A reference architecture for cloud-edge meta-operating systems enabling cross-domain, data-intensive, ML-assisted applications: architectural overview and key concepts

Future data-intensive intelligent applications are required to traverse across the cloudto-edge-to-IoT continuum, where cloud and edge resources elegantly coordinate, alongside sensor networks and data. However, current technical solutions can only partially handle the data outburst associated with the IoT proliferation experienced in recent years, mainly due to their hierarchical architectures. In this context, this paper presents a reference architecture of a meta-operating system (RAMOS), targeted to enable a dynamic, distributed and trusted continuum which will be capable of facilitating the next-generation smart applications at the edge. RAMOS is domain-agnostic, capable of supporting heterogeneous devices in various network environments. Furthermore, the proposed architecture possesses the ability to place the data at the origin in a secure and trusted manner. Based on a layered structure, the building blocks of RAMOS are thoroughly described, and the interconnection and coordination between them is fully presented. Furthermore, illustration of how the proposed reference architecture and its characteristics could fit in potential key industrial and societal applications, which in the future will require more power at the edge, is provided in five practical scenarios, focusing on the distributed intelligence and privacy preservation principles promoted by RAMOS, as well as the concept of environmental footprint minimization. Finally, the business potential of an open edge ecosystem and the societal impacts of climate net neutrality are also illustrated.For UPC authors: this research was funded by the Spanish Ministry of Science, Innovation and Universities and FEDER, grant number PID2021-124463OB-100.Peer ReviewedPostprint (published version

Spot Charter Rate Forecast for Liquefied Natural Gas Carriers

Recent maritime legislation demands the transformation of the transportation sector to greener and more energy efficient. Liquified natural gas (LNG) seems a promising alternative fuel solution that could replace the conventional fuel sources. Various studies have focused on the prediction of the LNG price; however, no previous work has been carried out on the forecast of the spot charter rate of LNG carrier ships, an important factor for the maritime industries and companies when it comes to decision-making. Therefore, this study is focused on the development of a machine learning pipeline to address the aforementioned problem by: (i) forming a dataset with variables relevant to LNG; (ii) identifying the variables that impact the freight price of LNG carrier; (iii) developing and evaluating regression models for short and mid-term forecast. The results showed that the general regression neural network presented a stable overall performance for forecasting periods of 2, 4 and 6 months ahead

A Swarm Intelligence Graph-Based Pathfinding Algorithm Based on Fuzzy Logic (SIGPAF): A Case Study on Unmanned Surface Vehicle Multi-Objective Path Planning

Advances in robotic motion and computer vision have contributed to the increased use of automated and unmanned vehicles in complex and dynamic environments for various applications. Unmanned surface vehicles (USVs) have attracted a lot of attention from scientists to consolidate the wide use of USVs in maritime transportation. However, most of the traditional path planning approaches include single-objective approaches that mainly find the shortest path. Dynamic and complex environments impose the need for multi-objective path planning where an optimal path should be found to satisfy contradicting objective terms. To this end, a swarm intelligence graph-based pathfinding algorithm (SIGPA) has been proposed in the recent literature. This study aims to enhance the performance of SIGPA algorithm by integrating fuzzy logic in order to cope with the multiple objectives and generate quality solutions. A comparative evaluation is conducted among SIGPA and the two most popular fuzzy inference systems, Mamdani (SIGPAF-M) and Takagi–Sugeno–Kang (SIGPAF-TSK). The results showed that depending on the needs of the application, each methodology can contribute respectively. SIGPA remains a reliable approach for real-time applications due to low computational effort; SIGPAF-M generates better paths; and SIGPAF-TSK reaches a better trade-off among solution quality and computation time

A Simulation-Based Planning Tool for Floating Storage and Regasification Units

The objective of this paper was to propose a functional simulation model for the operation of floating storage and regasification units (FSRUs) used for the import of liquefied natural gas (LNG). The physical operation of an FSRU is decomposed for each critical component of the LNG carrier (LNGC) and the FSRU, in order to construct a realistic model in Simulink. LNG mass balance equations are used to perform flow calculations from the tanks of an LNG carrier to the tanks of the FSRU and from there to shore. The simulation model produces results for cases, when multiple LNG carriers discharge cargoes during a monthly time horizon. This produces an accurate operational profile for the FSRU with information about the volume of LNG inside each of the cargo tanks of the FSRU, LNG cargo discharging and gas send-out rate. Potential practitioners may exploit the proposed planning tool to explore the feasibility of alternative operation scenarios for an FSRU terminal. The simulations can check the system sensitivity to different parameters and support schedule regarding: (i) slots for LNG carrier calls, (ii) LNG inventory fluctuation, and (iii) impact of gas demand and send-out rate changes

Liner shipping cycle cost modelling, fleet deployment optimization and what-if analysis

This article formulates the mathematical model of the liner shipping company cycle cost and attempts to optimize the operational profile of company assets in regards to specific network of routes of cargo flows and vessels portfolio. In other words it attempts to give a practical solution to the modern shipping company fleet deployment problem. This is achieved by developing a generic cost model methodology that aims to minimize total operating costs by using Genetic Algorithms in optimizing various predefined attributes such as operational speed. The finalized model could be applicable to liner shipping companies for optimization purposes of liner networks, as well as for simulation and examination of possible scenarios and what-if analysis. In the era of recession, a demand shock is examined and, interesting results are produced. In further research, this model can estimate the impact of environmental legislation intensification. In the what-if analysis, the model can depict how an initial design of a liner system can be optimized by modifying system attributes to dynamically meet new requirements.

Optimizing the operation of port energy systems

Summarization: This paper analyses the challenges emerged according to the current trends to transform the ports into smart energy hubs. Within this frame the particular features of all related electrified equipment is discussed while a way to establish a mutually beneficial exploitation scheme is outlined.Παρουσιάστηκε στο: 19th IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europ