Distributed Machine learning for Network Monitoring and predictive QoS in Automotive Applications
The shift towards automation in the automotive sector is giving rise to a plethora of cooperative distributed applications characterized by Quality-of-Service (QoS) constraints on the underlying 5G communication and computing infrastructure. In turn, this has fostered efforts to estimate QoS conditions and pro-actively adjust the network and/or service configuration, especially in cases of expected QoS degradation. For instance, the 5G-Automotive Association (5G-AA) has already proposed a framework for mobile networks to deliver In-advance QoS Notifications (IQN) to applications, in the so-called context of Predictive QoS.
The estimation of upcoming QoS conditions heavily builds on the use of historical data so as to gain insights of future system behavior. In the case of Artificial Intelligence and Machine Learning (AI/ML) this corresponds to the training of a ML model with historical data, so as to later use it for inference, based on the current conditions. Training is typically characterized by the need for large volumes of data (and corresponding 5G bandwidth), the correspondingly high computation load and the typically loose latency requirements. In the context of Predictive QoS, multiple data sources can be envisioned, including the 5G mobile network, having the vehicle itself as the natural focal point of past QoS experience and contextual information.
Applying the well-established centralized ML practices, where data are collected to a central location (e.g., a data lake) for training purposes, followed by the dispatching of the trained ML model, immediately reveals a particularly challenging environment: (i) training data can be of high volume consuming non-negligible network resources for collection, and/or (ii) subject to privacy concerns such as vehicle trajectory, while (iii) the dynamics of mobility call for a continuous learning process able to adapt to evolving and short-term conditions. The advent of Distributed Machine Learning (DML), including Federated Learning (FL) promises to address some of these challenges by realizing multi-node ML systems that bring the (un-)trained model to the data (and not the other way around) and hence (re-)train, collect and aggregate model instances in repetitive (a)synchronous steps (Figure 1).
Figure 1: The DML-FL Framework: 1) A Model Aggregator within the central cloud server selects a subset of clients and dispatches the current global model to them, 2) The clients perform local model training, 3) The clients upload the local models back to the central server, and 4) The central server aggregates the local models.
However, adopting DML/FL in the context of 5G-enabled services/applications presents significant practical challenges when it comes to the overall Management and Orchestration (MANO) processes. Realizing a DML/FL scheme requires the inclusion of vehicle On-Board Units (OBUs) and Road-Side Units (RSUs) within the broader operational scope of MANO processes, which comes with challenges related to the integration of the virtualization and programmability capabilities on the corresponding devices, as well as the integration within the MANO fabric in the presence of intermittent connectivity/availability. Then, the distributed character of the training process poses the requirement for advanced DML/FL MANO primitives, reducing the complexity of processes such as client selection, overlay topology formation and placement e.g., hierarchical FL, model aggregation and/or data transfer (where applicable), from the application-level implementation, down to the simple consumption of corresponding interfaces and/or the definition of corresponding policies.
Focusing on the case of AI/ML-enabled Predictive QoS, 5G-IANA identifies and addresses these challenges with the purpose of providing generic MANO primitives and NetApp VNF support for the realization of DML/FL services/applications. Our work will build on active/passive network monitoring data produced in NOKIA 5G testbed in Ulm, Germany, which consists of 5 sites-with 3 radio cells each. The monitoring data will be used to feed a DML/FL-enabled Predictive QoS service, with the purpose of eventually delivering IQNs for consumption by other services. The spatio-temporal dimensioning of the overall service will be carefully assessed, also feeding to the corresponding service Life-cycle Management (LCM) operations e.g., selection of ML model and/or model aggregation server corresponding to spatio-temporal QoS maps of the region of interest (Figure 2).
Figure 2: DML/FL-enabled Predictive QoS with geo-fencing: Single-model (instance) approach, adopting a global ML
model for all areas (top), versus multi-model (instance) approach, adopting multiple ML-model(s) instances per area (bottom).
Authors: Konstantinos Katsaros, Nehal Baganal-Krishna, Amr Rizk, Eirini Liotou, George Drainakis, Markus Wimmer, Steffen Schulz.
 5GAA Automotive Association, “Making 5G Proactive and Predictive for the Automotive Industry,” White Paper, Dec 2019.
 H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. Y. Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Proc. 20th Int. Conf. Artif. Intell. Statist., 2017, pp. 1273–1282
Distributed Machine learning for Network Monitoring and predictive QoS in Automotive ApplicationsComunicación2022-01-04T09:24:02+00:00
5G-IANA: A new Horizon 2020 project on 5G Intelligent Automotive Network Applications
ICCS, our project coordinator, introduces the project and provides a general introduction to its technical objectives, methodology and main expected outcomes
5G-based Automotive-related services (i.e., Connected and Automated Mobility services) are a broad range of digital services in and around vehicles including both safety-related and other commercial services provided, enabled, or supported by 5G networks. The rollout of 5G is expected to become a “game changer”. The prospect that 5G will be a unified multi-service platform, serving not only the traditional mobile broadband market but also enabling digital transformation in a number of vertical industries, is expected to result in the creation of unprecedented opportunities for innovation and economic growth.
In view of this opportunity, 5G-IANA aims at providing an open 5G experimentation platform, on top of which third party experimenters, i.e., Small and Medium Enterprises (SMEs) in the Automotive-related 5G-PPP vertical will have the opportunity to develop, deploy and test their services. An Automotive Open Experimental Platform (AOEP) will be specified, as the whole set of hardware and software resources that provides the computational and communication/transport infrastructure as well as the management and orchestration components, coupled with an enhanced NetApp Toolkit tailored to the Automotive sector. 5G-IANA will expose to experimenters secured and standardized Application Programming Interfaces (APIs) for facilitating all the different steps towards the production stage of a new service.
5G-IANA will target different virtualization technologies integrating different Management and Orchestration (MANO) frameworks for enabling the deployment of the end-to-end network services across different domains (vehicles, road infrastructure, Multi-access Edge Computing (MEC) nodes and cloud resources). 5G-IANA NetApp toolkit will be linked with a new Automotive Virtual Network Functions (VNFs) Repository including an extended list of ready to use open accessible Automotive-related VNFs and NetApp templates, that will form a repository for SMEs to use and develop new applications.
Furthermore, 5G-IANA will develop a Distributed Artificial Intelligence / Machine Learning (AI/ML) (DML) framework, that will provide functionalities for simplified management and orchestration of collections of AI/ML service components and will allow ML-based applications to penetrate the Automotive world, due to its inherent privacy preserving nature.
5G-IANA will be demonstrated through seven Automotive-related use cases in two 5G Stand Alone (SA) testbeds, which are:
Manoeuvres coordination for autonomous driving (vehicle-side and road-side manoeuvre coordination)
Augmented reality (AR) content delivery for vehicular networks
Situational awareness in cross border road tunnel accidents.
Moving beyond technological challenges, and exploiting input from the demonstration activities, 5G-IANA will perform a multi-stakeholder cost-benefit analysis that will identify and validate market conditions for innovative, yet sustainable business models supporting a long-term roadmap towards the pan-European deployment of 5G as key advanced Automotive services enabler.
INSTITUTE OF COMMUNICATION AND COMPUTER SYSTEMS (ICCS)
5G-IANA: A new Horizon 2020 project on 5G Intelligent Automotive Network ApplicationsComunicación2021-12-13T13:09:48+00:00
The EU project 5G-IANA kicks off to accelerate the creation and commercialisation of 5G-based Automotive Applications
The project gathers 16 partners from 8 European countries
An Open 5G Intelligent Experimentation Platform will be developed and available for companies in the sector
The disruptive approach of the project intends to exploit obtained results through 7 different use cases
5G-IANA is an EU-funded project focused on providing agents of the automotive and mobility sectors with an open 5G intelligent experimentation platform. This platform will enable companies (especially SMEs) to develop, implement and test their automotive services as well as to accelerate their development prior to the commercialization phase.
The AOEP (Automotive Open Experimental Platform) platform, which lies in the core of 5G-IANA, will consist of a complete set of hardware and software resources that will make up an advanced communications IT infrastructure applied to transport, taking advantage of 5G intelligent networks’ potential. It will be coupled with an enhanced NetApp Toolkit tailored to the mobility sector, available to all companies and agents of the service value chain. 5G-IANA will put at the disposal of these users secured and standardized APIs for accelerating the production stage of new services.
Within the framework of this project, different virtualization technologies will be investigated and developed for enabling the deployment of the end-to-end network services across different domains (vehicles, road infrastructure, MEC nodes and cloud resources).
‘5G-IANA aims at boosting 5G uptake on key segments of the automotive industry, where 5G/B5G business practical applications carry tremendous potential. The project is designed to bring significant changes in the automotive sector, impacting society at large, by delivering 5G solutions that are set to tackle challenges associated with road safety and energy efficiency, while also creating new business opportunities for SMEs and Start-Ups.’ mentions project coordinator Dr. Angelos Amditis from ICCS/I-Sense Group.
5G-IANA will be demonstrated through seven automotive-related use cases in two 5G testbeds: one operated by NOKIA in Ulm, Germany, and one operated by Telekom Slovenia in Ljubljana, Slovenia. Validation scenarios will be the following: remote driving; manoeuvres coordination for autonomous driving; virtual bus tour; Augmented Reality (AR) content delivery for vehicular networks; parking circulation and high-risk driving hotspot detection; network status monitoring; and situational awareness in cross border road tunnel accidents.
The disruptive approach of the project intends to go beyond technological development and exploit obtained results from these demonstration activities. 5G-IANA aims to increase the uptake of 5G starting from the key Automotive industrial segment. Also, significant benefits are foreseen by 5G-IANA on the areas of safety, environment, and economy. By providing real-time notifications about emergency cases on the road and by sharing kinematic information when overtaking, 5G-IANA will provide increased safety. Moreover, 5G-IANA will improve traffic flow by providing real-time traffic data to the drivers. Finally, 5G-IANA will also lead to emissions’ reduction by shortening the time-to-destination (and time for parking) for each driver.
As regards commercialisation of services, 5G-IANA will perform a multi-stakeholder cost-benefit analysis that will identify and validate market conditions for innovative commercial models focusing on (their) sustainability. These models will support a long-term roadmap towards the generalisation of 5G-based innovative services. This project is part of the strategy for the pan-European deployment of 5G as a key advanced Automotive services’ enabler.
The EU project 5G-IANA kicks off to accelerate the creation and commercialisation of 5G-based Automotive ApplicationsComunicación2021-12-01T11:14:50+00:00
5G-IANA project featured on the 5GPPP Phase 3 projects brochure
5G-IANA project was featured on the 5G PPP Phase 3 projects’ brochure. As the last set of 5G PPP phase 3 projects has started, the new 5G Infrastructure Public Private Partnership (5G PPP) projects’ brochure was published in June.
5G-IANA project was featured on this brochure. Full publication is available here
5G-IANA project featured on the 5G PPP Phase 3 projects brochureComunicación2021-11-09T10:15:46+00:00
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