Abstract

Structural Health Monitoring (SHM) is essential to assess the accuracy of durability predictions of structures. Using low-cost sensors on structural evaluation has gained significant attention compare to high-cost sensors. Although these may not be as accurate and sensitive as the expensive electronic devices, with efficient code and right use, there is a possibility of getting useful information from them. These sensors can vary based on their functionality and the measurements they provide. For example, one is highly sensitive to the light of its environment while the other kind would give different results in different temperatures. In this paper, three different displacement measuring sensors have been studied. An ultrasonic sensor (HC-SR04) and two different types of laser sensors (VL53L0X and VL53L1X) are investigated in the paper. An Arduino Mega has captured their measured data, and a raspberry pi has made the acquisition. Not only issues regarding coding and placing of these sensors have been presented ultimately, but precise solutions for the aforementioned problems as well as an efficient way of assembling all the sensors are also presented in this paper. The data generated from these electronic devices can be used for Structural Health Monitoring applications.

Abstract

International audience; Indoor occupancy estimation is a critical analytical task for several applications (e.g., social isolation of elderlies). The proliferation of Internet of Things (IoT) devices enabled the occupancy estimation, as it provided access to a mass amount of data. Several works have been proposed exploiting the IoT Passive Inference (PIR) or environmental (e.g., $CO_2$) features. These works however are traditionally selecting the feature space at the learning phase and passively using it over time. Hence, they ignore the dynamics of indoor occupancy, such as the location of the occupant or his motion patterns, leading to a decreasing accuracy over time. In this paper, we study those dynamics and show that motion patterns, along with environmental features favor the occupancy estimation. We design a Location-Aware Hidden Markov Model (HMM), which dynamically adapts the feature space based on the occupant's location. Our experiments on real data show that Location-Aware HMM can reach up to 10% better accuracy than Conventional HMM.

Abstract

This paper describes how the Automatic Identification System - Application Specific Message (AIS/ASM) via Low Earth Orbit (LEO) satellites can be used to transmit small data between ship and shore to facilitate information visibility in future maritime transport systems. The focus is on ships without advanced satellite terminals in remote areas where terrestrial infrastructure is not available. Here, AIS is proposed as a low cost and general alternative to ordinary satellite communications. In this paper, we report on how reliable ASM is for data exchange, for instance for cargo-monitoring data. One problem is to determine when the LEO satellite is in a geographic position to receive messages from the ship. Another problem is that the AIS satellite can fail to register data, even when in the radio range of the ship, because the ship antenna is constructed for horizontal radiation and sends very little signal upwards, especially when the satellite is at its closest to the ship. The study uses AIS data from the Norwegian Coastal Administration to determine the probability that specific AIS transmitters are detected by the satellites.

Abstract

On-demand shared mobility is increasingly being promoted as an influential strategy to address urban transport challenges in large and fast-growing cities. The appeal of this form of transport is largely attributed to its convenience, ease of use, and affordability made possible through digital platforms and innovations. The convergence of the shared economy with a number of established and emerging technologies—such as artificial intelligence (AI), Internet of Things (IoT), and Cloud and Fog computing—is helping to expedite their deployment as a new form of public transport. Recently, this has manifested itself in the form of Flexible Mobility on Demand (FMoD) solutions, aimed at meeting personal travel demands through flexible routing and scheduling. Increasingly, these shared mobility solutions are blurring the boundaries with existing forms of public transport, particularly bus operations. This paper presents an environmental scan and analysis of the technological, social, and economic impacts surrounding disruptive technology-driven shared mobility trends. Specifically, the paper includes an examination of current and anticipated external factors that are of direct relevance to collaborative and low carbon mobility. The paper also outlines how these trends are likely to influence the mobility industries now and into the future. The paper collates information from a wide body of literature and reports on findings from actual ‘use cases’ that exist today which have used these disruptive mobility solutions to deliver substantial benefits to travellers around the world. Finally, the paper provides stakeholders with insight into identifying and responding to the likely needs and impacts of FMoD and informs their policy and strategy positions on the implementation of smart mobility systems in their cities and jurisdictions. Document type: Article

Abstract

Smart devices and their connections to the Internet of Things (IoT) have been the subject of many papers in the past decade. In the context of IoT in transportation, one feature is the smart junction. This research deals with this junction, where several cars approach the intersection from different directions, and a smart traffic light must decide regarding the time intervals of red and green light in each direction. Out novel approach is based not only on the number of vehicles in each lane, but also on the social characteristics of the passengers (e.g. a handicapped person, a driver with no previous traffic violations). These factors will be gleaned from IoT network sources on cars, traffic lights, individuals, municipality data, and more. In this paper, we suggest using a VCG (Vickrey-Clarke-Groves) auction mechanism for the intersection scheduling, combining the social characteristic with a benefit parameter that expresses the passenger’s subjective perception of the importance of crossing the intersection as soon as possible. Our simulation results show the efficiency of the suggested protocol and demonstrate how the intersection scheduling depends on the passengers’ preferences, as well as on their social priorities. Document type: Article