A new US Army drone helicopter takes flight to capture six petabytes of HD video
The vast majority of Unmanned Aerial Vehicles that exist are used for military purposes. In spring 2012, the US Army is issuing three new unmanned aerial drones for surveillance operations in Afghanistan. Unlike the Predator-series drones used in various surveillance and combat operations around the world, the US Army will deploy the A-160 Hummingbird, a 35-foot (10.6m) unmanned helicopter with a range of approximately 2,250 nautical miles. According to a US Army press release, the aircraft will deploy for one full year in Afghanistan beginning in May or June 2012.
This particular helicopter drone is special; it is fitted with a 1.8 gigapixel camera, much larger than any others currently flying. The new system is called the Autonomous Real-time Ground Ubiquitous Surveillance Imaging System (ARGUS) and is named after Argos Panoptes, a Greek mythological giant with hundreds of eyes covering his entire body. This system was developed by DARPA and was originally meant for the US Special Forces, but now the system will be integrated with the A-160 Hummingbird.
Why the Hummingbird? In a statement(.pdf) to Congress by Dr. Robert Leheny, the acting director of DARPA said:
[The] A160 program has been developing an unmanned helicopter for intelligence, surveillance, and reconnaissance (ISR) missions, with long endurance – up to 20 hours – and the ability to hover at high altitudes. In 2008, the A160 set a world record for UAV endurance when it completed an 18.7-hour endurance flight, carrying a 300-pound payload, much of the time at 15,000 feet. The A160 will eventually fly at speeds up to 165 knots with a ceiling of 20,000 to 30,000 feet altitude for more than 20 hours, and a high hover capability of up to 15,000 feet altitude. The altitude and endurance of this UAV, combined with the ability to hover at altitude and take off and land vertically with a significant payload, will give our military a set of capabilities not currently found in any other operational aircraft.
But what is truly exceptional about this drone is its fusion with ARGUS. According to Wired, ARGUS consists of “92 five-megapixel imager” and “covers up to 36 square miles, depending on the quality of the resolution.” It will “give its remote pilots at least 65 independent, scalable video windows [in a single snapshot].” With the ARGUS technology attached to the A-160, the Hummingbird will be able to collect “six petabytes of video — the equivalent of 79.8 years’ worth of HD video.”
How much is a petabyte of data? One petabyte is approximately 20 million four-drawer filing cabinets filled with text, or 13.3 years of HD video. If you need to store Facebook’s 10 billion photos, you’ll need about 1.5 petabytes of hard drive space. Google processes about 20 petabytes worth of data each day, but you’ll need 50 petabytes if you want to store every single written work of mankind in every language since the beginning of recorded history.
Clearly six petabytes doesn’t seem too difficult to manage, but that is still an enormous amount of data for the US military to handle, and it’s not the first time Big Data geeks have wondered if innovations in Big Data management can help solve the US military’s increasing data glut.
Before drones became immensely popular with the Pentagon, there were just a few dozen surveillance missions flying over Iraq and Afghanistan in the few years after those respective invasions. Now, however, drones fly hundreds of missions monthly and dump massive waves of data back down to ground forces. The US military is going to have to organize its data management structure somewhere between that of Facebook and Google if it wants to have even a remote shot at exploiting all that data.
Lt. Gen. David A. Deptula (Ret.), the Deputy Chief of Staff for Intelligence, Surveillance and Reconnaissance, commented to PopSci concerning the data whirlpool the US military finds itself in, “The unavoidable truths are that data velocities are accelerating and the current way we handle data is really overwhelmed by this tsunami […] So we’re going to have to begin exploring different ways to meet the growing challenges of hyper-scale workloads.” The article noted that:
Simply growing the military’s Rackspace—and that’s been much of the strategy for dealing with the problem to date—isn’t going to tame the flood. The DoD doesn’t just need new storage methods, but completely new concepts of operation that blend novel storage architectures, all kinds of digital semantics, and—critically—a healthy dose of artificial intelligence.
And while some AI methods may prove useful if properly implemented, the Department of Defense still does not have a solid Big Data management plan. The mentality that forms the United States’ global military might—bigger, better, faster, stronger—is the same when it comes to methods used to store, manage, and analyze massive streams of surveillance date; they just occupy more space, with faster processors, and a decentralized method to extract various blocs of information.
Beyond problems of storage space, Mav6, an aerospace and defense technology company which has launched various innovations and solutions for the Department of Defense to help solve some of their bigger issues identified the underlying problem with the US military’s data fat:
The ‘big data problem’ actually has less to do with data storage than it does with transporting the data, that is, moving data from the edge (where it’s collected) to the core (where it’s stored). It may be within the realm of the possible to save all of the data, but it’s not possible to move all of that data around. This realization has led the community to consider approaches that aggregate metadata (i.e. data that describes the underlying data sets). Such approaches provide a valuable window into the distributed data inventory but fail to address the problem of leveraging the aggregate data to produce information.
For an organization the size of the US military, this would mean an attempt to save everything collected interdepartmentally and across the vast defense establishment, as well as all the redundant data that is shared between various military sectors and commands. While it may be possible, centralizing such a vast sea of data seems problematic on a massive scale, and on some levels, operationally risky.
Jay Harrison of Mav6 notes:
[One] smarter solution is to process data at the edge to derive feature vectors that describe the information contained in the data. More processing (and not just static data storage) at the edge supports rapid indexing, correlation, and fusion of data to establish the rich contextual relationships between data sets along with the spatial, temporal, phenomenological derivatives that capture the underlying dynamics of the data. Rather than “storing everything,” such an approach enables the community to “exploit everything” and store only what is needed.
But the challenges do not end there. If the US military pulled this off, they would then need to bring the analysis back to its forces, looping in the human element. As PopSci argues, “The issue then is finding the right balance between machine reliability and human decision-making so that the armed services and intelligence community can get the most out of both.”