Survey of smart parking systems

The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, looking for the most optimal path, are needed. Most contributions in the literature are routing strategies that take into account different criteria to select the optimal route required to find a parking space. This paper aims to identify the types of smart parking systems (SPS) that are available today, as well as investigate the kinds of vehicle detection techniques (VDT) they have and the algorithms or other methods they employ, in order to analyze where the development of these systems is at today. To do this, a survey of 274 publications from January 2012 to December 2019 was conducted. The survey considered four principal features: SPS types reported in the literature, the kinds of VDT used in these SPS, the algorithms or methods they implement, and the stage of development at which they are. Based on a search and extraction of results methodology, this work was able to effectively obtain the current state of the research area. In addition, the exhaustive study of the studies analyzed allowed for a discussion to be established concerning the main difficulties, as well as the gaps and open problems detected for the SPS. The results shown in this study may provide a base for future research on the subject.Fil: Diaz Ogás, Mathias Gabriel. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Fabregat Gesa, Ramon. Universidad de Girona; EspañaFil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin

Towards Intelligent and Generic LBS for Drivers and Mobile Users

In this talk I will focus on Location-Based Services (LBS) for hybrid networks composed of both vehicles and mobile users. The motivation is the interest of studying data management solutions that take into account a generic environment where different types of moving objects share different types of data and possibly using different communication technologies (ad hoc wireless communications forming a pure mobile P2P network, hybrid mobile P2P network with support infrastructure nodes, wide-area communications like 3G, etc.). I will start by summarizing some data management challenges for vehicular networks, related to the exchange of events (efficient and effective content-based data dissemination for push-based data access), query processing (pull-based data access by using query dissemination or mobile agent technology), data item relevance evaluation, management of information about scarce resources (like available parking spaces or charge stations for electric vehicles), semantic data management, automatic knowledge extraction from the data items, multimedia data management, incentives, and trust. Then ..

On the Feasibility of Social Network-based Pollution Sensing in ITSs

Intense vehicular traffic is recognized as a global societal problem, with a multifaceted influence on the quality of life of a person. Intelligent Transportation Systems (ITS) can play an important role in combating such problem, decreasing pollution levels and, consequently, their negative effects. One of the goals of ITSs, in fact, is that of controlling traffic flows, measuring traffic states, providing vehicles with routes that globally pursue low pollution conditions. How such systems measure and enforce given traffic states has been at the center of multiple research efforts in the past few years. Although many different solutions have been proposed, very limited effort has been devoted to exploring the potential of social network analysis in such context. Social networks, in general, provide direct feedback from people and, as such, potentially very valuable information. A post that tells, for example, how a person feels about pollution at a given time in a given location, could be put to good use by an environment aware ITS aiming at minimizing contaminant emissions in residential areas. This work verifies the feasibility of using pollution related social network feeds into ITS operations. In particular, it concentrates on understanding how reliable such information is, producing an analysis that confronts over 1,500,000 posts and pollution data obtained from on-the- field sensors over a one-year span.Comment: 10 pages, 15 figures, Transaction Forma

Investigating into the Prevalence of Complex Event Processing and Predictive Analytics in the Transportation and Logistics Sector: Initial Findings From Scientific Literature

As ever new sensor solutions are invading people’s everyday lives and business processes, the use of the signals and events provided by the devices poses a challenge. Innovative ways of handling the large amount of data promise an effective and efficient means to overcome that challenge. With the help of complex event processing and predictive techniques, added value can be created. While complex event processing is able to process the multitude of signals coming from the sensors in a continuous manner, predictive analytics addresses the likelihood of a certain future state or behavior by detecting patterns from the signal database and predicting the future according to the detections. As to the transportation and logistics domain, processing the signal stream and predicting the future promises a big impact on the operations because the transportation and logistics sector is known as a very complex one. The complexity of the sector is linked with the many stakeholders taking part in a variety of operations and the partly high level of automation often being accompanied by manual processes. Hence, predictions help to prepare better for upcoming situations and challenges and, thus, to save resources and cost. The present paper is to investigate the prevalence of complex event processing and predictive analytics in logistics and transportation cases in the research literature in order to motivate a subsequent systematic literature view as the next step in the research endeavor

Assessing Sustainability in the Shipbuilding Supply Chain 4.0: A Systematic Review

The supply chain is currently taking on a very important role in organizations seeking to improve the competitiveness and profitability of the company. Its transversal character mainly places it in an unbeatable position to achieve this role. This article, through a study of each of the key enabling technologies of Industry 4.0, aims to obtain a general overview of the current state of the art in shipbuilding adapted to these technologies. To do so, a systematic review of what the scientific community says is carried out, dividing each of the technologies into different categories. In addition, the global vision of countries interested in each of the enabling technologies is also studied. Both studies present a general vision to the companies of the concerns of the scientific community, thus encouraging research on the subject that is focused on the sustainability of the shipbuilding supply chain

The Visual Social Distancing Problem

One of the main and most effective measures to contain the recent viral outbreak is the maintenance of the so-called Social Distancing (SD). To comply with this constraint, workplaces, public institutions, transports and schools will likely adopt restrictions over the minimum inter-personal distance between people. Given this actual scenario, it is crucial to massively measure the compliance to such physical constraint in our life, in order to figure out the reasons of the possible breaks of such distance limitations, and understand if this implies a possible threat given the scene context. All of this, complying with privacy policies and making the measurement acceptable. To this end, we introduce the Visual Social Distancing (VSD) problem, defined as the automatic estimation of the inter-personal distance from an image, and the characterization of the related people aggregations. VSD is pivotal for a non-invasive analysis to whether people comply with the SD restriction, and to provide statistics about the level of safety of specific areas whenever this constraint is violated. We then discuss how VSD relates with previous literature in Social Signal Processing and indicate which existing Computer Vision methods can be used to manage such problem. We conclude with future challenges related to the effectiveness of VSD systems, ethical implications and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this manuscript and they are listed by alphabetical order. Under submissio

An Information-Centric Platform for Social- and Location-Aware IoT Applications in Smart Cities

Recent advances in Smart City infrastructures and the Internet of Things represent a significant opportunity to improve people's quality of life. Corresponding research often focuses on Cloud-centric network architectures where sensor devices transfer collected data to the Cloud for processing. However, the formidable traffic generated by countless IoT devices and the need for low-latency services raise the need to move away from centralized architectures and bring the computation closer to the data sources. To this end, this paper discusses SPF, a middleware solution that supports IoT application development, deployment, and management. SPF runs IoT services on capable devices located at the network edge and proposes an information-centric programming model that takes advantage of decentralized computation resources located in the proximity of application users and data sources. SPF also adopts Value-of-Information based methods to prioritize the transmission of essential information