Fair and Efficient Scheduling for Sensor Networks via Online Whittle Index Policy

Abstract

Wake-Up Radio (WUR) enables resource-constrained, battery-powered sensor nodes to remain in a low-power deep sleep state while continuously listening for a Wake-Up Signal (WUS). Sensor nodes only wake and transmit data after receiving the WUS, significantly reducing energy consumption. However, polling nodes whose transmitted data provides little or no meaningful update to the remote monitor can still result in unnecessary energy usage and increased storage overhead. To address this issue, this paper uses the Age of Incorrect Information (AoII) metric to prioritise the polling of nodes that provide informative updates to the remote monitor. Determining the optimal set of nodes to poll based on AoII can be formulated as a Restless Multi-Armed Bandit (RMAB) problem, which traditionally requires prior knowledge of the monitored process transition dynamics. Since such dynamics are often unknown in practical deployments, we propose an online learning framework based on state estimation to derive Whittle Index AoII (WAoII) and Fair Whittle Index AoII (FWAoII) policies without assuming known transition probabilities. The proposed policies efficiently schedule node polling while adapting to unknown process behaviour. Experimental evaluation using both real-world and synthetic datasets demonstrates that the proposed online WAoII policy can reduce packet transmissions by up to 70\% compared to the widely used Round Robin (RR) polling strategy, while maintaining Root Mean Squared Error (RMSE) values within acceptable application error tolerances. These results demonstrate the effectiveness of WAoII and FWAoII as energy-efficient polling techniques for low-power WUR sensor networks.