# Edge Computing Approach for Vessel Monitoring System
## 3 Proposed Approach  
To overcome the traditional VMS processing power limitations, we choose an algorithms' approach with low memory, i.e., CPU capacities to identify local patterns. These patterns are used to decide if the collected data should be immediately transmitted or at the port only. Our proposed system is illustrated in Figure 2: to create an edge computing approach on a local VMS box that sends alerts of possible abnormal behavior for the fishing control process to legal authorities. This emergent approach fits in this monitoring process because of the need to reduce communication costs. In this case, these edge processes reduce communication costs based on the fact that data is only transmitted if out of pattern data is registered. The creation of real-time alerts of possible non-conformity actions in the fishing activity allows legal authorities to perform targeted inspections to assess possible non-conformity actions. Since most of the data is stored locally, it is possible to increase data sampling frequency to a configurable parameter with frequency of around once every one minute (process 1), create data patterns based on historical data (process 2) and feed an alarm process based on these patterns (process 3), implemented in a decision tree process associated with configurable probability parameters. These processes together are the basis for the edge computing approach. In a more detailed way, the three processes used in this Monicap system (VMS), in which an edge computing approach is implemented, are as follows:  
1. Process 1: Creates a local database in SQL (structured query language) and sets the sampling time based on a configurable parameter. For each vessel, data is collected and stored locally on the VMS system. 2. Process 2: Implements a mining processes to identify patterns on VMS data (speed, position, date, and course direction). Taking into account the data collection process periodicity (which is a configurable parameter), it is possible to identify patterns for:  
Speed: Based on the velocity data collected, a statistical distribution is applied to group speed in discrete classes. From the usual curve we have two gaussian curves, visible in Figure 3, where the highest speed corresponds to the movement of the vessel to and from fishing zones and ports, and the lowest speed is associated with the fishing activity. The smallest gaussian velocity, with an associated probability, can indicate the type of fishing as a function of the speed. This analysis is applied to each  
Figure 2: Process implemented, taking into account the identification of real-time alerts related to fishing activity by the monitor process. vessel and, thus, has personalized information on the speed standards of fishing and can be customized based on the function of the time of the year and the fishing zone. Geographic position: There is an implementation of the K-means, heat maps, and BDscan algorithm for the identification of the fishing areas according to the time of the year and the vessel. This process comprises the implementation of the algorithm referred to in the embedded system, which allows to group data and creates a computational representation of the associated region, allowing fishing zones to be able to be represented computationally in a simple way: identification of rectangles, circles, or other polygons. These clustering algorithms were implemented based on low computer power applied. Change of direction: Taking into account changes in the course of the vessel, one can calculate the total rotation performed in a time interval and get the angular velocity. These parameters (total rotation and speed angular) allow inference or exclusion, with some degree of certainty, of the activity exerted at a certain moment by the vessel. A good example is the fishing of the siege, which is characterized by the fact that the vessel completely circumvents the shoal in a short time interval (a few minutes).