Subsampling OFDM-based ultrasonic data communication through metallic channels for monitoring of cargo containers

An enhanced ultrasonic communication system based on piezoelectric transducers for monitoring of goods in cargo containers is presented. The proposed system consists of several sensors placed inside the container, whose data are collected and transmitted outside it. Data transmission is carried out by an ultrasonic communication channel, in order to avoid drilling the wall of the container. The proposed data communication system is based on the transmission of a 128-OFDM signal. This modulation has been chosen due to its robustness to channels with frequency-selective fading and its spectrum efficiency. In order to increase the signal bandwidth and to reduce the power consumption at the internal node (transmitter), the proposed system exploits the non-linearity of the metallic channel to transmit at higher resonance frequencies. Moreover, power consumption at the external node (receiver) is reduced by using a subsampling based receiver, which allows its implementation by low-cost electronics.This work was supported by the Spanish Ministry of Economy and Competitiveness under Projects TEC2016-80396-C2-2-R and TEC2016-80396-C2-1-

Hybrid ports: the role of IoT and Cyber Security in the next decade

The next future will be played on a cyber level that imposes the need to merge “physical” with “digital in all fields”: phygital will be the future of current world, in many sectors, primarily in the transportation fields. Nowadays ports are doing several investment to provide technical solution to attract freight flows, are they ready to provide an answer to the cyber threat? This paper wish to present an overview of the main implications related to the cyber threats and maritime transports

Data-driven curation, learning and analysis for inferring evolving IoT botnets in the wild

The insecurity of the Internet-of-Things (IoT) paradigm continues to wreak havoc in consumer and critical infrastructure realms. Several challenges impede addressing IoT security at large, including, the lack of IoT-centric data that can be collected, analyzed and correlated, due to the highly heterogeneous nature of such devices and their widespread deployments in Internet-wide environments. To this end, this paper explores macroscopic, passive empirical data to shed light on this evolving threat phenomena. This not only aims at classifying and inferring Internet-scale compromised IoT devices by solely observing such one-way network traffic, but also endeavors to uncover, track and report on orchestrated "in the wild" IoT botnets. Initially, to prepare the effective utilization of such data, a novel probabilistic model is designed and developed to cleanse such traffic from noise samples (i.e., misconfiguration traffic). Subsequently, several shallow and deep learning models are evaluated to ultimately design and develop a multi-window convolution neural network trained on active and passive measurements to accurately identify compromised IoT devices. Consequently, to infer orchestrated and unsolicited activities that have been generated by well-coordinated IoT botnets, hierarchical agglomerative clustering is deployed by scrutinizing a set of innovative and efficient network feature sets. By analyzing 3.6 TB of recent darknet traffic, the proposed approach uncovers a momentous 440,000 compromised IoT devices and generates evidence-based artifacts related to 350 IoT botnets. While some of these detected botnets refer to previously documented campaigns such as the Hide and Seek, Hajime and Fbot, other events illustrate evolving threats such as those with cryptojacking capabilities and those that are targeting industrial control system communication and control services

Cyber Security in the Maritime Industry: A Systematic Survey of Recent Advances and Future Trends

The paper presents a classification of cyber attacks within the context of the state of the art in the maritime industry. A systematic categorization of vessel components has been conducted, complemented by an analysis of key services delivered within ports. The vulnerabilities of the Global Navigation Satellite System (GNSS) have been given particular consideration since it is a critical subcategory of many maritime infrastructures and, consequently, a target for cyber attacks. Recent research confirms that the dramatic proliferation of cyber crimes is fueled by increased levels of integration of new enabling technologies, such as IoT and Big Data. The trend to greater systems integration is, however, compelling, yielding significant business value by facilitating the operation of autonomous vessels, greater exploitation of smart ports, a reduction in the level of manpower and a marked improvement in fuel consumption and efficiency of services. Finally, practical challenges and future research trends have been highlighted

Sustainable Mobility and Transport

This Special Issue is dedicated to sustainable mobility and transport, with a special focus on technological advancements. Global transport systems are significant sources of air, land, and water emissions. A key motivator for this Special Issue was the diversity and complexity of mitigating transport emissions and industry adaptions towards increasingly stricter regulation. Originally, the Special Issue called for papers devoted to all forms of mobility and transports. The papers published in this Special Issue cover a wide range of topics, aiming to increase understanding of the impacts and effects of mobility and transport in working towards sustainability, where most studies place technological innovations at the heart of the matter. The goal of the Special Issue is to present research that focuses, on the one hand, on the challenges and obstacles on a system-level decision making of clean mobility, and on the other, on indirect effects caused by these changes