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

Performance of management solutions and cooperation approaches for vehicular delay-tolerant networks

A wide range of daily-life applications supported by vehicular networks attracted the interest, not only from the research community, but also from governments and the automotive industry. For example, they can be used to enable services that assist drivers on the roads (e.g., road safety, traffic monitoring), to spread commercial and entertainment contents (e.g., publicity), or to enable communications on remote or rural regions where it is not possible to have a common network infrastructure. Nonetheless, the unique properties of vehicular networks raise several challenges that greatly impact the deployment of these networks. Most of the challenges faced by vehicular networks arise from the highly dynamic network topology, which leads to short and sporadic contact opportunities, disruption, variable node density, and intermittent connectivity. This situation makes data dissemination an interesting research topic within the vehicular networking area, which is addressed by this study. The work described along this thesis is motivated by the need to propose new solutions to deal with data dissemination problems in vehicular networking focusing on vehicular delay-tolerant networks (VDTNs). To guarantee the success of data dissemination in vehicular networks scenarios it is important to ensure that network nodes cooperate with each other. However, it is not possible to ensure a fully cooperative scenario. This situation makes vehicular networks suitable to the presence of selfish and misbehavior nodes, which may result in a significant decrease of the overall network performance. Thus, cooperative nodes may suffer from the overwhelming load of services from other nodes, which comprises their performance. Trying to solve some of these problems, this thesis presents several proposals and studies on the impact of cooperation, monitoring, and management strategies on the network performance of the VDTN architecture. The main goal of these proposals is to enhance the network performance. In particular, cooperation and management approaches are exploited to improve and optimize the use of network resources. It is demonstrated the performance gains attainable in a VDTN through both types of approaches, not only in terms of bundle delivery probability, but also in terms of wasted resources. The results and achievements observed on this research work are intended to contribute to the advance of the state-of-the-art on methods and strategies for overcome the challenges that arise from the unique characteristics and conceptual design of vehicular networks.O vasto número de aplicações e cenários suportados pelas redes veiculares faz com que estas atraiam o interesse não só da comunidade científica, mas também dos governos e da indústria automóvel. A título de exemplo, estas podem ser usadas para a implementação de serviços e aplicações que podem ajudar os condutores dos veículos a tomar decisões nas estradas, para a disseminação de conteúdos publicitários, ou ainda, para permitir que existam comunicações em zonas rurais ou remotas onde não é possível ter uma infraestrutura de rede convencional. Contudo, as propriedades únicas das redes veiculares fazem com que seja necessário ultrapassar um conjunto de desafios que têm grande impacto na sua aplicabilidade. A maioria dos desafios que as redes veiculares enfrentam advêm da grande mobilidade dos veículos e da topologia de rede que está em constante mutação. Esta situação faz com que este tipo de rede seja suscetível de disrupção, que as oportunidades de contacto sejam escassas e de curta duração, e que a ligação seja intermitente. Fruto destas adversidades, a disseminação dos dados torna-se um tópico de investigação bastante promissor na área das redes veiculares e por esta mesma razão é abordada neste trabalho de investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à disseminação dos dados em ambientes veiculares. Para garantir o sucesso da disseminação dos dados em ambientes veiculares é importante que este tipo de redes garanta a cooperação entre os nós da rede. Contudo, neste tipo de ambientes não é possível garantir um cenário totalmente cooperativo. Este cenário faz com que as redes veiculares sejam suscetíveis à presença de nós não cooperativos que comprometem seriamente o desempenho global da rede. Por outro lado, os nós cooperativos podem ver o seu desempenho comprometido por causa da sobrecarga de serviços que poderão suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de estratégias de cooperação, monitorização e gestão de rede no desempenho das redes veiculares com ligações intermitentes (Vehicular Delay-Tolerant Networks - VDTNs). O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global da rede. Em particular, as estratégias de cooperação e gestão de rede são exploradas para melhorar e optimizar o uso dos recursos da rede. Ficou demonstrado que o uso deste tipo de estratégias e metodologias contribui para um aumento significativo do desempenho da rede, não só em termos de agregados de pacotes (“bundles”) entregues, mas também na diminuição do volume de recursos desperdiçados. Os resultados observados neste trabalho procuram contribuir para o avanço do estado da arte em métodos e estratégias que visam ultrapassar alguns dos desafios que advêm das propriedades e desenho conceptual das redes veiculares

