Future data-intensive intelligent applications are required to traverse across the cloud-to-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.

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.

The continuous increase in global maritime freight transport has led to an increase in emissions. The port of Heraklion was selected as a case study to investigate the environmental impact of shipping in wider areas. Two different maritime fuels were examined: the conventional maritime fuel, marine diesel oil (MDO), and an alternative maritime fuel, liquified natural gas (LNG). To carry out this study, real data from the port of Heraklion, the Lloyd’s Register Fairplay (LRF) Sea-Web database, and literature reviews were used. The bottom-up method was adopted for data processing. The results of this study demonstrate that alternative maritime fuels, such as LNG, could drastically reduce SO₂, NOx, PM, and CO₂ emissions.

Unmanned Surface Vehicles (USVs) are nowadays used in various applications for security, inspection and delivery among others. To operate in dynamic and complex environments efficiently demands an optimal path planning where multiple factors should be taken into account. In this paper, the multi-objective path planning problem of USV is formulated aiming to minimize the traveled distance maximizing in parallel the trajectory smoothness and energy efficiency. To address this multi-objective path planning problem with contradicting terms, the popular Ant Colony Optimization (ACO) algorithm is employed enhanced with the proposed Fuzzy Pareto framework. In particular, ACO is used to solve the problem by finding the Pareto solutions optimizing each single objective. Then these solutions are evaluated via the Mamdani fuzzy inference system to identify the optimal one. The ranking of the solutions is based on the defuzzification values. A case study is performed in a simulation area based on Saronic Gulf topology. The results showed that depending the needs of an operation and the conditions of the area of operations (number of obstacles, currents, and distance from the initial to the target point), each objective can impact the path quality differently.