Lagrange Index based Scheduling for Minimizing Age of Updates from Heterogeneous Sources
For network operators managing heterogeneous IoT or sensing systems, this work provides a practical scheduling solution that improves freshness of information under strict medium-access constraints.
This paper addresses the problem of minimizing the weighted average Age of Information (AoI) in a single-hop wireless uplink network with heterogeneous sensors generating updates of varying sizes. The proposed Lagrange index based scheduling policy achieves consistent performance gains over existing non-preemptive policies in numerical simulations.
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.