End-to-End Service Monitoring for Zero-Touch Networks

Autonomous management capability is the main pillar for paving Zero-touch Networks and efficiently deliver and operate use cases under the light of 5G requirements. To this end, Closed Control Loop (CCL), Intent-Based Networking (IBN), and Machine Learning (ML) are regarded as enablers to automatically executed all operational processes, ideally without human intervention. In this context, the ETSI Zero-touch network and Service Management (ZSM) framework specifies an end-to-end network and service management reference architecture for managing the full lifecycle of services. However, the whole process of service monitoring is not yet well-consolidated in ETSI ZSM. In this work, we propose the Monitoring Model Generator (MMG) component to automatically construct templates for service monitoring. MMG implements a novel methodology where service deployment models and standard information models are used as inputs to generate a high-level monitoring template, called Service Monitoring Model (SMM) and built upon an ontology-based schema based on the Resource Description Framework (RDF) vocabulary. We present a proof of concept implementation along with an experimental functional validation of the MMG and using RDF data in turtle syntax and format. The resulting monitoring models are then used to define actual monitoring KPIs and construct management policies in a control loop architecture.

Data Analysis Model Design of Health Service Monitoring System for China’s Elderly Population: The Proposal of the F-W Model Based on the Collaborative Governance Theory of Healthy Aging

In the era of artificial intelligence, big data and 5G, health care for elderly people is facing an important digital transformation. The objective of this study is to design the data analysis module of the elderly health service monitoring system (HSMS) and attempt to put forward a new healthy aging (HA) model that is applicable not only to the individual HA, but also to the regional HA system. Based on the HA theory of collaborative governance, we divided the elderly HSMS into four modules, including physical health, mental health, ability of daily activity, and social participation. Then, factors that influence HA were assessed by stepwise logistic regression to build the analysis model, using the public micro-panel data of the China Health and Retirement Longitudinal Survey (CHARLS). Age (odds ratio (OR) = 1.55 (95% confidence interval (CI): 1.06–2.27)), living in urban areas (OR = 1.57 (95% CI: 1.03–2.39)), being literate (OR = 1.51 (95% CI: 1.01–2.23)), expecting to get long-term health care in the future from their grown children (OR = 1.69 (95% CI: 1.10–2.61)) and having literate grown children (OR = 2.01 (95% CI: 0.26–0.97)) had a significant positive impact on HA of elderly people. Therefore, the F-W (factors and weighs, also family and welfare) model is proposed in this paper. The outcomes can contribute with designing HSMS for different provinces and several different regions in China and leave a door open to improve the model and algorithm application for HSMS in the future studies.