Foundations and case studies on the scalable intelligence in AIoT domains

Abstract

Abstract The Internet-of-things (IoT) concept is based on networked, mobile, and sensor-equipped microelectronic devices. They are capable of reacting to their environment by collecting and processing data, computing, and communicating with other IoT devices and the cloud. 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 whether it is designed for human co-operation, completely autonomous operations, or something in between. The IoT devices, including smart phones and wearables, can be applied in a plethora of applications ranging from building automation and industrial systems to self-driving vehicles and health services. The distributed and growing usage of the connected devices deliver the users more responsive and intelligent support for decision-making in a given environment. The foundation of AI is based on data fed to algorithms for machine learning (ML). They require 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. This study focuses on finding the existing and/or recommended solutions for up-to-date AI close to the devices. First, definitions of devices are reviewed to find out classifications of their capacity to contribute for the computation and scalability. Second, other computing and serving options between devices and the cloud are studied. Those are referred to as Fog/Edge services, and they are more stationary than the IoT devices. Third, the facts learned are being applied in two use cases to support the discussion and applicability of AIoT in practice. The main conclusion is that currently there are no single solutions – neither hardware nor software – for solving all the identified requirements were found. Instead, there are multiple options from mutually connected devices via middle-layer support to cloud services and distributed learning, respectively.