The increasing complexity of autonomous systems and the exponential rise in security vulnerabilities require robust, innovative solutions. GENZERO aims to leverage Generative AI (GenAI) and Large Language Models (LLM) to address critical challenges in edge device deployment, threat intelligence integration, and incident response and recovery.

Our system is designed with a hierarchical architecture to ensure robust security and efficient data management. On the edge, we have devices that include UAVs (Unmanned Aerial Vehicles), UGVs (Unmanned Ground Vehicles), or even individuals equipped with communication devices. These edge devices are the frontline of our autonomous systems, operating in various environments and conditions.

On the higher end of the edge architecture, we have Fog drones. These devices possess higher resource capabilities and act as data aggregators for the edge devices. Each edge device performs sensor and information fusion individually, processing data such as health status and detecting potential attacks. However, at the fog level, data fusion occurs at the individual device level and swarm level, aggregating and analyzing information from multiple devices to provide a comprehensive overview.

This hierarchical fusion allows us to manage and interpret data effectively, ensuring that individual and collective insights are leveraged to enhance the overall system performance and security. We monitor diverse inputs, including the operational health of devices, security threats, and attacks at both the device and swarm levels.

GENZERO invites participants interested in advancing the capabilities of Generative AI (GenAI) and Large Language Models (LLMs) across five pivotal challenges. With a significant allocation of up to $1 million in funding per challenge over two years, this initiative offers an unparalleled opportunity to spearhead groundbreaking research and foster innovative breakthroughs. The aim is to develop and validate TRL4 Proof of Concept across diverse challenge areas.

Overview: Develop and validate a TRL4 Proof of Concept for a resilient, lightweight GenAI/LLM framework specifically designed for hierarchical drone swarms. This initiative is aimed at enabling robust, real-time AI inference on edge devices, with a strong emphasis on securing operations and enhancing the adaptability of drone responses under diverse operational conditions.

Key Objectives:

  • Engineer GenAI/LLM models that prioritize operational security and data integrity on drones.
  • Strengthen AI systems against vulnerabilities during live operations.
  • Enhance the resilience of drone operations to adapt dynamically across varied mission types.
  • Seamlessly integrate with existing systems for unified flight and mission management, focusing on robust fail-safe mechanisms.

Demonstration Scenario:

Execute a mission where edge drones make real-time decisions, with fog drones aggregating and processing data for enhanced situational awareness.

Overview: Develop a sophisticated AI-driven threat detection and response system for hierarchical drone swarms that can perform real-time threat data aggregation and analysis, enabling coordinated, swarm-wide proactive and reactive security measures.

Key Objectives:

  • Establish AI algorithms dedicated to the early detection and mitigation of threats, enhancing swarm security.
  • Develop coordinated defense mechanisms across the swarm, enabling a synchronized response to real and simulated threats.
  • Craft systems capable of identifying both known and emerging threat patterns, ensuring continuous adaptation and learning.
  • Integrate robust security protocols into the flight and mission operations platforms, ensuring a secure communication framework.

Demonstration Scenario:

Simulate a security breach and watch as the system autonomously detects the threat, coordinates a response, and adapts mission parameters.

Overview: Design a TRL4 Proof of Concept for a continual learning system that equips GenAI/LLM models within drone swarms to quickly adapt to new environments and threats, enhancing both the operational resilience and safety of the autonomous systems.

Key Objectives:

  • Implement and update models directly on drones to foster adaptive learning during missions, focusing on secure and safe data handling.
  • Enable federated learning that emphasizes resilience, allowing drones to learn collaboratively while maintaining data privacy and security.
  • Tailor AI responses to dynamic operational scenarios, maintaining high performance and safety standards.
  • Ensure integration with mission operations platforms is secure, reliable, and continuously monitored for threats and vulnerabilities.

Demonstration Scenario:

Conduct a multi-phase mission where the drone swarm encounters new challenges, adapts its behavior, and enhances its operational efficiency over time.

Overview: Develop a comprehensive communication and coordination framework that ensures secure, reliable, and efficient operations for hierarchical drone swarms, incorporating advanced protections against cyber threats like hacking, jamming, and spoofing.

Key Objectives:

  • Deploy advanced encryption and authentication techniques to safeguard drone communications.
  • Utilize AI to optimize communication strategies and enhance resilience against environmental and malicious disruptions.
  • Implement anomaly detection algorithms to quickly identify and counteract communication threats in real time.
  • Establish proactive defenses to maintain integrity and reliability of communications under adversarial conditions.

Demonstration Scenario:

Execute a coordinated mission where drones communicate in real-time to adapt to changing conditions and complete tasks efficiently while maintaining secure communication channels.

Overview: Create an AI-driven interface that facilitates effective human oversight and collaboration with drone swarms, significantly enhancing operational resilience, decision-making capabilities, and safety during complex missions.

Key Objectives:

  • Develop explainable AI to enhance transparency and trust in AI decisions, promoting safer human-swarm interactions.
  • Incorporate human-in-the-loop approaches to reinforce learning and decision-making processes, emphasizing safety and precision.
  • Build a natural language interface to improve communication efficiency between human operators and autonomous swarms, ensuring clear and secure interactions.
  • Ensure all systems integrate seamlessly with operational platforms, maintaining high security and resilience standards.

Demonstration Scenario:

Perform a complex mission requiring human intervention, highlighting how the interface improves decision-making and mission outcomes.

Overview: Develop a comprehensive Zero Trust platform incorporating SoC (System on Chip), system, and communication architecture to support the deployment and operation of Generative AI and Large Language Models from the cloud to the edge continuum.

Key Objectives:

  • Design and implement a secure, scalable Zero Trust platform that ensures end-to-end security from the cloud to edge devices.
  • Integrate advanced encryption and authentication techniques to safeguard communications and data integrity.
  • Develop a robust communication architecture that supports seamless and secure AI operations across different layers of the system hierarchy.
  • Ensure that the platform can efficiently handle the deployment and execution of GenAI/LLM models, optimizing resource use and maintaining high performance and security standards.

Demonstration Scenario:

Implement and demonstrate a use case where GenAI/LLM models are securely deployed and operated across cloud and edge devices, showcasing secure data transmission, processing, and decision-making in a simulated mission environment.

Additional Implementation Notes

  1. Hardware and Software Integration: All solutions should be designed to integrate seamlessly with the hierarchical drone swarm hardware provided and the unified flight and mission operations software.
  2. Scalability: Demonstrations should showcase how the solutions perform across different levels of the swarm hierarchy, from individual edge drones to fog drones and the overall swarm.
  3. Real-World Conditions: While operating at TRL4, the demonstrations should simulate real-world conditions as closely as possible within safety and regulatory constraints.
  4. Data Collection and Analysis: Implement mechanisms to collect performance data during demonstrations for post-mission analysis and validation of key objectives.
  5. Safety Measures: Ensure all demonstrations include appropriate safety measures and fail-safes, given the experimental nature of the technologies being tested.

Why Participate?

This workshop is not just an event; it’s a launchpad for innovation. By participating, one will:

  • Be at the forefront of autonomous drone technology.
  • Access up to $1M in funding for each challenge over two years, based on the proposals.
  • Collaborate with leading experts and gain access to advanced hardware and software stacks.
  • Contribute to cutting-edge advancements with real-world impact.