In order to optimising the sea traffic network efficiency, improving the safety of shipping and the protection of the environment, it is useful to model the sea network and its spatio-temporal characteristics of the ship patterns. These maritime patterns could also be an a-priori set of knowledge for the upcoming Maritime Autonomous Surface Ships (MASS) which are starting to navigate our seas with or without remote human controls. The above concepts are crucial and essential elements for defining and understanding the Maritime Situational Awareness (MSA). Nowadays the applied methodologies for modelling the maritime traffic use large scale of database for extracting the patterns. The Knowledge Discovery from Data (KDD), strictly connected with Data Mining (DM) is growing significantly to modelling the behaviour of the vessels in relations to their surroundings. This is just one example that confirms the growing up of the cloud computing usage for maritime applications too. Besides these applications there are also a continuous and fast evolution of the IT services, which more often than not means data centre scale-ups with consequent improve of power consumptions. This paper is a case study based on real world data assessing a multi-objective energy consumption analysis. It is based on the comparison between the traditional air conditioning structures known as Heating, Ventilation and Air Conditioning (HVAC) and the Free Cooling Technique (FCT) in order to reduce the data centre power consumption keeping the same number of computational calculations performed.

Emerging technology trends can seem both elusive and ephemeral but some become integral to business and IT strategies and form the backbone of tomorrow’s business model and technology innovation. Companies (and Administrations) must examine the business impact of these trends and adjust business models and operations appropriately or risk losing competitive advantage to those who do. Rather the technology being difficult it is the implementation of it that could be a challenge. We’re working in an environment where volumes and complexity are increasing, but budgets are decreasing. How to sense and act upon a future that remains unclear? It is required to think very differently about the way to conceive and deliver technology services. The technology is the last step of the foresight process. The author aims to provide an answer to the above enquire starting from the identification of technologies and future technological concepts having potentially a significant impact on maritime traffic management and border control systems and the community in the medium to long term, i.e. 5 to 20 years. It is aimed at the idea of capacity building, not simply forecasting. A brief history of Vessel Traffic Services (VTS) followed by some systems engineering considerations are presented in paragraph 1 with connections to technology trends such as intelligent, digital and mesh in the next paragraph. On maritime domain these means, for instance, moving from traditional VTS to Maritime Service Portfolios (MSP) for e-Navigation. Bioinspired technologies forecasts are presented in paragraph 3 with examples of concrete practical use and possible further applications: drones, camera tracking and classification systems and passive as well as cognitive radars. Conclusions and a brief outlook will close the text.

The use of radar-based systems for vessel monitoring is not suitable in populated areas, due to the high electromagnetic emissions. In this paper, a camera based vessel recognition system for application in the context of Vessel Traffic Services (VTS) and Homeland Protection (HP) is proposed. Our approach is designed to extend the functionality of traditional VTS systems by permitting the classification of both cooperative and non-cooperative targets, using camera images only. This allows enhancing the surveillance function in populated areas, where public opinion is strongly concerned about electromagnetic emissions and therefore antennas are suspiciously observed and radars are not allowed. Experiments have been carried out on a publicly available data set of images coming from the ARGOS boat traffic monitoring system in the City of Venice (Italy). The obtained classification accuracy of 89.6% (with 11 different classes of boats) demonstrates the effectiveness of the proposed approach.

The constant increase in marine traffic and the simultaneous growth of the demand for exploiting marine areas (e.g., installing offshore wind power plants) require an adequate planning strategy for managing high traffic volumes. Maritime Spatial Planning (MSP) is the process of public development of an allocation plan for distributing, both spatially and temporally, human activities in marine areas. The adoption of e-Navigation is a possible solution for improving safety and security at sea by integrating maritime information on board and ashore. Automatic Identification System (AIS) data represents a fundamental source of information, since the analysis of AIS data can highlight the presence of congested areas as well as of illegal actions, such as smuggling, pollution, and unauthorized phishing in protected areas. Indeed, those activities are often characterized by abnormal manoeuvres that can be recognized by analyzing the routes of the vessels. However, the huge dimension of the AIS data to process requires the adoption of careful strategies for the data visualization. In this paper, we present a complete pipeline for visualizing ship routes from raw AIS data, which is a fundamental pre-requisite for carrying out a significant AIS-based route analysis, and describe a real case study, where 90 million AIS records, corresponding to one month of world-wide observations, are visualized using only open-source software.