One of the fundamentals in parallel processing in computer science involves the separation of tasks per worker to reduce contention. When you treat the worker as an autonomous drone with minimal co-ordination with other members of its fleet, an independent task might appear something like installing a set of solar panels in an industry with 239 GW estimate in 2023 for the global solar powered renewable energy. That estimate was a 45% increase over the previous year. As industry expands, drones are employed for their speed. Drones  aid in every stage of a plant’s lifecycle from planning to maintenance. They can assist in topographic surveys, during planning, monitor construction progress, conduct commissioning inspections, and perform routine asset inspections for operations and maintenance. Drone data collection is not only comprehensive and expedited but also accurate.

During planning for solar panels, drones can conduct aerial surveys to assess topography, suitability, and potential obstacles, create accurate 3D maps to aid in designing and optimizing solar farm layouts, and analyze shading patterns to optimize panel placement and maximize energy production. During construction, drones provide visual updates on construction progress, and track and manage inventory of equipment, tools, and materials on-site. During maintenance, drones can perform close-up inspections of solar panels to identify defects, damage, or dirt buildup, monitor equipment for wear and tear, detect hot spots in panels with thermal imaging, identify and manage vegetation growth that might reduce the efficiency of solar panels and enhance security by patrolling the perimeter and alerting to unauthorized access.

When drones become autonomous, these activities go to the next level. The dependency on human pilots has always been a limitation on the frequency of flights. On the other hand, autonomous drones boost efficiency, shorten fault detection times, and optimize outcomes during O&M site visits. Finally, they help to increase the power output yield of solar farms. The sophistication of the drones in terms of hardware and software increases from remote-controlled drones to autonomous drones. Field engineers might suggest selection of an appropriate drone as well as the position of docking stations, payload such as thermal camera and capabilities. A drone data platform that seamlessly facilitates data capture, ensures safe flight operations with minimal human intervention,  prioritize data security and meet compliance requirements becomes essential at this stage. Finally, this platform must also support integration with third-party data processing and analytics applications and reporting stacks that publish various charts and graphs. As usual, a separation between data processing and data analytics helps just as much as a unified layer for programmability and user interaction with API, SDK, UI and CLI. While the platform can be sold separately as a product, leveraging a cloud-based SaaS service reduces the cost on the edge.

There is still another improvement possible over this with the formation of dynamic squadrons, consensus protocol and distributed processing with hash stores. While there are existing applications that serve to improve IoT data streaming at the edges and cloud processing via stream stores and analytics with the simplicity of SQL based querying and programmability, a cloud service that installs and operates a deployment stamp with a solution accelerator and as a citizen resource of a public cloud helps bring the best practices of storage engineering, data engineering and enabling businesses to be more focused.