import random
import time
import asyncio
import json
import numpy as np
from scipy.integrate import odeint
import requests
from collections import defaultdict
import threading
class DigitalTwin:
"""Physics-based digital twin for resources (e.g., water flow, energy dissipation)."""
def __init__(self, resource_type, initial_state):
self.type = resource_type
self.state = initial_state # e.g., {'level': 1000, 'flow_rate': 10}
def simulate_physics(self, dt=1):
"""ODE-based simulation for resource dynamics."""
def model(y, t):
level, flow = y
dlevel_dt = flow - random.uniform(0.5, 1.5) # Usage/demand
dflow_dt = -0.1 * flow + random.uniform(-0.1, 0.1) # Damping + noise
return [dlevel_dt, dflow_dt]
t = np.linspace(0, dt, 10)
sol = odeint(model, [self.state['level'], self.state['flow_rate']], t)
self.state['level'], self.state['flow_rate'] = sol[-1]
return self.state
class IoTSimulator:
def __init__(self, num_sensors=100, planetary_scale=True):
self.num_sensors = num_sensors
self.sensors = {f"sensor_{i}": {
'location': (random.uniform(-90, 90), random.uniform(-180, 180)), # Lat/Long
'data': {'water_level': random.uniform(0, 1000), 'energy_usage': random.uniform(0, 500), 'minerals_stock': random.uniform(0, 10000)},
'twin': DigitalTwin('water', {'level': random.uniform(500, 1500), 'flow_rate': random.uniform(5, 15)}),
'anomaly_score': 0,
'network_status': 'active'
} for i in range(num_sensors)}
self.data_stream = asyncio.Queue()
self.anomaly_detector = self._init_anomaly_detector()
self.consensus_log = []
self.planetary_scale = planetary_scale # Enable global augmentations
def _init_anomaly_detector(self):
"""Simple ML-based anomaly detection (threshold-based for demo)."""
return {'water_threshold': 200, 'energy_threshold': 400} # Tune with real data
async def simulate_tracking(self):
"""Real-time tracking with streaming."""
for sensor_id, sensor in self.sensors.items():
# Update digital twin
sensor['twin'].simulate_physics()
# Simulate sensor readings with noise
sensor['data']['water_level'] = max(0, sensor['twin'].state['level'] + random.gauss(0, 50))
sensor['data']['energy_usage'] += random.gauss(0, 20)
sensor['data']['minerals_stock'] -= random.uniform(0, 10)
# Anomaly detection
sensor['anomaly_score'] = self._detect_anomaly(sensor['data'])
if sensor['anomaly_score'] > 0.8:
sensor['network_status'] = 'alert' # Trigger self-healing
await self.data_stream.put({sensor_id: sensor})
return dict(self.sensors)
def _detect_anomaly(self, data):
"""Anomaly score based on thresholds."""
score = 0
if data['water_level'] < self.anomaly_detector['water_threshold']:
score += 0.5
if data['energy_usage'] > self.anomaly_detector['energy_threshold']:
score += 0.5
return min(score, 1)
async def stream_data(self):
"""Continuous streaming loop."""
while True:
data = await self.data_stream.get()
print(f"Streaming: {json.dumps(data, indent=2)}")
await asyncio.sleep(1) # Real-time interval
def augment_with_real_data(self):
"""Augment with real APIs (e.g., satellite for global water levels)."""
if self.planetary_scale:
try:
# Example: NASA Earthdata API (replace with key)
response = requests.get("https://api.nasa.gov/planetary/earth/imagery?lon=-122.0&lat=37.0&date=2023-01-01&api_key=your_api_key")
if response.status_code == 200:
# Simulate adjusting sensors based on real imagery
for sensor in self.sensors.values():
sensor['data']['water_level'] *= random.uniform(0.9, 1.1) # Adjust based on "real" data
except:
print("Real data augmentation failed; using synthetic.")
def multi_agent_consensus(self):
"""Simulate mesh network consensus for data validation."""
votes = defaultdict(list)
for sensor_id, sensor in self.sensors.items():
for neighbor in random.sample(list(self.sensors.keys()), 3): # Random neighbors
votes[sensor_id].append(self.sensors[neighbor]['data']['water_level'])
for sensor_id in votes:
consensus_value = np.median(votes[sensor_id])
self.sensors[sensor_id]['data']['water_level'] = (self.sensors[sensor_id]['data']['water_level'] + consensus_value) / 2
self.consensus_log.append({"round": len(self.consensus_log) + 1, "consensus": dict(votes), "timestamp": time.time()})
def get_digital_twin(self, sensor_id):
"""Retrieve full digital twin data."""
return self.sensors.get(sensor_id, {}).get('twin', {}).state
def integrate_with_quantum_ai(self, quantum_ledger, ai_optimizer):
"""Full integration: Feed IoT data to quantum sync and AI optimization."""
iot_data = asyncio.run(self.simulate_tracking())
regions_data = [[d['data']['water_level'], d['data']['energy_usage'], d['data']['minerals_stock']] for d in iot_data.values()]
allocations = ai_optimizer.optimize_allocation(regions_data)
planetary_data = {f"region_{i}": {"water": allocations[i][0], "energy": allocations[i][1], "minerals": allocations[i][2]} for i in range(len(allocations))}
synced_ledgers, consensus = quantum_ledger.multi_node_sync(planetary_data)
return synced_ledgers, consensus, iot_data
# Example Usage (Runnable Standalone)
async def main():
from quantum_ledger import QuantumLedger
from ai_optimizer import ResourceOptimizer
simulator = IoTSimulator(num_sensors=10) # Scale up for planetary
ledger = QuantumLedger()
optimizer = ResourceOptimizer()
# Augment with real data
simulator.augment_with_real_data()
# Run consensus
simulator.multi_agent_consensus()
# Start streaming in background
asyncio.create_task(simulator.stream_data())
# Integrate
synced, consensus, iot = await simulator.integrate_with_quantum_ai(ledger, optimizer)
print("Integrated IoT Data:", json.dumps(iot, indent=2))
print("Synced Ledgers:", json.dumps(synced, indent=2))
print("Consensus:", consensus)
if __name__ == "__main__":
asyncio.run(main())
