Abstract Foundation models have gained growing interest in the IoT domain due to their reduced reliance on labeled data and strong generalizability across tasks, which address key limitations of traditional machine learning approaches. However, most existing foundation model based methods are developed for specific IoT tasks, making it difficult to compare approaches across IoT domains and limiting guidance for applying them to new tasks. This survey aims to bridge this gap by providing a comprehensive overview of current methodologies and organizing them around four shared performance objectives by different domains: efficiency , context-awareness , safety , and security & privacy . For each objective, we review representative works, summarize commonly-used techniques and evaluation metrics. This objective-centric organization enables meaningful cross-domain comparisons and offers practical insights for selecting and designing foundation model based solutions for new IoT tasks. We conclude with key directions for future research to guide both practitioners and researchers in advancing the use of foundation models in IoT applications.

This paper presents OMSense, an intelligent surface solution that leverages origami-inspired metasurfaces to allow scalable and precise surface pressure sensing. People interact with various surfaces daily, and these interactions cause the surfaces to deform, a process that can be captured by sensors. This interaction can be utilized in various forms of human-computer interaction and human monitoring, enabling new use cases. However, existing surface sensing schemes are either expensive, difficult to scale, or low-precision due to signal leakage in multiplex design. To solve this problem, we propose OMSense, which adopts the multiplex matrix sensing design and incorporates a 3D metastructure to reduce the shared physical connection-induced signal leakage. In addition to this physical augmentation, OMSense adopts a circuit-guided CNN to mitigate the circuit connection-induced signal leakage (ghosting). We 3D print a circuit-integrated metastructure and evaluate the sensor unit accuracy. OMSense achieves up to 2× sensor unit activation detection F1 score compared to the baselines.

The rising older adults population has led to an increased demand for in-home health monitoring to support their well-being in daily life. For instance, localization and tracking are essential applications in elderly monitoring since they can provide various information on health, mobility and can detect falls. The required power and computational resources of traditional acoustic sensor-array solutions make them unavailable on power- and computation- constrained embedded devices. In this paper, we present LEVO, a single-point directional acoustic sensing system that leverages simple LEGO® bricks to build up a physical structure that can embed directional information into a signal waveform. Our preliminary results verifies the feasibility of adopting LEVO for signal direction recognition from signal-point sensing data.

Approximately 3.5 billion people worldwide have oral diseases [8], which significantly impact people's quality of life [1] and may lead to mortality if left unattended [7]. Out of these oral diseases, occlusal diseases, such as temporomandibular joint and muscle dis-order (TMD), gum recession, fractured teeth, and undesired tooth mobility, are especially hard to diagnose due to the subjective inter-pretation of many current practices used to test for this condition [9]. Occlusal diseases, associated with the alignment of a person's teeth, are usually caused by excessive wearing of teeth, bruxism, and unbalanced biting [5].