// Phase 6: Edge Computing Examples
// Refactored to use bond (orchestration), ai (agents), iot (devices/edge), and mold (agent configs)
//
// Bond: Connects IoT → AI → DB/chain flows
// AI: Edge agents for analysis and coordination
// IoT: Devices, sensors, edge nodes, actuators
// Mold: Agent config objects (name, type, role, capabilities) passed to ai::spawn_agent
@trust("hybrid")
@chain("ethereum")
service SmartCityEdgeManager {
edge_nodes: list<any>;
city_sensors: list<any>;
traffic_systems: list<any>;
environmental_monitors: list<any>;
public_safety_systems: list<any>;
data_processing_pipelines: list<any>;
edge_agents: map<string, string>;
city_db: string;
fn initialize_smart_city_infrastructure() {
log::info("iot", { "message": "Initializing Smart City Edge Infrastructure" });
self.edge_nodes = [];
self.city_sensors = [];
self.traffic_systems = [];
self.environmental_monitors = [];
self.public_safety_systems = [];
self.data_processing_pipelines = [];
self.edge_agents = {};
self.city_db = database::connect("city_db");
self.deploy_city_edge_nodes();
self.setup_city_sensor_networks();
self.configure_traffic_systems();
self.initialize_environmental_monitoring();
self.setup_public_safety_systems();
self.create_data_processing_pipelines();
self.spawn_edge_ai_agents();
}
fn spawn_edge_ai_agents() {
// Mold-style agent configs for edge AI
let traffic_mold = {
"name": "TrafficEdgeAnalyst",
"type": "ai",
"role": "traffic_optimization",
"capabilities": ["congestion_analysis", "signal_timing", "predictive_flow"]
};
let env_mold = {
"name": "EnvironmentalMonitor",
"type": "ai",
"role": "environmental_analysis",
"capabilities": ["air_quality", "anomaly_detection", "recommendations"]
};
let safety_mold = {
"name": "SafetyCoordinator",
"type": "system",
"role": "emergency_response",
"capabilities": ["threat_detection", "incident_classification", "response_coordination"]
};
self.edge_agents["traffic"] = ai::spawn_agent(traffic_mold);
self.edge_agents["environmental"] = ai::spawn_agent(env_mold);
self.edge_agents["safety"] = ai::spawn_agent(safety_mold);
let coordinator_id = ai::create_agent_coordinator();
log::info("iot", {
"message": "Edge AI agents spawned (mold configs)",
"agents": self.edge_agents,
"coordinator": coordinator_id
});
}
fn deploy_city_edge_nodes() {
let city_zones = [
{ "name": "downtown", "lat": 40.7589, "lon": -73.9851, "population_density": "high" },
{ "name": "residential", "lat": 40.7282, "lon": -73.7949, "population_density": "medium" },
{ "name": "industrial", "lat": 40.6782, "lon": -74.1285, "population_density": "medium" },
{ "name": "commercial", "lat": 40.7505, "lon": -73.9934, "population_density": "high" },
{ "name": "park_recreational", "lat": 40.7829, "lon": -73.9654, "population_density": "low" }
];
for zone in city_zones {
let node_id = "city_edge_" + zone.name;
let is_high = zone.population_density == "high";
let cpu_cores = 16;
let memory_gb = 32.0;
let storage_cap = 500000000000;
let bandwidth = 10000000000;
if (!is_high) {
cpu_cores = 8;
memory_gb = 16.0;
storage_cap = 100000000000;
bandwidth = 1000000000;
}
let edge_config = {
"node_id": node_id,
"name": "Smart City Edge Node - " + zone.name,
"location": {
"latitude": zone.lat,
"longitude": zone.lon,
"accuracy": 5.0
},
"capabilities": [
"real_time_processing",
"ai_inference",
"data_aggregation",
"predictive_analytics",
"anomaly_detection",
"traffic_optimization",
"emergency_response"
],
"processing_power": {
"cpu_cores": cpu_cores,
"memory_gb": memory_gb,
"gpu_available": is_high,
"ai_acceleration": true
},
"storage_capacity": storage_cap,
"network_bandwidth": bandwidth,
"power_source": "grid",
"backup_power": "battery"
};
iot::create_edge_node(edge_config);
self.edge_nodes.push({ "node_id": node_id, "zone": zone });
}
}
fn setup_city_sensor_networks() {
let sensor_types = [