Agrégation et routage efficace de données dans les réseaux de capteurs sans fils

Wireless Sensor Networks (WSNs) have gained much attention in a large range of technical fields such as industrial, military, environmental monitoring etc. Sensors are powered by batteries, which are not easy to replace in harsh environments. The energy stored by each sensor is the greatest impediment for increasing WSN lifetime. Since data transmission consumes more energy, our major concern is how to efficiently transmit the data from all sensors towards a sink. We suggest three tree-based data aggregation algorithms: Depth-First Search Aggregation (DFSA), Flooding Aggregation (FA) and Well-Connected Dominating Set Aggregation (WCDSA) to reduce the number of transmissions from each sensor towards the sink. Tree-based data aggregation suffers from increased data delivery time because the parents must wait for the data from their leaves. Some parents might have many leaves, making it very expensive for a parent to store all incoming data in its buffer. We need to determine the aggregation time each parent in the tree has to spend in aggregating and processing the data from its leaves. We propose an Efficient Tree-based Aggregation and Processing Time (ETAPT) algorithm using Appropriate Data Aggregation and Processing Time (ADAPT) metric. Given the maximum acceptable latency, ETAPT's algorithm takes into account the position of parents, their number of leaves and the depth of the tree, in order to compute an optimal ADAPT time. At any time, the amount of data aggregated by parents may become greater than the amount of data that can be forwarded. We propose the introduction into the network of many data aggregators called Mini-Sinks (MSs). MSs are mobile and move according to a random mobility model inside the sensor field to maintain the fully-connected network in order to aggregate the data based on the controlled Multipath Energy Conserving Routing Protocol (MECRP). Sensors may use many radio interfaces sharing a single wireless channel, which they may use to communicate with several neighbours. Two sensors operating on the same wireless channel may interfere with each other during the transmission of data. We need to know which channel to use in the presence of multiple channels for a given transmission. We propose a distributed Well-Connected Dominating Set Channel Assignment (WCDS-CA) approach, in which the number of channels that are needed over all sensor nodes in the network in such a way that adjacent sensor nodes are assigned to distinct channels.Les Réseaux de Capteurs Sans Fils (RCSFs) ont pris beaucoup d'importance dans plusieurs domaines tels que l'industrie, l'armée, la pollution atmosphérique etc. Les capteurs sont alimentés par des batteries qui ne sont pas faciles à remplacer surtout dans les environnements peu accessibles. L'énergie de chaque capteur est considérée comme la source première d'augmentation de la durée de vie des RCSFs. Puisque la transmission de données est plus coûteuse en consommation d'énergie, notre préoccupation première est de proposer une technique efficace de transmission des données de tous les capteurs vers le sink tout en réduisant la consommation en énergie. Nous suggérons trois trois algorithmes d'agrégation de données basé sur la construction d'arbres : Depth-First Search Aggregation (DFSA), Flooding Aggregation (FA) et Well-Connected Dominating Set Aggregation (WCDSA) qui permettront de réduire le nombre de transmissions de chaque capteur vers le sink. L'agrégation des données basée sur la construction d'arbres souffre du délai de délivrance de données parce que les parents doivent attendre de recevoir les données de leurs feuilles. Certains parents pourraient avoir beaucoup de feuilles, et il serait alors assez coûteux pour un parent de stocker toutes les données entrantes dans sa mémoire. Ainsi, nous devons déterminer le temps que chaque parent doit mettre pour agréger et traiter les données de ses feuilles. Nous proposons un algorithme, Efficient Tree-based Aggregation and Processing Time (ETAPT) qui utilise la métrique Appropriate Data Aggregation and Processing Time (ADAPT). Etant donné la durée maximale acceptable, l'algorithme ETAPT prend en compte la position des parents, le nombre de feuilles et la profondeur de l'arbre pour calculer l'ADAPT optimal. A n'importe quel moment pendant l'agrégation des données par les parents, il peut arriver que la quantité de données collectées soit très grande et dépasse la quantité de stockage maximale de données que peut contenir leurs mémoires. Nous proposons l'introduction dans le réseau de plusieurs collecteurs de données appelés Mini-Sinks (MSs). Ces MSs sont mobiles et se déplacent selon un modèle de mobilité aléatoire dans le réseau pour maintenir la connexité afin d'assurer la collecte contrôlée des données basée sur le protocole de routage Mulipath Energy Conserving Routing Protocol (MECRP). Les capteurs peuvent être équipés de plusieurs interfaces radios partageant un seul canal sans fil avec lequel ils peuvent communiquer avec plusieurs voisins. La transmission des données à travers une liaison de communication entre deux parents peut interférer avec les transmissions d'autres liaisons si elles transmettent à travers le même canal. Nous avons besoin de savoir quel canal utiliser en présence de plusieurs canaux pour une transmission donnée. Nous proposons une méthode distribuée appelée: Well Connected Dominating Set Channel Assignement (WCDS-CA), pour calculer le nombre de canaux qui seront alloués à tous les capteurs de telle sorte que les capteurs adjacents se voient attribués des canaux différent

Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies (TR_C), Volume 145, 202

Applications of Internet of Things

This book introduces the Special Issue entitled “Applications of Internet of Things”, of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITSs), (II) location-based services (LBSs), and (III) sensing techniques and applications. Three papers on ITSs are as follows: (1) “Vehicle positioning and speed estimation based on cellular network signals for urban roads,” by Lai and Kuo; (2) “A method for traffic congestion clustering judgment based on grey relational analysis,” by Zhang et al.; and (3) “Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road anomaly detection,” by Ishikawa and Fujinami. Three papers on LBSs are as follows: (1) “A high-efficiency method of mobile positioning based on commercial vehicle operation data,” by Chen et al.; (2) “Efficient location privacy-preserving k-anonymity method based on the credible chain,” by Wang et al.; and (3) “Proximity-based asynchronous messaging platform for location-based Internet of things service,” by Gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) “Detection of electronic anklet wearers’ groupings throughout telematics monitoring,” by Machado et al.; and (2) “Camera coverage estimation based on multistage grid subdivision,” by Wang et al

Journal of Telecommunications and Information Technology, 2019, nr 2

kwartalni

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity