MEC-based architecture for interoperable and trustworthy internet of moving things

The expansion of the Internet of Moving Things(IoMT)leads to limitless and continuous working playgrounds exploited by highly dynamic end devices.This requires the adoption of multi-Radio Access Technologies(RATs)-based strategies to provide IoMT units with ubiquitous connectivity.To this end,the development of secure bootstrapping and authentication mechanisms is necessary to permit the secure operation of end devices.Given the transmission and power limitations of these elements,current cryptographic solutions do not address these stringent requirements.For that reason,in the study we present a Multi-Access Edge Computing(MEC)-based endto-end architecture that enables an efficient and secure authentication and key agreement between end devices and network servers over heterogeneous resource-limited networks such as the Low Power Wide Area Networks(LPWANs).Our proposal is based on the Authentication,Authorization,and Accounting(AAA)architecture and the recent Internet Engineering Task Force initiatives Static Context Header Compression and Low-Overhead CoAP-EAP.The results obtained from experimental tests reveal the validity of the proposal as it enables constrained IoMT devices to gain IPv6 connectivity as well as performs end-to-end secure authentication with notable reliability and controlled latency.

Machine learning-based zero-touch network and service management:a survey

The exponential growth of mobile applications and services during the last years has challenged the existing network infrastructures.Consequently,the arrival of multiple management solutions to cope with this explosion along the end-to-end network chain has increased the complexity in the coordinated orchestration of different segments composing the whole infrastructure.The Zero-touch Network and Service Management(ZSM)concept has recently emerged to automatically orchestrate and manage network resources while assuring the Quality of Experience(QoE)demanded by users.Machine Learning(ML)is one of the key enabling technologies that many ZSM frameworks are adopting to bring intelligent decision making to the network management system.This paper presents a comprehensive survey of the state-of-the-art application of ML-based techniques to improve ZSM performance.To this end,the main related standardization activities and the aligned international projects and research efforts are deeply examined.From this dissection,the skyrocketing growth of the ZSM paradigm can be observed.Concretely,different standardization bodies have already designed reference architectures to set the foundations of novel automatic network management functions and resource orchestration.Aligned with these advances,diverse ML techniques are being currently exploited to build further ZSM developments in different aspects,including multi-tenancy management,traffic monitoring,and architecture coordination,among others.However,different challenges,such as the complexity,scalability,and security of ML mechanisms,are also identified,and future research guidelines are provided to accomplish a firm development of the ZSM ecosystem.

TinyML-Based Fall Detection for Connected Personal Mobility Vehicles

A new wave of electric vehicles for personal mobility is currently crowding public spaces.They offer a sustainable and efficient way of getting around in urban environments,however,these devices bring additional safety issues,including serious accidents for riders.Thereby,taking advantage of a connected personal mobility vehicle,we present a novel on-device Machine Learning(ML)-based fall detection system that analyzes data captured from a range of sensors integrated on an on-board unit(OBU)prototype.Given the typical processing limitations of these elements,we exploit the potential of the TinyML paradigm,which enables embedding powerful ML algorithms in constrained units.We have generated and publicly released a large dataset,including real riding measurements and realistically simulated falling events,which has been employed to produce different TinyML models.The attained results show the good operation of the system to detect falls efficiently using embedded OBUs.The considered algorithms have been successfully tested on mass-market low-power units,implying reduced energy consumption,flash footprints and running times,enabling new possibilities for this kind of vehicles.

From Network Functions to NetApps: The 5GASP Methodology

As the 5G ecosystem continues its consolidation,the testing and validation of the innovations achieved by integrators and verticals service providers is of preponderant importance.In this line,5GASP is a European H2020-funded project that aims at easing the idea-to-market process through the creation of an European testbed that is fully automated and self-service,in order to foster rapid development and testing of new and innovative 5G Network Applications(NetApps).The main objective of this paper is to present the 5GASP’s unified methodology to design,develop and onboard NetApps within the scope of different vertical services,letting them use specific 5G facilities.Besides,we examine the whole 5GASP process in a tutorial fashion by adopting a specific use case focusing on the integration of a virtual On-Board Unit(vOBU)service that permits offloading processing from the attached vehicle and serving data-access requests.As demonstrated,the presented workflow permits the agile,rigorous,and safe development,testing and certification of NetApps,which will enable valuable in-network services for 5G and beyond infrastructures.

Federated Network Intelligence Orchestration for Scalable and Automated FL-Based Anomaly Detection in B5G Networks

The management of network intelligence in Beyond 5G(B5G)networks encompasses the complex challenges of scalability,dynamicity,interoperability,privacy,and security.These are essential steps towards achieving the realization of truly ubiquitous Artificial Intelligence(AI)-based analytics,empowering seamless integration across the entire Continuum(Edge,Fog,Core,Cloud).This paper introduces a Federated Network Intelligence Orchestration approach aimed at scalable and automated Federated Learning(FL)-based anomaly detection in B5Gnetworks.By leveraging a horizontal Federated learning approach based on the FedAvg aggregation algorithm,which employs a deep autoencoder model trained on non-anomalous traffic samples to recognize normal behavior,the systemorchestrates network intelligence to detect and prevent cyber-attacks.Integrated into a B5G Zero-touch Service Management(ZSM)aligned Security Framework,the proposal utilizes multi-domain and multi-tenant orchestration to automate and scale the deployment of FL-agents and AI-based anomaly detectors,enhancing reaction capabilities against cyber-attacks.The proposed FL architecture can be dynamically deployed across the B5G Continuum,utilizing a hierarchy of Network Intelligence orchestrators for real-time anomaly and security threat handling.Implementation includes FL enforcement operations for interoperability and extensibility,enabling dynamic deployment,configuration,and reconfiguration on demand.Performance validation of the proposed solution was conducted through dynamic orchestration,FL,and real-time anomaly detection processes using a practical test environment.Analysis of key performance metrics,leveraging the 5G-NIDD dataset,demonstrates the system’s capability for automatic and near real-time handling of anomalies and attacks,including real-time network monitoring and countermeasure implementation for mitigation.