  The next generation of machine learning systems must be adept at perceiving
and interacting with the physical world through a diverse array of sensory
channels. Commonly referred to as the `Internet of Things (IoT)' ecosystem,
sensory data from motion, thermal, geolocation, depth, wireless signals, video,
and audio are increasingly used to model the states of physical environments
and the humans inside them. Despite the potential for understanding human
wellbeing, controlling physical devices, and interconnecting smart cities, the
community has seen limited benchmarks for building machine learning systems for
IoT. Existing efforts are often specialized to a single sensory modality or
prediction task, which makes it difficult to study and train large-scale models
across many IoT sensors and tasks. To accelerate the development of new machine
learning technologies for IoT, this paper proposes MultiIoT, the most expansive
and unified IoT benchmark to date, encompassing over 1.15 million samples from
12 modalities and 8 real-world tasks. MultiIoT introduces unique challenges
involving (1) generalizable learning from many sensory modalities, (2)
multimodal interactions across long temporal ranges, (3) extreme heterogeneity
due to unique structure and noise topologies in real-world sensors, and (4)
complexity during training and inference. We evaluate a comprehensive set of
models on MultiIoT, including modality and task-specific methods, multisensory
and multitask supervised models, and large multisensory foundation models. Our
results highlight opportunities for ML to make a significant impact in IoT, but
many challenges in scalable learning from heterogeneous, long-range, and
imperfect sensory modalities still persist. We release all code and data to
accelerate future research in machine learning for IoT.
