Fish piracy remains widespread globally despite national and international efforts. Experts estimate it accounts for about 20% of the total seafood catch worldwide. Technology is playing a key role in detecting illegal fishing, with satellite imagery and sensors being used to track vessels and monitor fishing practices. Since fishing boats broadcast their positions using a vessel tracking system, this data can be processed to detect illegal activity. This study focuses on classifying fishing vessel trajectories using only positional data. A novel trajectory representation and a Convolutional Neural Network is employed, showing promising results compared to traditional methods.

Edge computing presents a promising paradigm for the management and processing of the vast volumes of data generated by Internet of Things (IoT) devices. By merging cloud services with decentralized processing at the edge of the network, edge computing optimizes resource utilization while mitigating communication overhead and data transfer delays. Despite advancements, there are issues regarding cloud/edge-based application requirements. A distributed edge storage solution is crucial, ensuring data proximity, minimizing network congestion, and adapting to changing demands. Nevertheless, implementing or selecting an efficient edge-enabled storage system presents numerous challenges due to the distributed and heterogeneous nature of the edge, as well as its limited resource capabilities. Hence, it is essential for the research community to actively contribute towards clarifying the objectives and delineating the strengths and weaknesses of different storage solutions. This work presents an overview and performance analysis of three storage solutions in the edge computing context, namely MinIO, IPFS, and BigchainDB. The evaluation considers a set of Quality of Service (QoS) and resource utilization metrics. The systems are deployed on a cluster of four Raspberry Pis, which function as a network of edge devices. The results demonstrate the superiority of IPFS and provide insights into the performance of the evaluated storage systems for edge deployments.

The 2nd International Workshop on Flexible Resource and Application Management on the Edge (FRAME 2022) is dedicated to the so-called Cloud/Edge Continuum, where Cloud and Edge infrastructures can work together to fulfill requirements from a variety of NextGen applications. Clouds provide appropriate levels of performance to large groups of different users, whereas Edge resources act as a first layer of computing capacity that is closer to the user, to reduce the service latency. With respect to Clouds, Edge infrastructures typically are composed of heterogeneous and constrained resources and introduce new challenges from the viewpoint of security, orchestration and resource management. Tackling these new issues calls for innovative combinations of tools and abstractions, where AI and machine learning techniques complement algorithmic orchestration and optimization, bringing about new levels of distributed adaptivity and self-management. As real-time data-driven decisions can be promptly taken on the spot, without the need to wait for data to travel to the Cloud and back, also interactive and time-sensitive services like the immersive data processing of Extended Reality (XR) applications can be partially extended toward the edge, thus exploiting a better computation to communication tradeoff and smoother connections to improve their QoE and remote collaboration. The FRAME'22 workshop proceedings are available at: https://dl.acm.org/citation.cfm?id=3526059.

The sudden rise of GPS-enabled mobile devices has given birth to research related to the analysis and visualization of big mobility data that are stored in large spatio-temporal databases. Therefore, this research is focused on evaluating and comparing widely-used database systems that are employed in the analysis of spatio-temporal data. Specifically, three database systems are evaluated and compared with each other, namely PostGIS, MobilityDB, and MongoDB, in their ability to perform range, temporal aggregate, distance and nearest-neighbor queries. To this end, a subset of the BerlinMOD benchmark queries is employed for evaluation purposes over vessel tracking data. The experimental results presented in this paper are only preliminary in an attempt to drive future research in the field of industrial use case surveillance.

The maritime industry is intensively embracing green thinking. According to the International Maritime Organization’s (IMO) Greenhouse Gas (GHG ) strategy, the total annual GHG emissions from international shipping should be reduced by at least 50% by 2050 compared to 2008. Shipping adopts policies to comply with the set target, including ship redesign, structural retrofit, use of low-carbon material, and the installation of emission abatement technologies. All these approaches pave the way to circularity in the maritime economy, abandoning the linear model in vessel lifetime and adopting lean management, re-manufacturing, and re-usability of the asset. To this end, in the SmartShip project, we give prominence to data-driven ship monitoring by delivering an Information and Communication Technology (ICT) & Internet of Things (IoT)-enabled holistic cloud-based maritime performance and monitoring system. This system is considering the entire lifecycle of a ship, aiming to optimize energy efficiency, emissions reduction, fuel consumption, while, at the same time, include circular economy concepts in the maritime field. Our approach supports a cost-effective strategy where data analysis drives decisions in ship operation and maintenance.

Nowadays, the increasing number of moving objects tracking sensors, results in the continuous flow of high-frequency and high-volume data streams. This phenomenon can especially be observed in the maritime domain since most of the vessels worldwide are now transmitting their positions periodically. Therefore, there is a strong necessity to extract meaningful information and identify mobility patterns from such tracking data in an automated fashion, eliminating the need for experts' input. To this end, a novel approach is presented in this paper, which fuses the research fields of computer vision and trajectory classification, in order to deliver a high-precision classification of mobility patterns. The experimental results demonstrate that the classification performance of the proposed approach can reach an f1-score of over 95%.

All the modern surveillance systems take advantage of the Automatic Identification System (AIS), a compulsory tracking system for many types of vessels. Ships that carry AIS transponders on board transmit their position and status in order to alert nearby vessels and ground stations, but this information can well be used to identify events of interest and support decision making. The detection of anomalies (i.e. unexpected sailing behavior) in vessels’ trajectories is such an event, which is of utmost importance. Approaches for detecting such anomalies vary from extracting normality models to searching for individual cases, such as AIS switch-off or collision avoidance maneuvers. The current research work follows the former method; it employs sparse historic AIS data and polynomial interpolation in order to extract shipping lanes. It modifies the DB-Scan clustering algorithm in order to achieve more coherent trajectory clusters, which are then composed to create the shipping lanes. The proposed approach implements distributed processing on Apache Spark in order to improve processing speed and scalability and is evaluated using real-world AIS data collected from terrestrial AIS receivers. The evaluation shows that the biggest part (i.e. more than 90%) of any future vessel trajectory falls within the extracted shipping lanes.

We present a stream reasoning system for monitoring vessel activity in large geographical areas. The system ingests a compressed vessel position stream, and performs online spatio-temporal link discovery to calculate proximity relations between vessels, and topological relations between vessel and static areas. Capitalizing on the discovered relations, a complex activity recognition engine, based on the Event Calculus, performs continuous pattern matching to detect various types of dangerous, suspicious and potentially illegal vessel activity. We evaluate the performance of the system by means of real datasets including kinematic messages from vessels, and demonstrate the effects of the highly efficient spatio-temporal link discovery on performance.