  Centralized clouds processing the large amount of data generated by
Internet-of-Things (IoT) can lead to unacceptable latencies for the end user.
Against this backdrop, Edge Computing (EC) is an emerging paradigm that can
address the shortcomings of traditional centralized Cloud Computing (CC). Its
use is associated with improved performance, productivity, and security. Some
of its use cases include smart grids, healthcare Augmented Reality (AR)/Virtual
Reality (VR). EC uses servers strategically placed near end users, reducing
latency and proving to be particularly well-suited for time-sensitive IoT
applications. It is expected to play a pivotal role in 6G and Industry 5.0.
Within the IoT-edge environment, artificial intelligence (AI) plays an
important role in automating decision and control, including but not limited to
resource allocation activities, drawing inferences from large volumes of data,
and enabling powerful security mechanisms. The use cases in the IoT-Edge-cloud
environment tend to be complex resulting in large AI models, big datasets, and
complex computations. This has led to researchers proposing techniques that
partition data, tasks, models, or hybrid to achieve speed, efficiency, and
accuracy of processing. This survey comprehensively explores the IoT-Edge-AI
environment, application cases, and the partitioning techniques used. We
categorize partitioning techniques and compare their performance. The survey
concludes by identifying open research challenges in this domain.
