papers/lux-oracle-infrastructure/sections/09-implementation-details.tex

\section{Implementation Details}


\subsection{Smart Contract Architecture}

The oracle system consists of modular smart contracts:

\begin{lstlisting}[caption={Oracle System Architecture}]
contract OracleSystem {
    IPriceFeed public chainlinkFeed;
    IPythNetwork public pythFeed;
    ILuxAIOracle public aiOracle;
    IFastPriceFeed public fastPriceFeed;
    
    mapping(address => PriceData) public prices;
    mapping(address => OracleConfig) public configs;
    
    struct PriceData {
        uint256 price;
        uint256 timestamp;
        uint256 confidence;
        bytes32 proof;
    }
    
    struct OracleConfig {
        address[] sources;
        uint256 minSources;
        uint256 maxDeviation;
        uint256 updateInterval;
    }
    
    function updatePrice(
        address token,
        uint256[] memory sourcePrices,
        bytes[] memory proofs
    ) external onlyOracle {
        validateSources(token, sourcePrices);
        uint256 finalPrice = aggregatePrice(sourcePrices);
        
        prices[token] = PriceData({
            price: finalPrice,
            timestamp: block.timestamp,
            confidence: calculateConfidence(sourcePrices),
            proof: generateProof(token, finalPrice)
        });
        
        emit PriceUpdated(token, finalPrice);
    }
}
\end{lstlisting}

\subsection{Off-Chain Infrastructure}

Oracle nodes run specialized software for data collection and validation:

\begin{itemize}
    \item \textbf{Data Collectors}: Fetch prices from exchanges and aggregators
    \item \textbf{Validators}: Verify data integrity and apply business rules
    \item \textbf{Aggregators}: Combine multiple data sources
    \item \textbf{Publishers}: Submit validated data to blockchain
\end{itemize}

\subsection{Monitoring and Alerting}

Comprehensive monitoring system for oracle health:

\begin{lstlisting}[language=Python, caption={Oracle Monitoring System}]
class OracleMonitor:
    def __init__(self):
        self.metrics = {
            'latency': [],
            'accuracy': [],
            'uptime': 0,
            'errors': []
        }
    
    def check_health(self):
        # Monitor latency
        if self.get_p95_latency() > 2000:  # 2 seconds
            self.alert("High latency detected")
        
        # Check accuracy
        deviation = self.calculate_deviation()
        if deviation > 0.05:  # 5%
            self.alert(f"Price deviation: {deviation}")
        
        # Verify all sources active
        inactive = self.check_inactive_sources()
        if len(inactive) > 0:
            self.alert(f"Inactive sources: {inactive}")
        
        # Check for manipulation attempts
        if self.detect_manipulation():
            self.alert("Potential manipulation detected")
            self.trigger_circuit_breaker()
\end{lstlisting}