By David Hemphill, Chief Technology Officer, ObjectFX
Published in GeoPlace August 2009
Because of their proximity to urban centers and other critical infrastructure, ports make attractive terrorist targets due to the fact that a successful attack would result in significant loss of life, structural damage and economic impact.
Protecting our ports requires constant vigilance by the Department of Homeland Security (DHS) and marine enforcement officers. We rely on the U.S. Coast Guard (USCG) and others to ensure our safety and protect our nation’s critical infrastructure.
Since Sept. 11, 2001, however, mission priorities of the USCG have shifted significantly, as critical new challenges made their way to the forefront. Traditionally, mission priorities focused on search-and-rescue operations, combating drug smuggling and preventing illegal immigration.
Dynamic Cluster Detection determines when a user-specified number of vessels are within a certain proximity at the same time.
After the terrorist attack in 2001, priorities shifted to homeland security and maritime domain awareness. Because terrorism is a constant threat, and resources are becoming more limited, new technologies are vital to enable port-security officials to make the most efficient use of existing resources to support this mission.
A Complex Challenge
Protecting U.S. borders is a daunting task, and marine enforcement officers are faced with a unique set of challenges at each individual port. Adding an extra layer to the challenge is the fact that ports weren’t designed to be secure; port systems were designed for efficiency, reliability and speed. Security was seldom an important factor.
Because no two ports are alike, a tailored security solution needs to be implemented at each and every port. Because most cargo handled within U.S. ports is immediately loaded onto railcars and truck beds, leaving the nation’s rail and highway systems directly vulnerable, enhanced security at each individual port is crucial.
A spatiotemporal rules engine filters a stream of real-time sensor-based data, identifying actionable intelligence.
Intelligence information currently is being used as an efficient means of increasing security. However, this information is gathered by an expanding number of land-based and airborne sensor platforms, providing a deluge of information that’s unmanageable without an effective way to make sense of the information and extract what’s important—identifying actionable intelligence.
In addition to the challenges created by the structure of the ports themselves, government agencies are faced with shrinking budgets, limited resources and an overwhelming amount of sensor-based information that must be sifted through to find relevant data.
The goal of sensor-based information is to identify actionable intelligence—getting critical information to the right people at the right time to best protect our nation’s ports—but there’s too much information to sift through. As technology progresses and people become more sophisticated in data collection, more sensors are used to secure ports, providing more information to sift through to find the valuable data.
Airborne sensors and land-based cameras only provide relevant information when they’re capturing the correct information—cameras may not always be pointed at something of interest, while airborne sensors can’t be everywhere at once. To efficiently use sensor and camera resources, tipping and cueing information is critical to determine where they should be tasked.
Although the number of sensors returning a steady stream of raw, unfiltered information continues to grow, the number of false-positives delivered also grows. As a result, analysts and port-security personnel spend unnecessary time and resources investigating and verifying false or unqualified leads.
With millions of individual containers entering U.S. ports every year, it’s almost impossible for marine enforcement officers to board and inspect every ship. Resources permitting, it would be expensive and highly inefficient to search every container, ultimately increasing the cost of many products purchased by Americans.
A spatiotemporal rules engine can track vessel-routing information, determining when two vessels are within a given proximity of each other within a specified amount of time. Using a “breadcrumb trail,” two vessels need not be in the same place at the same time to alert port-security officials.
Although a 2006 Congress mandate requires that DHS must scan 100 percent of cargo coming into U.S. ports by 2012, DHS has set forth a proposal that doesn’t incorporate a search of every container, but will focus on gathering more information about countries of origin and packaging of containers. To most efficiently use such information, the government needed a solution to filter through the floods of sensor-based data, which would enable port security to “red flag” any potential problem areas to narrow the list of targets, instead of wasting resources inspecting each individual container.
New technologies, such as a spatiotemporal rules engine, can address these challenges, saving time, money and increasing the opportunity for success. A spatiotemporal rules engine allows existing sensors and databases to condense the deluge of information into a single stream before applying customized rule layers to identify actionable intelligence.
As the name suggests, the technology uses spatial and temporal information, intelligently managing streams of real-time information to track moving objects or real-time events, triggering notifications based on an unlimited number of user-defined rules. These rules engines are compatible with any number of location-aware devices and sensors, displaying output within a designated geospatial viewer.
Although data fusion is a crucial added value from such technology, there are other, equally important capabilities enabled by a spatiotemporal rules engine. For example, ship monitoring and tracking is critical in determining whether certain vessels deviate from planned routes. It also can help pinpoint a possible cause for deviation.
Without a spatiotemporal rules engine, sensor data can be overwhelming and difficult to sift through.
If a vessel deviates, an Automatic Identification System signal from that ship can be used to track locations, enabling marine enforcement officers to determine whether that variance was due to an environmental situation, such as severe weather, or for questionable motives. If the cause of deviation is determined to be questionable, port security can flag that vessel, indicating a need for ship interception and inspection.
Defining Actionable Intelligence
Ship monitoring can be enabled by user-customized rule layers that are defined and applied based on needed information and information being tracked. Routing information, travel patterns and cluster detection all are determined by the application of rule layers, providing port security analysts with the information needed to target high-interest vessels.
After being applied to a spatiotemporal rules engine, sensor data can enable port security officials to more efficiently track high-interest vessels and other important information.
Vessels arriving from certain ports or carrying certain types of material or personnel included on a given “watch list” may be marked as “high-interest vessels.” Rules applied to track routing information can focus on specific points of origin or stops along the way; ships that originate from ports at higher-risk countries or ships that stop at those ports along the way are more closely watched, narrowing the list of vessels that need to be monitored.
The “breadcrumb trail” is another method of tracking travel patterns that’s used to narrow the list of suspect vessels. Using this method, travel patterns of individual vessels can be tracked and compared to travel patterns of other vessels, especially those of high interest. Using this rule layer, the paths of the two vessels need not cross simultaneously—a spatiotemporal rules engine can use the function to determine whether two ships were in the same vicinity within a given amount of time, enabling the transfer of illegal contraband (e.g., arms, people, drugs, potential weapons of mass destruction, etc.) to an otherwise inconspicuous ship.
Dynamic Cluster Detection is another method used to monitor travel patterns of high-interest vessels that may be transferring illegal contraband onto other ships. The number of vessels that make up a cluster is customized by users and applied. After a given number of ships are within a given proximity at the same time, an incident alert is distributed, marking those vessels to be searched and monitored.
Spatiotemporal rules engines have proven to be especially useful when it comes to data fusion, ship monitoring and tracking, and incident response. Spatiotemporal rules engines also can help port-security officials with other critical, yet more traditional challenges, such as search-and-rescue missions and illegal immigration. Although large vessels tend to be the focus for such rules engines, they also have the ability to track foreign fishing boats and other small vessels that may have illegally entered U.S. territory to export immigrants or illegally fish within a U.S. commerce zone.
As the threat of terrorism at vulnerable U.S. ports continues to expand, and increased mission requirements are being levied on U.S. port-security officials, the use of sensors and new technology, such as a spatiotemporal rules engine, helps the government do more with less, better protecting the nation’s critical infrastructure by focusing on its ports.