What We Did

• Designed and implemented a scalable architecture to support sensor fusion from GPS, Radar, Lidar, Hyperspectral sensors, and Computer Vision

• Developed an onboard ETL system to de-duplicate sensor data, optimize bandwidth, and enable edge processing

• Created conduits between command-and-control systems and reasoning algorithms to support autonomous, cooperative UAV operations

• Enabled federated learning models to improve decision-making in real time while securing edge communications

The Challenge

Traditional pest control methods carry risks to both people and property and are often too slow to respond to fast-moving threats. Meeting this challenge required the development of an autonomous system capable of processing massive amounts of sensor and IoT data faster than human operators. Managing multiple drones with overlapping sensor data, combined with the need for real-time decision-making, bandwidth optimization, and edge processing, called for a robust and innovative data architecture.

Lifescale Analytics’ Solution

Lifescale Analytics designed a scalable architecture that integrated diverse sensor data into a single, efficient data stream. The team developed an onboard ETL system to consume and de-duplicate data, reducing bandwidth demands and ensuring critical information reached ground control when needed. By building conduits between onboard command systems and reasoning algorithms, the solution enabled drones to communicate cooperatively and act autonomously.

The system also leveraged federated learning models, allowing UAVs to process and share only essential outputs rather than raw data—enhancing both efficiency and security during live operations.

Impact

With this architecture in place, the autonomous platform was able to:

• ​Achieve real-time decision-making in UAV operations

• Reduce communication contention through optimized edge processing

• Secure transmissions by sending mathematical outputs instead of raw sensor data

• Gather historically valuable information for future algorithm development

• Enable the successful deployment of a fully autonomous pest control system