Remote vehicle monitoring is a field that has recently attracted the attention of both academia and industry. With the dawn of the Internet of Things (IoT) paradigm, the possibilities for performing this task have multiplied, due to the emergence of low-cost and multi-purpose monitoring devices and the evolution of wireless transmission technologies. Low Power-Wide Area Network (LPWAN) encompasses a set of IoT communication technologies that are gaining momentum, due to their highly valued features regarding transmission distance and end-device energy consumption. For that reason, in this work we present a vehicular monitoring platform enabled by LPWAN-based technology, namely Long Range Wide Area Network (LoRaWAN). Concretely, we explore the end-to-end architecture considering vehicle data retrieving by using an On-Board Diagnostics II (OBD-II) interface, their compression with a novel IETF compression scheme in order to transmit them over the constrained LoRaWAN link, and information visualization through a data server hosted in the cloud, by means of a web-based dashboard. A key advance of the proposal is the design and development of a UNIX-based network interface for LPWAN communications. The whole system has been tested in a university campus environment, showing its capabilities to remotely track vehicle status in real-time. The conducted performance evaluation also shows high levels of reliability in the transmission link, with packet delivery ratios over 95%. The platform boosts the process of monitoring vehicles, enabling a variety of services such as mechanical failure prediction and detection, fleet management, and traffic monitoring, and is extensible to light vehicles with severe power constraints.

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.

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.

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 50 µs.