Convergence of AI and MEC for Autonomous IoT Service Provisioning and Assurance in B5G

Abstract

With the exponential growth of Internet of Things (IoT) devices, IoT has become a transformative technology with applications spanning various domains. It encompasses a wide range of public and industrial vertical services that come with diverse and stringent Quality of Service (QoS) requirements. Traditional networks often struggle to meet the demands of these diverse IoT services. As a result, the introduction of 5G and Beyond 5G (B5G) networks holds promise in accommodating these diverse IoT services through network slicing technology. Network slicing involves partitioning a single physical network infrastructure into multiple logically isolated networks and ensures dedicated resources to each service as per QoS requirements. Additionally, Multi-Access Edge Computing (MEC) in B5G networks presents an innovative solution to facilitate low-latency communication for IoT services. However, the automatic provisioning and management of end-to-end (e2e) network slicing for IoT services across multi-domain infrastructures pose significant challenges, including manual error-prone resource configuration, network slice template preparation, and human intervention. This paper proposes an automated Artificial Intelligence (AI) and MEC-enabled solution for provisioning and managing network slice resources across multiple domains specifically tailored for IoT services. Our solution provides an abstraction layer that generates slice templates for each domain and automates the deployment of resources based on the specified QoS requirements. It automates the slice resource configuration process, reduces human intervention, and manages the complete lifecycle of IoT slices. We have conducted several tests with our system, creating multiple IoT slices, and have observed stable performance in slice design, resource provisioning, slice isolation, and management.