{ "type": "traffic_camera", "count_per_zone": 8, "sampling_rate": 30 },
{ "type": "air_quality", "count_per_zone": 4, "sampling_rate": 300 },
{ "type": "crowd_density", "count_per_zone": 6, "sampling_rate": 60 },
{ "type": "energy_monitor", "count_per_zone": 15, "sampling_rate": 300 }
];
for edge_node in self.edge_nodes {
let node_id = edge_node.node_id;
for sensor_type in sensor_types {
for i in 0..sensor_type.count_per_zone {
let idx = i + 1;
let sensor_id = node_id + "_" + sensor_type.type + "_" + idx;
let sensor_config = {
"sensor_id": sensor_id,
"name": sensor_type.type + " Sensor " + idx,
"sensor_type": sensor_type.type,
"sampling_rate": sensor_type.sampling_rate,
"edge_node": node_id
};
iot::add_sensor_to_device(node_id, sensor_config);
self.city_sensors.push({ "sensor_id": sensor_id, "node_id": node_id });
}
}
}
}
fn configure_traffic_systems() {
for edge_node in self.edge_nodes {
let node_id = edge_node.node_id;
let system_id = node_id + "_traffic";
let traffic_system_config = {
"system_id": system_id,
"name": "Traffic Management - " + node_id,
"capabilities": ["signal_control", "congestion_detection", "emergency_vehicle_priority"],
"connected_cameras": 8,
"traffic_lights": 12,
"detection_zones": 6
};
iot::register_device(traffic_system_config);
self.traffic_systems.push({ "system_id": system_id, "node_id": node_id });
for i in 0..12 {
let light_id = system_id + "_traffic_light_" + (i + 1);
iot::add_actuator_to_device(system_id, {
"actuator_id": light_id,
"name": "Traffic Light " + (i + 1),
"actuator_type": "traffic_signal",
"supported_commands": ["green", "yellow", "red", "flash"]
});
}
}
}
fn initialize_environmental_monitoring() {
for edge_node in self.edge_nodes {
let node_id = edge_node.node_id;
let monitor_id = node_id + "_env_monitor";
let env_monitor_config = {
"monitor_id": monitor_id,
"name": "Environmental Monitor - " + node_id,
"capabilities": ["air_quality", "noise_pollution", "weather_monitoring"],
"alert_thresholds": {
"air_quality_index": 150,
"noise_level_db": 70,
"temperature_celsius": 35
}
};
iot::register_device(env_monitor_config);
self.environmental_monitors.push({ "monitor_id": monitor_id, "node_id": node_id });
}
}
fn setup_public_safety_systems() {
for edge_node in self.edge_nodes {
let node_id = edge_node.node_id;
let system_id = node_id + "_safety";
let safety_system_config = {
"system_id": system_id,
"name": "Public Safety System - " + node_id,
"capabilities": ["emergency_detection", "crowd_monitoring", "incident_reporting", "emergency_alerting"],
"emergency_protocols": {
"fire_detection": "activate_fire_response",
"medical_emergency": "dispatch_ambulance",
"security_threat": "alert_authorities",
"crowd_control": "manage_access"
}
};
iot::register_device(safety_system_config);
self.public_safety_systems.push({ "system_id": system_id, "node_id": node_id });
}
}
fn create_data_processing_pipelines() {
let pipeline_configs = [
{
"name": "traffic_optimization",
"data_sources": ["traffic_camera", "crowd_density", "parking_sensor"],
"processing_steps": ["object_detection", "congestion_analysis", "predictive_modeling"],
"output_sinks": ["traffic_control_system", "city_command_center", "public_app"]
},
{
"name": "environmental_monitoring",
"data_sources": ["air_quality", "weather_station", "noise_monitor"],
"processing_steps": ["data_validation", "trend_analysis", "anomaly_detection"],
"output_sinks": ["environmental_agency", "public_alert_system", "research_database"]
},
{
"name": "public_safety",
"data_sources": ["crowd_density", "traffic_camera", "emergency_sensors"],
"processing_steps": ["threat_detection", "incident_classification", "response_coordination"],
"output_sinks": ["emergency_services", "police_department", "city_monitoring"]
},
{
"name": "energy_management",
"data_sources": ["energy_monitor", "weather_station", "building_sensors"],
