Task-Aware Multi-Modal Sensing Control for IoT Systems

Future Internet of Things (IoT) systems increasingly rely on multiple sensing modalities to support sensing-intensive tasks such as localization and smart healthcare. However, always collecting all available sensing data can waste energy and communication resources, whereas aggressive sensing reduction may degrade task-level performance. The key challenge is therefore to adapt sensing operations according to the requirements of the target task while avoiding unnecessary sensing effort. To address this challenge, this article presents Task-Aware Multi-Modal Sensing Control (TA-MSC) for IoT systems. TA-MSC treats sensing operation as a task-level-output-driven closed-loop control problem that identifies a low-cost sensing configuration while satisfying a task-level performance requirement. By analyzing task-level outputs from the inference process, TA-MSC evaluates whether the current sensing configuration is sufficient and updates modality-wise sensing levels accordingly. The selected sensing configuration can be realized through practical control dimensions such as modality selection, sensor activation, channel adjustment, and duty-cycle management. We instantiate TA-MSC through a representative multi-modal localization case study, in which sensing energy is evaluated using literature-based sensing and transmission-energy models rather than direct hardware measurements. In the evaluated cross-device analysis, TA-MSC reduces the mean total modeled sensing energy by 52.8% relative to All-Max, while achieving a Top-1 localization accuracy of 96.14% compared with 97.37% for All-Max and a confidence-satisfaction rate of 98.94% compared with 97.68% for All-Max. These results indicate substantial modeled-energy savings with a modest Top-1 accuracy tradeoff, while the confidence-based task condition is satisfied more frequently.