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TinyML-Based Fall Detection for Connected Personal Mobility Vehicles

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

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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)

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

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Digital Communications and Networks 2023年第1期9卷 270-279页

作者： Jesus Sanchez-Gomez Rafael Marin-Perez Ramon Sanchez-Iborra Miguel Angel Zamora Department of Information and Communications Engineering University of MurciaMurciaSpain Odin Solutions S.L. MurciaSpain University Center of Defense San Javier Air Force BaseMurciaSpain

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.

关键词： Internet of moving things LPWAN Authentication LO-CoAP-EAP SCHC

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