  Artificial intelligence (AI) technologies, and particularly deep learning
systems, are traditionally the domain of large-scale cloud servers, which have
access to high computational and energy resources. Nonetheless, in
Internet-of-Things (IoT) networks, the interface with the real-world is carried
out using edge devices that are limited in hardware and can communicate. The
conventional approach to provide AI processing to data collected by edge
devices involves sending samples to the cloud, at the cost of latency,
communication, connectivity, and privacy concerns. Consequently, recent years
have witnessed a growing interest in enabling AI-aided inference on edge
devices by leveraging their communication capabilities to establish
collaborative inference. This article reviews candidate strategies for
facilitating the transition of AI to IoT devices via collaboration. We identify
the need to operate in different mobility and connectivity constraints as a
motivating factor to consider multiple schemes, which can be roughly divided
into methods where inference is done remotely, i.e., on the cloud, and those
that infer on the edge. We identify the key characteristics of each strategy in
terms of inference accuracy, communication latency, privacy, and connectivity
requirements, providing a systematic comparison between existing approaches. We
conclude by presenting future research challenges and opportunities arising
from the concept of collaborative inference.
