As enterprises develop their drone strategies, they can take a cue from the military.
Good news: The unmanned aerial vehicle (UAV) or drone space keeps evolving.
Bad news: Companies hoping to use UAV technology--and the big data intelligence it collects--are limited in the project scope due to regulatory restrictions that require drone flights to be within a line of sight.
SEE: Drone policy (Tech Pro Research)
More good news: This provides time for CIOs to articulate a clear business strategy for drone usage and drone-generated big data in business operations.
And there are plenty of drone-data capture applications. Drone site surveys and data collection at remote sites are options for the construction, utility, oil and gas, real estate and mining industries. Express delivery of goods by unmanned aerial vehicle is an option for retailers like Amazon. In environmental use cases, UAVs can be used for weather observation, forest fire fighting, climate change monitoring, and disaster recovery operations.
The military is already aware of UAV's potential to fly missions and collect and transmit valuable information. It is making significant investments in UAV technology and putting those investments to use in the field. While CIOs design their own UAV and big data strategies, they can benefit from what the military learned from their own drone deployments.
Drone best practices
Below are six best practices that UAV military deployments have revealed.
1. Have purposeful objectives
When the military began its investigation of drone and big data collection, it targeted objectives such as troop safety, military reconnaissance, and infiltrating dangerous areas that otherwise were inaccessible. Strategy architects knew precisely, which types of operations to deploy with drones, and how these operations would complement other military operations.
2. Demand interoperability
The IoT and drone industries are notorious for producing proprietary operating systems that don't always mesh well with alternate brands or legacy technology. Like commercial companies, the military made significant investments in legacy systems that worked well and they had no intention of ripping and replacing it. In fact, a standard demand in requests for proposals (RFPs) was that any new drone or big data technology had to coexist and work with legacy systems. Your RFPs should, too.
3. Determine your AI
Military big data architects and data science engineers had to understand the type of AI they were using in order to design the right sequences of data processing. For example, military drone missions use artificial intelligence (AI) to autopilot drones and assist with collecting and processing critical data. While data is collected in-flight, drones use machine learning (ML) and deep learning algorithms to identify both mission-critical data elements and data elements to discard. ML uses algorithms to parse data, make deductions from it, and execute decisions based upon what is learned. Deep learning (DL) steps in whenever an inaccurate machine learning result is returned and uses a neural network to assess what information was missed and what assumptions were incorrect in an initial decision.
CIOs must ensure that their data scientists and architects can do the same.
SEE: Machine automation policy guidelines (Tech Pro Research)
4. Smaller is better
Battery life remains an issue with drones, so the lighter the drone (and the drone payload), the better. Smaller drones are also easier for individuals to operate.
5. The man-machine interface is crucial
The military used drones in fully autonomous operations and in exercises where autonomous drones flew alongside manned jets to exchange information and coordination on a mission. A great deal of work went into perfecting a man-machine interface that enabled both human-piloted jets and unmanned drones to work together as a team.
6. Plan for redundant operational backup
There is no room for failure in military operations. The military equipped Drones with sensors for data collection, and in some cases also used LiDAR as a backup technology in the event of sensor failure. Drones in a mission flight formation could also self-heal, so if a drone was lost, the remaining drones could cross-communicate and reform into a new formation to continue the mission.
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