"processing_steps": ["consumption_analysis", "efficiency_optimization", "demand_prediction"],
"output_sinks": ["utility_company", "building_management", "city_planning"]
}
];
for config in pipeline_configs {
self.data_processing_pipelines.push({
"pipeline_id": "pipeline_" + config.name,
"name": config.name,
"edge_node": "city_edge_downtown",
"data_sources": config.data_sources,
"processing_steps": config.processing_steps,
"output_sinks": config.output_sinks,
"processing_priority": "high",
"real_time_processing": true,
"data_retention_days": 30
});
}
}
// Bond: IoT → AI → edge processing flow
fn bond_iot_to_ai(edge_node_id: string, sensor_data: any) -> any {
let processed = iot::process_data_at_edge(edge_node_id, sensor_data, "city_data_processing");
let traffic_agent_id = self.edge_agents["traffic"];
let task_id = ai::create_task(traffic_agent_id, "analysis", "Analyze traffic and environmental data", "high");
return {
"processed": processed,
"agent_id": traffic_agent_id,
"task_id": task_id,
"edge_node_id": edge_node_id
};
}
// Bond: AI results → cloud / chain sync
fn bond_ai_to_cloud(device_id: string, report: any) {
iot::sync_device_data_to_cloud(device_id, report);
log::info("bond", { "flow": "ai_to_cloud", "device_id": device_id });
}
fn process_real_time_city_data() {
let processing_report = {
"timestamp": chain::get_block_timestamp(1),
"data_points_processed": 0,
"ai_inferences_performed": 0,
"alerts_generated": 0,
"traffic_optimizations": 0,
"energy_savings": 0.0,
"response_time_ms": 0
};
for edge_node in self.edge_nodes {
let node_id = edge_node.node_id;
let sensor_data = self.collect_edge_sensor_data(node_id);
processing_report.data_points_processed = processing_report.data_points_processed + 1;
let bond_result = self.bond_iot_to_ai(node_id, sensor_data);
processing_report.ai_inferences_performed = processing_report.ai_inferences_performed + 15;
let ai_analysis = self.perform_edge_ai_analysis(bond_result.processed);
let alerts_actions = self.generate_city_alerts_actions(ai_analysis);
processing_report.alerts_generated = processing_report.alerts_generated + alerts_actions.alerts_count;
processing_report.traffic_optimizations = processing_report.traffic_optimizations + alerts_actions.traffic_actions;
processing_report.energy_savings = processing_report.energy_savings + alerts_actions.energy_savings;
iot::cache_data_at_edge(node_id, "city_processing_results", bond_result, 3600);
}
processing_report.response_time_ms = 150;
self.bond_ai_to_cloud("city_edge_coordinator", processing_report);
log::info("iot", {
"city_processing_report": processing_report,
"performance_metrics": {
"throughput": processing_report.data_points_processed * 1000 / 150,
"efficiency": processing_report.energy_savings / (processing_report.data_points_processed + 1)
}
});
}
fn collect_edge_sensor_data(edge_node_id: string) -> any {
let sensor_data = {
"edge_node_id": edge_node_id,
"traffic_data": [],
"environmental_data": [],
"crowd_data": [],
"energy_data": [],
"timestamp": chain::get_block_timestamp(1)
};
for i in 0..8 {
let sensor_id = edge_node_id + "_traffic_camera_" + (i + 1);
let reading = iot::read_sensor_data(sensor_id);
sensor_data.traffic_data.push(reading);
}
for i in 0..4 {
let sensor_id = edge_node_id + "_air_quality_" + (i + 1);
let reading = iot::read_sensor_data(sensor_id);
sensor_data.environmental_data.push(reading);
}
for i in 0..6 {
let sensor_id = edge_node_id + "_crowd_density_" + (i + 1);
let reading = iot::read_sensor_data(sensor_id);
sensor_data.crowd_data.push(reading);
}
for i in 0..15 {
let sensor_id = edge_node_id + "_energy_monitor_" + (i + 1);
let reading = iot::read_sensor_data(sensor_id);
sensor_data.energy_data.push(reading);
}
return sensor_data;
}
fn perform_edge_ai_analysis(data: any) -> any {
let analysis_results = {
"inference_count": 15,
"traffic_analysis": {
"congestion_level": "low",
"optimal_signal_timing": {},
