Abstract
Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.
Highlights
Today, an ever growing market of Internet of Things (IoT) applications, such as video surveillance, intelligent personal assistants, smart home appliances, and smart manufacturing, requires advanced Artificial Intelligence (AI) capabilities, including computer vision, speech recognition, and natural language processing
We provide the semantic description of the AI-empowered IoT devices through the well-known Open Mobile Alliance (OMA) Lightweight Machine-to-Machine (LwM2M) resource description model [17] proposed in the IoT domain
We have presented a novel solution to enable the vision of AI deployed at the deep edge in order to support intelligent IoT applications
Summary
An ever growing market of Internet of Things (IoT) applications, such as video surveillance, intelligent personal assistants, smart home appliances, and smart manufacturing, requires advanced Artificial Intelligence (AI) capabilities, including computer vision, speech recognition, and natural language processing. Such intelligent applications are traditionally implemented using a centralized approach: raw data collected by IoT devices are streamed to the remote cloud, which has virtually unlimited capabilities to run compute-intensive tasks, such as AI model building/training and inference. Transferring sensitive data retrieved by IoT devices may raise privacy issues [2]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
