Canarin II: Designing a smart e-bike eco-system

Aguiari, Davide; Delnevo, Giovanni; Monti, Lorenzo; Ghini, Vittorio; Mirri, Silvia; Salomoni, Paola; Pau, Giovanni; Im, Sio‐Kei; Tse, Rita; Ekpanyapong, Mongkol; Battistini, Roberto

doi:10.1109/ccnc.2018.8319221

Cited by 40 publications

(16 citation statements)

References 31 publications

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“…In 2018, the Canarin II portable sensors were upgraded to include multiple wireless connectivity options and a GPS (global positioning system). [ 44 ] They were then dispersed over the urban environment of Bologna, while mounted on publicly available e-bikes. The ability to visualize geolocalized air pollution data in real time was a major improvement.…”

Section: Methodsmentioning

confidence: 99%

Academically Produced Air Pollution Sensors for Personal Exposure Assessment: The Canarin Project

Dessimond

Annesi‐Maesano

Pépin

et al. 2021

Sensors

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The World Health Organization has estimated that air pollution is a major threat to health, causing approximately nine million premature deaths every year. Each individual has, over their lifetime, a unique exposure to air pollution through their habits, working and living conditions. Medical research requires dedicated tools to assess and understand individual exposure to air pollution in view of investigating its health effects. This paper presents portable sensors produced by the Canarin Project that provides accessible, real time personal exposure data to particulate matter. Our primary results demonstrate the use of portable sensors for the assessment of personal exposure to the different micro-environments attended by individuals, and for inspecting the short-term effects of air pollution through the example of sleep apnea. These findings underscore the necessity of obtaining contextual data in determining environmental exposure and give perspectives for the future of air pollution sensors dedicated to medical research.

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Section: Methodsmentioning

confidence: 99%

Academically Produced Air Pollution Sensors for Personal Exposure Assessment: The Canarin Project

Dessimond

Annesi‐Maesano

Pépin

et al. 2021

Sensors

Self Cite

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“…An Arduino-based platform was adopted in [24] to retrieve and report pollution data employing cellular communications. The authors of [25] developed a similar sensing platform although they made use of a smart phone tethering to transmit the gathered data. A similar solution was presented in [26], in this case employing LoRa and WiFi as communication technologies.…”

Section: Related Workmentioning

confidence: 99%

Eco-Efficient Mobility in Smart City Scenarios

2020

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Cooperative-Intelligent Transportation Systems (C-ITS) have brought a technological revolution, especially for ground vehicles, in terms of road safety, traffic efficiency, as well as in the experience of drivers and passengers. So far, these advances have been focused on traditional transportation means, leaving aside the new generation of personal vehicles that are nowadays flooding our streets. Together with bicycles and motorcycles, personal mobility devices such as segways or electric scooters are firm sustainable alternatives that represent the future to achieve eco-friendly personal mobility in urban settings. In a near future, smart cities will become hyper-connected spaces where these vehicles should be integrated within the underlying C-ITS ecosystem. In this paper, we provide a wide overview of the opportunities and challenges related to this necessary integration as well as the communication solutions that are already in the market to provide these moving devices with low-cost and efficient connectivity. We also present an On-Board Unit (OBU) prototype with different communication options based on the Low Power Wide Area Network (LPWAN) paradigm and several sensors to gather environmental information to facilitate eco-efficiency services. As the attained results suggest, this module allows personal vehicles to be fully integrated in smart city environments, presenting the possibilities of LoRaWAN and Narrow Band-Internet of Things (NB-IoT) communication technologies to provide vehicle connectivity and enable mobile urban sensing.

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“…The goal of MCS ecosystems is to inform citizens about the surrounding environment in real-time, so according to [ 51 ], users have a dynamic and always updated picture of the situation. In fact, smart mobility, together with the use of sensors for environmental monitoring and connectivity, can play a strategic role in providing useful information to citizens, so that they can improve their daily activities and the quality of their daily life [ 55 ]. These solutions are usually general enough to be used to monitor other kinds of environmental data, such as air pollution agents, contributing to the related analysis of fine particles.…”

Section: Related Workmentioning

confidence: 99%

“…The second component is an environmental sensing system that can monitor temperature, relative humidity, air pressure and Particulate Matter (PM), in particular PM 1.0, 2.5 and 10, based on a Canarin II multi-sensor platform [ 55 ]. This is a crucial component to gather additional environmental data and include them in the training activities, during which we can evaluate and quantify how such information can improve the precision of the whole noise sensing platform, with the aim of reaching our goal: overcoming the precision gap between a low-cost microphone and a sound level meter calibrated.…”

Section: Raveguard: a System Overviewmentioning

confidence: 99%

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RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning

Monti

Vincenzi

Mirri

et al. 2020

Sensors

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Urban noise is one of the most serious and underestimated environmental problems. According to the World Health Organization, noise pollution from traffic and other human activities, negatively impact the population health and life quality. Monitoring noise usually requires the use of professional and expensive instruments, called phonometers, able to accurately measure sound pressure levels. In many cases, phonometers are human-operated; therefore, periodic fine-granularity city-wide measurements are expensive. Recent advances in the Internet of Things (IoT) offer a window of opportunities for low-cost autonomous sound pressure meters. Such devices and platforms could enable fine time–space noise measurements throughout a city. Unfortunately, low-cost sound pressure sensors are inaccurate when compared with phonometers, experiencing a high variability in the measurements. In this paper, we present RaveGuard, an unmanned noise monitoring platform that exploits artificial intelligence strategies to improve the accuracy of low-cost devices. RaveGuard was initially deployed together with a professional phonometer for over two months in downtown Bologna, Italy, with the aim of collecting a large amount of precise noise pollution samples. The resulting datasets have been instrumental in designing InspectNoise, a library that can be exploited by IoT platforms, without the need of expensive phonometers, but obtaining a similar precision. In particular, we have applied supervised learning algorithms (adequately trained with our datasets) to reduce the accuracy gap between the professional phonometer and an IoT platform equipped with low-end devices and sensors. Results show that RaveGuard, combined with the InspectNoise library, achieves a 2.24% relative error compared to professional instruments, thus enabling low-cost unmanned city-wide noise monitoring.

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Canarin II: Designing a smart e-bike eco-system

Cited by 40 publications

References 31 publications

Academically Produced Air Pollution Sensors for Personal Exposure Assessment: The Canarin Project

Academically Produced Air Pollution Sensors for Personal Exposure Assessment: The Canarin Project

Eco-Efficient Mobility in Smart City Scenarios

RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning

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