Drone Swarm Trust Attestation Using Distributed Blockchain Consensus
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Solution Overview
Problem
Mobile computers, such as drones, operating in sensitive environments face challenges with falsified updates, malicious commands, and rogue drones joining swarms, especially when firmware updates or guidance are required without connectivity to central stations.
Innovation Solution
Implementing a method using a distributed blockchain and state machine replication protocol to assess and maintain trust among drones through a leader-driven assessment matrix, where local assessments are computed and shared to determine global trust assessments, which are then appended to a blockchain, allowing for secure and decentralized decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a distributed blockchain and state machine replication protocol are implemented for trust assessment among drones, then security against malicious drones and falsified updates is improved, but device complexity and computational overhead increase
Solution Approach 1:
The trust assessment system is segmented into multiple independent attesters that each perform local assessments of requesters. Each attester operates autonomously using the same state machine replication protocol, dividing the overall security function across multiple nodes rather than requiring a single complex centralized authority. This segmentation reduces the complexity burden on individual devices while maintaining collective security.
Solution Approach 2:
The blockchain serves as an intermediary data structure that mediates between local assessments and global trust decisions. Rather than requiring direct complex interactions between all attesters and requesters, the blockchain provides a standardized interface for storing and retrieving trust assessments, simplifying the overall system architecture while enhancing security through decentralized verification.
2Measurement precision
If local assessments are computed and shared among all attesters through the state machine replication protocol, then global trust assessment accuracy is improved, but communication overhead and time consumption increase
Solution Approach 1:
Each attester performs and stores local assessments in advance using the state machine replication protocol, before global trust assessments are needed. The blockchain maintains a history of pre-computed local assessments that can be quickly retrieved and aggregated when global trust decisions are required, eliminating the need for real-time computation during critical trust verification moments.
Solution Approach 2:
The system implements feedback loops where local assessments are continuously computed, stored in the blockchain, and used to inform subsequent global trust assessments. This feedback mechanism allows the system to learn from past assessments and improve future trust decisions, increasing accuracy over time while the pre-stored nature of the feedback data minimizes time delays.
3Reliability
If a permissioned blockchain is used with authenticated members, then trust and security among drones are improved, but adaptability to dynamic joining and leaving of drones is reduced
Solution Approach 1:
The permissioned blockchain is designed with dynamic membership capabilities, allowing drones to authenticate and join the network dynamically while maintaining security. The state machine replication protocol supports dynamic addition and removal of attesters and requesters from the permissioned list, enabling the system to adapt to changing operational conditions while preserving the trust guarantees of permissioned access.
Solution Approach 2:
The system allows parameters such as the permissioned member list and authentication credentials to be dynamically updated through the blockchain protocol. When new drones join or existing ones leave, the permissioned membership parameters are modified through consensus among attesters, maintaining security while enabling adaptability to dynamic operational requirements.
Data Source
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AI summary
Method, apparatus, and computer program are disclosed for operating drones or other mobile computers in sensitive environments, where the drones are exposed to attacks with updates, incorrect/malicious commands or even being joined by malicious drones pretending to be part of the drone swarm or group. The method includes leveraging both trusted computing capabilities and that each individual drone can decide on the level of assurance and trust of any other drone we can construct a consensus algorithm such that if a drone wishes to perform a sensitive operation then all drones can attest that drone and decided on its level of assurance.