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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

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

Parking Availability Prediction with Coarse-Grained Human Mobility Data

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Computers, Materials & Continua 2022年第6期71卷 4355-4375页

作者： Aurora Gonzalez-Vidal Fernando Terroso-Sáenz Antonio Skarmeta University of Murcia Murcia30003Spain Universidad Católica de Murcia(UCAM) Murcia30107Spain

Nowadays,the anticipation of parking-space demand is an instrumental service in order to reduce traffic congestion levels in urban spaces.The purpose of our work is to study,design and develop a parking-availability predictor that extracts the knowledge from human mobility data,based on the anonymized human displacements of an urban area,and also from weather conditions.Most of the existing solutions for this prediction take as contextual data the current road-traffic state defined at very high temporal or spatial resolution.However,access to this type of fine-grained location data is usually quite limited due to several economic or privacy-related restrictions.To overcome this limitation,our proposal uses urban areas that are defined at very low spatial and temporal resolution.We conducted several experiments using three Artificial Neural Networks:Multilayer Perceptron,Gated Recurrent Units and bidirectional Long Short Term Memory networks and we tested their suitability using different combinations of inputs.Several metrics are provided for the sake of comparison within our study and between other studies.The solution has been evaluated in a real-world testbed in the city of Murcia(Spain)integrating an open human-mobility dataset showing high accuracy.A MAPE between 4%and 10%was reported in horizons of 1 to 3 h.

关键词： Parking space human mobility mining recurrent neural networks prediction

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)