Drones and IoT, Cloud Computing, Big Data, and AI

 As a formal introduction to UAS, it's important to understand how they work, and how the industry is actively changing. We are going to break down and look at drones from the perspective of IoT, Cloud Computing, Big Data, and AI.

IoT - Internet of Things:

    Internet of things describes physical objects that are embedded with sensors, processing ability, software, and other technologies that connect and exchange data with other devices over the internet. UAS platforms are perfect examples of IoT because not only can they capture detailed aerial photographs, but they can also collect enough data to create a complete map of the worksite with proper coordinate systems and ground control points. These can either be made in 2D or 3D which allow for accurate measurements of distances, surfaces, and elevation. A great example of this is a construction site; before you can start working on the site, a topographic survey must be completed. This can now be done as the drone data can generate this and show possible drainage points, changes in elevation, and find the best location for the different tasks on the site.

    Another example of how drones are used with IoT is Bathymetry, the study of underwater depths in oceans and lake floors To measure water depth, they must use echo sounders which transmit sonar waves into the water to determine the depth. When the sonar waves bounce back, they can measure the data to find out the depth. There are times where the seaweed is too thick or solid rock debris where it’s not feasible to lower an echo sounder into the water. An echo sounder can be added to a drone to use its own IoT system along with the echo sounder to measure the sound waves that bounce off the bottom of the water to measure its depth.

Cloud Computing:

    Cloud Computing is the delivery of different services on the internet. This includes data storage, servers, databases, networking, and software. Initially, drones relied on a middleman to land the UAV and then transfer the data from the device to the cloud. Cloud computing is going to have a large impact on the drone industry and the way we process data. In the future, I believe that data will be transferred straight from the drone to the cloud over 5G. Once you finish your flight, you just upload your data for cloud processing and mark ground control points. This allows you to track changes, measure volumes, and perform much more analysis tasks. 

    An example of cloud computing in the UAS industry is DroneDeploy. DroneDeploy is a mapping software that can be used by both beginners and advanced users. It allows you to do several types of in-field processing before you even get to the office. The software’s cloud operations make it easy to manage for team members or clients. They were flying new sites at such as fast pace that they couldn’t keep up as a growing business. They began evaluating several cloud-based platforms to find a new image processing software solution. After they developed it, they received faster, higher quality data, with a much shorter turnaround time.

Big Data:

    Big data is a field that treats ways to analyze and systematically extract information from data sets that are too large or complex to be dealt with traditional data processing methods. Big data can be used in the UAS industry by allowing intelligent data analysis and recognition in real-time. Demand for big data for commercial uses, technological advancements, and increased venture capital funding will continue to drive rapid growth in drone use. Agriculture, real estate, construction, and highway safety are some of the industries analyzing this data. While drones are valued for the images and video footage they collect, they are increasingly able to store other types of information, including radio signals, soil moisture, factory emissions, and geodetic data, which includes precision measurements for land surveys. 

    An example of Big Data in UAS is a company called Airdata UAV, and they are a data processing company. Your flights and data are automatically collected then processed to track your flights. You fly a mission just like normal, then upload your data either manually or it’ll do it automatically, then you will get immediate access to your flight, aircraft, battery health, and even manage maintenance reports. This will help you to identify early problems, get notified of any problems, or generate reports.

AI - Artificial Intelligence: 

    Artificial Intelligence uses computers to mimic problem-solving capabilities of the human mind. This allows for machines to operate without human interaction. AI applications in the drone industry are gaining importance as automated flights are becoming more common. The next phase for AI in the drone industry is using it to fully automate missions, process the data from it, and learn from that data to be smarter in the future. AI drones rely on computer vision to detect objects while flying and then allow for data to be processed and recorded on the ground. AI allows for the data to stream in real-time and is processed while flying. That means the drone can automatically make corrections to flight operations in the air. 

    An example of Artificial Intelligence with UAS is a company called Drone Volt. Drone Volt offers solutions based on the drone, computer vision, and neural networks; for example, object detection, crowd counting, thermal detection, and many more. The application has real-time video processing and has cloud-based solutions as well. The system is fed by satellite data and intel collected by the ground unit. With the use of Artificial Intelligence, it can continue its mission through a variety of obsticals and environments.