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 safet...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.展开更多
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 Coop...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.展开更多
Personal Air Mobility(PAM)is poised to transform urban and regional transportation by offering efficient,on-demand aerial solutions to alleviate ground congestion and reduce environmental impact.This paper explores th...Personal Air Mobility(PAM)is poised to transform urban and regional transportation by offering efficient,on-demand aerial solutions to alleviate ground congestion and reduce environmental impact.This paper explores the integration of recycled CO_(2)-derived carbon fiber reinforced polymer(CFRP)composites and advanced pro-pulsion systems-electric,hydrogen-based(combustion engines and fuel cells),and e-jet fuels-in PAM vehicles,such as electric Vertical Take-Off and Landing(eVTOL)aircraft and flying cars.CO_(2)-CFRP,with its carbon-negative production and 40-60%weight reduction compared to metals,enhances airframe efficiency,ex-tends range,and supports sustainability goals.Electric propulsion excels for short-range urban missions due to its high efficiency and low noise,while hydrogen and e-jet fuels enable longer regional routes,leveraging high energy densities and compatibility with existing infrastructure.Synergies between CO_(2)-CFRP and these pro-pulsion systems optimize performance,reducing lifecycle emissions by 50-70%and aligning with the Inter-national Civil Aviation Organization’s net-zero emissions target by 2050.However,technical challenges,such as scaling CO_(2)-CFRP production,improving battery energy density,and developing hydrogen and e-fuel infra-structure,alongside regulatory and societal barriers,must be addressed.Through collaborative research,publicprivate partnerships,and policy incentives,PAM can overcome these hurdles,redefining urban mobility with sustainable,high-performance solutions that make urban skies as navigable as city streets.展开更多
基金This work has been supported by the Spanish Ministry of Science,Innovation and Universities,under the Ramon y Cajal Program(ref.RYC-2017-23823)the projects ONOFRE 3(ref.PID2020-112675RB)and Go2Edge(ref.RED2018-102585-T)+1 种基金by the European Commission,under the 5G-MOBIX(ref.825496)projectby the Spanish Ministry for the Ecological Transition and the Demographic Challenge,under the MECANO project(ref.PGE-MOVES-SING-2019-000104).
文摘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.
基金This work has been supported by the Spanish Ministry of Science,Innovation and Universities,under the Ramon y Cajal Program(ref.RYC-2017-23823)and the projects PERSEIDES(ref.TIN2017-86885-R)and Go2Edge(ref.RED2018-102585-T)the European Commission,under the 5G-MOBIX(Grant No.825496)and IoTCrawler(Grant No.779852)projectsthe Spanish Ministry of Energy,through the project MECANO(ref.PGE-MOVESSING-2019-000104).
文摘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.
文摘Personal Air Mobility(PAM)is poised to transform urban and regional transportation by offering efficient,on-demand aerial solutions to alleviate ground congestion and reduce environmental impact.This paper explores the integration of recycled CO_(2)-derived carbon fiber reinforced polymer(CFRP)composites and advanced pro-pulsion systems-electric,hydrogen-based(combustion engines and fuel cells),and e-jet fuels-in PAM vehicles,such as electric Vertical Take-Off and Landing(eVTOL)aircraft and flying cars.CO_(2)-CFRP,with its carbon-negative production and 40-60%weight reduction compared to metals,enhances airframe efficiency,ex-tends range,and supports sustainability goals.Electric propulsion excels for short-range urban missions due to its high efficiency and low noise,while hydrogen and e-jet fuels enable longer regional routes,leveraging high energy densities and compatibility with existing infrastructure.Synergies between CO_(2)-CFRP and these pro-pulsion systems optimize performance,reducing lifecycle emissions by 50-70%and aligning with the Inter-national Civil Aviation Organization’s net-zero emissions target by 2050.However,technical challenges,such as scaling CO_(2)-CFRP production,improving battery energy density,and developing hydrogen and e-fuel infra-structure,alongside regulatory and societal barriers,must be addressed.Through collaborative research,publicprivate partnerships,and policy incentives,PAM can overcome these hurdles,redefining urban mobility with sustainable,high-performance solutions that make urban skies as navigable as city streets.