"predicted_flow": []
},
"environmental_analysis": {
"air_quality_index": 85,
"pollution_sources": [],
"recommended_actions": []
},
"crowd_analysis": {
"density_level": "normal",
"safety_score": 95,
"capacity_warnings": []
},
"energy_analysis": {
"efficiency_score": 88,
"optimization_opportunities": [],
"predicted_demand": []
},
"anomaly_detection": {
"anomalies_detected": 2,
"anomaly_types": ["unusual_traffic_pattern", "energy_spike"],
"confidence_levels": [0.92, 0.87]
}
};
return analysis_results;
}
fn generate_city_alerts_actions(analysis: any) -> any {
let alerts_actions = {
"alerts_count": 0,
"traffic_actions": 0,
"energy_savings": 0.0,
"alerts": [],
"actions": []
};
if (analysis.traffic_analysis.congestion_level == "high") {
alerts_actions.actions.push({
"action_type": "traffic_signal_optimization",
"target_intersections": ["intersection_1", "intersection_2"],
"new_timing": { "green_time": 45, "yellow_time": 5, "red_time": 60 },
"expected_improvement": "25%_flow_increase"
});
alerts_actions.traffic_actions = alerts_actions.traffic_actions + 1;
}
if (analysis.environmental_analysis.air_quality_index > 150) {
alerts_actions.alerts.push({
"alert_type": "poor_air_quality",
"severity": "medium",
"affected_zones": ["downtown", "commercial"],
"recommended_actions": ["reduce_vehicle_emissions", "activate_air_purification"]
});
alerts_actions.alerts_count = alerts_actions.alerts_count + 1;
}
if (analysis.energy_analysis.efficiency_score < 85) {
alerts_actions.actions.push({
"action_type": "energy_optimization",
"target_buildings": ["building_1", "building_2"],
"optimization_type": "load_balancing",
"expected_savings_kwh": 150.5
});
alerts_actions.energy_savings = 150.5;
}
for anomaly in analysis.anomaly_detection.anomaly_types {
alerts_actions.alerts.push({
"alert_type": "system_anomaly",
"anomaly_type": anomaly,
"severity": "low",
"investigation_required": true
});
alerts_actions.alerts_count = alerts_actions.alerts_count + 1;
}
return alerts_actions;
}
fn optimize_city_operations() {
let optimization_report = {
"timestamp": chain::get_block_timestamp(1),
"traffic_optimization": {
"average_speed_improvement": 15.2,
"emissions_reduction": 8.5,
"travel_time_savings": 120
},
"energy_optimization": {
"total_savings_kwh": 2450.5,
"peak_demand_reduction": 12.3,
"cost_savings_usd": 1850.75
},
"environmental_impact": {
"air_quality_improvement": 6.2,
"noise_reduction": 4.8,
"greenhouse_gas_reduction": 125.5
},
"public_safety": {
"incident_prevention_rate": 94.2,
"response_time_improvement": 35.1,
"emergency_detection_accuracy": 98.7
}
};
let actions = self.generate_optimization_actions(optimization_report);
for action in actions {
self.execute_optimization_action(action);
}
self.bond_ai_to_cloud("city_edge_coordinator", optimization_report);
log::info("iot", {
"city_optimization_report": optimization_report,
"total_benefits": {
"energy_savings": optimization_report.energy_optimization.total_savings_kwh,
"cost_savings": optimization_report.energy_optimization.cost_savings_usd,
"environmental_benefit": optimization_report.environmental_impact.greenhouse_gas_reduction
}
});
}
fn generate_optimization_actions(report: any) -> list<any> {
let actions = [];
if (report.traffic_optimization.average_speed_improvement < 20) {
actions.push({
"action_type": "traffic_signal_timing",
"target": "city_intersections",
"optimization_algorithm": "adaptive_timing",
"expected_improvement": 8.5
});
}
if (report.energy_optimization.peak_demand_reduction < 15) {
actions.push({
"action_type": "demand_response",
"target": "commercial_buildings",
"optimization_strategy": "peak_shaving",
"expected_savings": 850.25
});
}
if (report.environmental_impact.air_quality_improvement < 10) {
actions.push({
"action_type": "emission_control",
"target": "traffic_systems",
"optimization_method": "eco_routing",
"expected_improvement": 12.3
});
}
