🔭lora monitoring app

# Django API for LoRa data processing
from django.http import JsonResponse
from .models import LoRaDevice, SensorReading
import json

class LoRaDataProcessor:
    def __init__(self):
        self.ai_processor = JetsonAIProcessor()
        
    def process_lora_packet(self, request):
        # Parse LoRa packet data
        packet_data = json.loads(request.body)
        device_id = packet_data['device_id']
        sensor_data = packet_data['payload']
        
        # AI-powered anomaly detection
        anomalies = self.ai_processor.detect_anomalies(sensor_data)
        
        # Store in database
        reading = SensorReading.objects.create(
            device_id=device_id,
            data=sensor_data,
            anomalies=anomalies
        )
        
        return JsonResponse({
            'status': 'success',
            'anomalies_detected': len(anomalies),
            'recommendations': self.generate_recommendations(anomalies)
        })

# Automatic device registration via Django admin
from django.contrib import admin
from .models import LoRaDevice

@admin.register(LoRaDevice)
class LoRaDeviceAdmin(admin.ModelAdmin):
    list_display = ['device_name', 'dev_eui', 'app_key', 'last_seen', 'battery_level']
    list_filter = ['device_type', 'location', 'is_active']
    search_fields = ['device_name', 'dev_eui']
    
    def get_readonly_fields(self, request, obj=None):
        if obj:  # Editing existing object
            return ['dev_eui', 'app_key']
        return []

# AI-powered security monitoring for LoRa network
import numpy as np
from sklearn.ensemble import IsolationForest

class LoRaSecurityAI:
    def __init__(self):
        self.anomaly_detector = IsolationForest(contamination=0.1)
        self.threat_patterns = self.load_threat_signatures()
        
    def analyze_traffic_patterns(self, lora_packets):
        # Extract features from LoRa traffic
        features = self.extract_features(lora_packets)
        
        # Detect anomalous behavior
        anomalies = self.anomaly_detector.predict(features)
        
        # Check against known attack patterns
        threats = self.check_threat_signatures(lora_packets)
        
        return {
            'anomalies': anomalies,
            'potential_threats': threats,
            'security_score': self.calculate_security_score(anomalies, threats)
        }

# Predictive maintenance using historical sensor data
class PredictiveMaintenanceAI:
    def __init__(self):
        self.lstm_model = self.load_trained_model()
        
    def predict_device_failure(self, device_history):
        # Preprocess time series data
        features = self.preprocess_timeseries(device_history)
        
        # LSTM prediction for failure probability
        failure_prob = self.lstm_model.predict(features)
        
        # Generate maintenance recommendations
        recommendations = self.generate_maintenance_plan(failure_prob)
        
        return {
            'failure_probability': failure_prob,
            'days_until_failure': self.estimate_ttf(failure_prob),
            'recommended_actions': recommendations
        }

# Battery monitoring via LoRa for digital twin
class LoRaBatteryMonitor:
    def __init__(self):
        self.digital_twin = BatteryDigitalTwin()
        self.lora_gateway = LoRaGateway()
        
    def process_battery_telemetry(self, lora_packet):
        # Parse battery data from LoRa packet
        battery_data = self.parse_battery_packet(lora_packet)
        
        # Update digital twin model
        twin_state = self.digital_twin.update_state(battery_data)
        
        # Generate predictive insights
        predictions = self.digital_twin.predict_degradation()
        
        return {
            'current_state': twin_state,
            'health_score': predictions['health_score'],
            'remaining_life': predictions['estimated_life']
        }

# LoRa-based drone coordination
class DroneLoRaCoordinator:
    def __init__(self):
        self.drone_fleet = BebopFleetManager()
        self.lora_network = LoRaNetworkManager()
        
    def coordinate_area_monitoring(self, sensor_alerts):
        # Analyze sensor data for areas requiring drone inspection
        priority_areas = self.analyze_sensor_alerts(sensor_alerts)
        
        # Dispatch drones to high-priority locations
        for area in priority_areas:
            available_drone = self.drone_fleet.get_nearest_drone(area.location)
            mission = self.create_inspection_mission(area)
            available_drone.execute_mission(mission)