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TinyML-Based Fall Detection for Connected Personal Mobility Vehicles

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Computers, Materials & Continua 2022年第5期71卷 3869-3885页

作者： ramon Sanchez-Iborra Luis Bernal-Escobedo Jose Santa Antonio Skarmeta University Center of Defense General Air Force AcademySan Javier30720Spain University of Murcia Murcia30100Spain Technical University of Cartagena Cartagena30202Spain

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.

关键词： Personal mobility machine learning on-board unit C-ITS IoT

Machine Learning-Based Radio Access Technology Selection in the Internet of Moving Things

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China Communications 2021年第7期18卷 13-24页

作者： ramon Sanchez-Iborra Luis Bernal-Escobedo Jose Santa Dept.Engineering and Applied Techniques University Centre of Defence at the Spanish Air Force Academy30729 San JavierSpain Dept.Information and Communication Engineering University of Murcia30100 MurciaSpain Dept.of Electronics Computing Technology and ProjectsTechnical University of Cartagena30202 CartagenaSpain

The Internet of Moving Things(IoMT)takes a step further with respect to traditional static IoT deployments.In this line,the integration of new eco-friendly mobility devices such as scooters or bicycles within the Cooperative-Intelligent Transportation Systems(C-ITS)and smart city ecosystems is crucial to provide novel services.To this end,a range of communication technologies is available,such as cellular,vehicular WiFi or Low-Power Wide-Area Network(LPWAN);however,none of them can fully cover energy consumption and Quality of Service(QoS)requirements.Thus,we propose a Decision Support System(DSS),based on supervised Machine Learning(ML)classification,for selecting the most adequate transmission interface to send a certain message in a multi-Radio Access Technology(RAT)set up.Different ML algorithms have been explored taking into account computing and energy constraints of IoMT enddevices and traffic type.Besides,a real implementation of a decision tree-based DSS for micro-controller units is presented and evaluated.The attained results demonstrate the validity of the proposal,saving energy in communication tasks as well as satisfying QoS requirements of certain urgent messages.The footprint of the real implementation on an Arduino Uno is 444 bytes and it can be executed in around 50µs.

关键词： internet of moving things multi-RAT CITS classification personal mobility

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

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Digital Communications and Networks 2022年第2期8卷 105-123页

作者： Jorge Gallego-Madrid ramon Sanchez-Iborra Pedro M.Ruiz Antonio F.Skarmeta Department of Information and Communication Engineering University of Murcia30100MurciaSpain Department of Engineering and Applied Techniques University Center of Defense30729San Javier Air Force BaseSpain

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.

关键词： Zero-touch network and service management(ZSM) Next generation networks(NGN) Artificial intelligence(AI) Machine learning(ML)

From Network Functions to NetApps: The 5GASP Methodology

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Computers, Materials & Continua 2022年第5期71卷 4115-4134页

作者： Jorge Gallego-Madrid ramon Sanchez-Iborra Antonio Skarmeta University of Murcia Murcia30100Spain University Center of Defense General Air Force AcademySan Javier30720Spain

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.

关键词： 5G NetApp 5GASP vOBU