SIMULATION OF VESSEL BERTHING USING ARENA: CASE OF BEIRUT CONTAINER TERMINAL

The main objective of this research is to analyze the current situation of Beirut Container Terminal. The proposed methodology is to mimic current terminal operations using a simulation model using ARENA software in order to identify causes of queueing occurring at berth allocation. Field research was conducted and both qualitative and quantitate data were collected using interviews, on–site observations, and online vessel tracking. A base model is developed to simulate the current operations at Beirut Container Terminal. Then, three different feasible scenarios are proposed to minimize the total time spent by the vessel at the quay side. Proposed scenarios take into consideration physical and resources expansion subject to political and financial constraints. The aim of this research is to provide a tool for the decision maker at Beirut Container Terminal in formulating an investment strategy for future expansion.

Promoting Cooperation of Monitoring, Control, and Surveillance of IUU Fishing in the Asia-Pacific

Illegal, unreported, and unregulated (IUU) fishing is becoming a growing threat to sustainable fisheries and the economy worldwide. To solve this issue, various efforts on monitoring, control, and surveillance (MCS) have been made at the national, regional, and international levels. However, there is still the lack of measures against IUU fishing vessels at the multilateral level. Here, we assessed the situations of fisheries, and the current systems and challenges of MCS in eight Asia-Pacific countries with a focus on MCS of IUU fishing vessels at sea. Through a literature review and interviews, we confirmed that IUU fishing was linked with the status of fisheries in each country, and that each country implements various MCS measures with different emphases. However, there was a trend of enhancing or newly establishing four areas of MCS: vessel tracking, patrol, onboard observers, and port State measures, with amended or newly adopted laws. We also identified challenges of MCS such as insufficient MCS in coastal areas and fragmented cooperation among the countries. Based on our findings, we advance several recommendations including the enhancement of cooperation among stakeholders, especially fishers, for co-monitoring in coastal areas and the establishment of a communication platform for Asia-Pacific countries.

Detecting anchored fish aggregating devices (AFADs) and estimating use patterns from vessel tracking data in small-scale fisheries

Monitoring the use of anchored fish aggregating devices (AFADs) is essential for effective fisheries management. However, detecting the use of these devices is a significant challenge for fisheries management in Indonesia. These devices are continually deployed at large scales, due to large numbers of users and high failure rates, increasing the difficulty of monitoring AFADs. To address this challenge, tracking devices were attached to 34 handline fishing vessels in Indonesia over a month period each. Given there are an estimated 10,000–50,000 unlicensed AFADs in operation, Indonesian fishing grounds provided an ideal case study location to evaluate whether we could apply spatial modeling approaches to detect AFAD usage and fish catch success. We performed a spatial cluster analysis on tracking data to identify fishing grounds and determine whether AFADs were in use. Interviews with fishers were undertaken to validate these findings. We detected 139 possible AFADs, of which 72 were positively classified as AFADs. Our approach enabled us to estimate AFAD use and sharing by vessels, predict catches, and infer AFAD lifetimes. Key implications from our study include the potential to estimate AFAD densities and deployment rates, and thus compliance with Indonesia regulations, based on vessel tracking data.

A Deep Learning Streaming Methodology for Trajectory Classification

Due to the vast amount of available tracking sensors in recent years, high-frequency and high-volume streams of data are generated every day. The maritime domain is no different as all larger vessels are obliged to be equipped with a vessel tracking system that transmits their location periodically. Consequently, automated methodologies able to extract meaningful information from high-frequency, large volumes of vessel tracking data need to be developed. The automatic identification of vessel mobility patterns from such data in real time is of utmost importance since it can reveal abnormal or illegal vessel activities in due time. Therefore, in this work, we present a novel approach that transforms streaming vessel trajectory patterns into images and employs deep learning algorithms to accurately classify vessel activities in near real time tackling the Big Data challenges of volume and velocity. Two real-world data sets collected from terrestrial, vessel-tracking receivers were used to evaluate the proposed methodology in terms of both classification and streaming execution performance. Experimental results demonstrated that the vessel activity classification performance can reach an accuracy of over 96% while achieving sub-second latencies in streaming execution performance.

AIS Meets IoT: A Network Security Mechanism of Sustainable Marine Resource Based on Edge Computing

The sustainable utilization of marine resources is a vital issue to enrich marine life and to prevent species extinction caused by overfishing. Nowadays, it is common that commercial and smaller vessels are equipped with an Automatic Identification System (AIS) and GPS for better vessel tracking to avoid vessel collision as well as mayday calls. Additionally, governments can monitor vessels’ sea activities through AIS messages, stopping them from overfishing or tracking if any vessel has caused marine pollution. However, because AIS devices cannot guarantee data security, they are susceptible to malicious attacks such as message modification or an illegitimate identity faking a distress signal that causes other vessels to change their course. Given the above, a comprehensive network security system of a sustainable marine environment should be proposed to ensure secure communication. In this paper, a stationary IoT-enabled (Internet of Things) vessel tracking system of a sustainable marine environment is proposed. The system combines network security, edge computing, and tracking management. It offers the following functions: (1) The IoT-based vessel tracking system tracks each aquafarmer’s farming zone and issues periodic warning to prevent vessel collision for pursuing a sustainable marine environment; (2) the system can serve as a relay station that evaluates whether a vessel’s AIS data is correct; (3) the system detects abnormal behavior and any irregular information to law enforcement; (4) the system’s network security mechanism adopts a group key approach to ensure secure communication between vessels; and (5) the proposed edge computing mechanism enables the tracking system to perform message authentication and analysis, and to reduce computational burden for the remote or cloud server. Experiment results indicate that our proposed system is feasible, secure, and sustainable for the marine environment, and the tendered network security mechanism can reduce the computational burden while still ensuring security.