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Foundations and Case Studies on the Scalable Intelligence in AIoT Domains

EasyChair Preprint no. 5326

12 pagesDate: April 15, 2021


The Internet-of-Things (IoT) concept is based on networked, mobile, and sensor equipped microelectronic devices. The deployment of artificial intelligence (AI) to IoT, referred to as artificial intelligence of things (AIoT), enables intelligent behavior for the whole cyber-physical system (CPS) whether it is designed for human co-operation, completely autonomous operations, or something in between.

The foundation of AI requires a lot of processing power due to the amount of data and recursive/concurrent nature of calculation. Until recently this has been accomplished mainly in the cloud environment, where the raw data is uploaded into. This exposes all the data, even private and sensitive data, to the transmission phase and processing system. In conjunction with IoT there is a possibility to perform ML closer to the origin of data concerning local intelligence. It means that only the results of local or edge ML are transmitted to cloud for more general aggregation of AI. Local systems do not need to send the raw data anymore, which helps on prevailing the privacy and security of the data. This type of ML is referred to as federated/collaborative learning (FL).

This study focuses on finding the existing and/or recommended solutions for up-to-date AI close to the devices. At first, the definitions of devices are reviewed in order to find out classifications of their capacity to contribute for the computation and scalability. Secondly, the other computing and serving options between the devices and the cloud are studied. Thirdly, the facts learned are being applied in two use cases in order to support the discussion and applicability of AIoT in practice.

The main conclusion is that there are no silver bullets for solving all the requirements. Instead there are multiple options from mutually connected devices via middle layer support to cloud services, and distributed learning, respectively.

Keyphrases: Artificial Intelligence, Artificial Intelligence of Things, Edge Computing, Federated Learning, Fog Computing, Internet of Things, machine learning, Scalability

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Aarne Klemetti and Erkki Räsänen},
  title = {Foundations and Case Studies on the Scalable Intelligence in AIoT Domains},
  howpublished = {EasyChair Preprint no. 5326},

  year = {EasyChair, 2021}}
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