edge computing

# Edge AI inference optimization for real-time control
import tensorrt as trt
import pycuda.driver as cuda
import numpy as np

class EdgeAIController:
    def __init__(self, model_path):
        # Load TensorRT optimized model for edge inference
        self.engine = self.load_tensorrt_engine(model_path)
        self.context = self.engine.create_execution_context()
        self.stream = cuda.Stream()
        
    def real_time_inference(self, camera_input):
        # Preprocess image data
        processed_input = self.preprocess_image(camera_input)
        
        # GPU inference with minimal latency
        output = self.engine_inference(processed_input)
        
        # Convert to control commands
        steering, throttle = self.interpret_output(output)
        return steering, throttle
    
    def preprocess_image(self, image):
        # Optimized preprocessing for edge device
        resized = cv2.resize(image, (224, 224))
        normalized = resized.astype(np.float32) / 255.0
        return np.expand_dims(normalized, axis=0)

# Edge AI for infrastructure inspection
class EdgeInspectionAI:
    def __init__(self):
        self.defect_detector = self.load_optimized_model()
        self.ar_processor = AROverlayEngine()
        self.edge_cache = EdgeCacheManager()
        
    def analyze_structure_realtime(self, camera_feed):
        # Real-time defect detection on edge device
        defects = self.defect_detector.detect(camera_feed)
        
        # Generate AR overlays for immediate feedback
        ar_annotations = self.ar_processor.create_overlays(defects)
        
        # Cache results for offline analysis
        self.edge_cache.store_analysis(defects, ar_annotations)
        
        return {
            'defects': defects,
            'ar_overlays': ar_annotations,
            'confidence_scores': self.calculate_confidence(defects)
        }

# TensorRT optimization for edge deployment
import tensorrt as trt

class ModelOptimizer:
    def __init__(self):
        self.logger = trt.Logger(trt.Logger.WARNING)
        self.builder = trt.Builder(self.logger)
        
    def optimize_for_edge(self, onnx_model_path, precision='FP16'):
        # Create optimization profile
        config = self.builder.create_builder_config()
        config.max_workspace_size = 1 << 30  # 1GB
        
        # Enable precision optimization
        if precision == 'FP16':
            config.set_flag(trt.BuilderFlag.FP16)
        elif precision == 'INT8':
            config.set_flag(trt.BuilderFlag.INT8)
            
        # Build optimized engine
        network = self.builder.create_network()
        parser = trt.OnnxParser(network, self.logger)
        
        with open(onnx_model_path, 'rb') as model:
            parser.parse(model.read())
            
        engine = self.builder.build_engine(network, config)
        return engine

# Edge device performance monitoring
class EdgePerformanceMonitor:
    def __init__(self):
        self.metrics_collector = MetricsCollector()
        self.alert_system = AlertManager()
        
    def monitor_inference_performance(self):
        metrics = {
            'inference_time': self.measure_inference_latency(),
            'memory_usage': self.get_memory_consumption(),
            'gpu_utilization': self.get_gpu_stats(),
            'temperature': self.get_thermal_status(),
            'power_consumption': self.get_power_draw()
        }
        
        # Check performance thresholds
        if metrics['inference_time'] > 100:  # ms
            self.alert_system.trigger_latency_alert()
            
        if metrics['temperature'] > 75:  # Celsius
            self.alert_system.trigger_thermal_alert()
            
        return metrics

# Federated learning for distributed edge devices
class EdgeFederatedLearning:
    def __init__(self, device_id):
        self.device_id = device_id
        self.local_model = self.initialize_local_model()
        self.federation_client = FederationClient()
        
    def local_training_cycle(self, local_data):
        # Train on local edge device data
        self.local_model.fit(local_data, epochs=1)
        
        # Extract model weights
        local_weights = self.local_model.get_weights()
        
        # Send to federation server
        self.federation_client.send_weights(local_weights)
        
        # Receive global model update
        global_weights = self.federation_client.receive_global_weights()
        self.local_model.set_weights(global_weights)
        
        return self.evaluate_local_performance()

# Hybrid edge-cloud processing strategy
class HybridProcessingManager:
    def __init__(self):
        self.edge_processor = EdgeAIProcessor()
        self.cloud_client = CloudAPIClient()
        self.decision_engine = ProcessingDecisionEngine()
        
    def process_request(self, data, urgency_level):
        # Decide processing location based on requirements
        processing_strategy = self.decision_engine.choose_strategy(
            data_size=len(data),
            urgency=urgency_level,
            network_quality=self.get_network_status(),
            edge_load=self.edge_processor.get_current_load()
        )
        
        if processing_strategy == 'edge':
            return self.edge_processor.process(data)
        elif processing_strategy == 'cloud':
            return self.cloud_client.process_async(data)
        else:  # hybrid
            return self.hybrid_process(data)

# Privacy-preserving edge AI processing
class PrivacyPreservingEdgeAI:
    def __init__(self):
        self.differential_privacy = DifferentialPrivacyEngine()
        self.homomorphic_encryption = HomomorphicEncryption()
        self.local_anonymizer = DataAnonymizer()
        
    def secure_inference(self, sensitive_data):
        # Anonymize data locally
        anonymized_data = self.local_anonymizer.anonymize(sensitive_data)
        
        # Add differential privacy noise
        private_data = self.differential_privacy.add_noise(anonymized_data)
        
        # Perform inference on privacy-protected data
        result = self.ai_model.predict(private_data)
        
        # Return results without exposing raw data
        return self.sanitize_output(result)

# Edge AI processing for LoRaWAN sensor data
class LoRaEdgeAI:
    def __init__(self):
        self.anomaly_detector = EdgeAnomalyDetector()
        self.prediction_model = EdgePredictionModel()
        self.lora_gateway = LoRaGateway()
        
    def process_sensor_data(self, lora_packet):
        # Parse sensor data from LoRa packet
        sensor_data = self.parse_lora_payload(lora_packet)
        
        # Real-time anomaly detection on edge
        anomalies = self.anomaly_detector.detect(sensor_data)
        
        # Predictive analytics for maintenance
        predictions = self.prediction_model.predict(sensor_data)
        
        # Immediate response for critical conditions
        if anomalies['severity'] > 0.8:
            self.trigger_immediate_response(anomalies)
            
        return {
            'processed_data': sensor_data,
            'anomalies': anomalies,
            'predictions': predictions,
            'response_time': self.get_processing_time()
        }

# Edge AI for digital twin real-time updates
class EdgeDigitalTwin:
    def __init__(self):
        self.twin_model = DigitalTwinModel()
        self.uncertainty_quantifier = UncertaintyQuantification()
        self.edge_optimizer = EdgeOptimizer()
        
    def update_twin_realtime(self, sensor_telemetry):
        # Real-time state estimation on edge
        current_state = self.twin_model.update_state(sensor_telemetry)
        
        # Quantify uncertainty locally
        uncertainty_bounds = self.uncertainty_quantifier.calculate(current_state)
        
        # Optimize edge resource usage
        optimization_result = self.edge_optimizer.optimize_performance()
        
        return {
            'digital_twin_state': current_state,
            'uncertainty': uncertainty_bounds,
            'confidence_score': self.calculate_confidence(uncertainty_bounds),
            'optimization_metrics': optimization_result
        }