems to minimize the age of updates, considering strict medium constraints and non-preemptive transmissions.

Contribution

It formulates the scheduling problem as a restless multi-armed bandit with SMDP dynamics and develops a novel index-based heuristic for efficient, near-optimal decision making.

Findings

The heuristic achieves consistent performance improvements over existing policies.

Numerical results validate the effectiveness of the proposed scheduling approach.

The method efficiently computes indices leveraging structural properties of the heuristic.

Abstract

Modern sensing systems generate heterogeneous updates ranging from small status packets to large data objects. We study a single-hop wireless uplink network where sensors generate updates at will, each consisting of a sensor dependent number of packets. Under a strict medium-access constraint and non-preemptive (no-switching) transmissions, decision stages become action-dependent and stochastic. We formulate the problem as a restless multi-armed bandit (RMAB) with semi-Markov decision process (SMDP) dynamics and develop a Lagrange index based heuristic for minimizing weighted average AoI cost. For the weighted AoI setting, we utilize the structural properties of the heuristic to enable efficient index computation. Numerical results demonstrate consistent performance gains over existing non-preemptive scheduling policies, providing a practical solution for heterogeneous freshness-aware systems.