return actions;
}
fn execute_optimization_action(action: any) {
if (action.action_type == "traffic_signal_timing") {
for i in 0..12 {
let cmd = {
"command": "update_timing",
"parameters": {
"algorithm": "adaptive",
"congestion_weight": 0.7,
"emergency_weight": 0.3
}
};
iot::send_actuator_command("traffic_light_" + (i + 1), "optimize", cmd);
}
} else if (action.action_type == "demand_response") {
for i in 0..10 {
let cmd = {
"command": "demand_response",
"parameters": {
"target_reduction": 15,
"duration_minutes": 30,
"compensation_rate": 0.25
}
};
iot::send_actuator_command("building_management_" + (i + 1), "optimize_energy", cmd);
}
} else if (action.action_type == "emission_control") {
let eco_config = {
"command": "enable_eco_routing",
"parameters": {
"priority_routes": ["route_a", "route_b"],
"emission_threshold": 50,
"rerouting_enabled": true
}
};
iot::send_actuator_command("traffic_management_system", "eco_mode", eco_config);
}
log::info("iot", {
"optimization_action_executed": action.action_type,
"target": action.target
});
}
fn handle_city_emergencies() {
for edge_node in self.edge_nodes {
let node_id = edge_node.node_id;
let emergency_check = {
"edge_node_id": node_id,
"emergency_types": [
"fire_detected",
"chemical_spill",
"traffic_accident",
"medical_emergency",
"security_threat",
"infrastructure_failure"
],
"detection_sensors": ["camera", "air_quality", "motion", "sound"],
"response_protocols": {
"fire_detected": "fire_department_response",
"chemical_spill": "hazmat_response",
"traffic_accident": "emergency_medical_response",
"security_threat": "police_response"
}
};
let emergency_analysis = self.analyze_emergency_conditions(node_id);
if (emergency_analysis.emergency_detected) {
self.activate_emergency_response(emergency_analysis);
}
}
}
fn analyze_emergency_conditions(edge_node_id: string) -> any {
let analysis = {
"edge_node_id": edge_node_id,
"emergency_detected": false,
"emergency_type": "",
"confidence_level": 0.0,
"affected_area": "",
"severity_level": "",
"immediate_actions_required": [],
"response_resources_needed": []
};
let ts = chain::get_block_timestamp(1);
let emergency_probability = (ts % 100);
if (emergency_probability > 95) {
analysis.emergency_detected = true;
analysis.emergency_type = "traffic_accident";
analysis.confidence_level = 0.87;
analysis.affected_area = "intersection_5";
analysis.severity_level = "medium";
analysis.immediate_actions_required = ["divert_traffic", "dispatch_ambulance"];
analysis.response_resources_needed = ["ambulance", "police_unit", "fire_truck"];
}
return analysis;
}
fn activate_emergency_response(emergency_data: any) {
let emergency_response = {
"emergency_id": "emergency_" + chain::get_block_timestamp(1),
"emergency_type": emergency_data.emergency_type,
"location": emergency_data.affected_area,
"severity": emergency_data.severity_level,
"timestamp": chain::get_block_timestamp(1),
"response_coordinated": true,
"resources_activated": []
};
for action in emergency_data.immediate_actions_required {
if (action == "divert_traffic") {
let traffic_diversion = {
"command": "divert_traffic",
"parameters": {
"affected_intersections": ["intersection_5", "intersection_6"],
"alternative_routes": ["route_alpha", "route_beta"],
"duration_minutes": 60
}
};
iot::send_actuator_command("traffic_management_system", "emergency_routing", traffic_diversion);
emergency_response.resources_activated.push("traffic_diversion_system");
} else if (action == "dispatch_ambulance") {
let ambulance_dispatch = {
"command": "dispatch_emergency",
"parameters": {
"service_type": "ambulance",
"priority": "high",
"location": emergency_data.affected_area,
"estimated_response_time": 8
}
};
iot::publish_message("emergency_services", "emergency_dispatch", ambulance_dispatch);
emergency_response.resources_activated.push("emergency_medical_services");
}
}
let emergency_alert = {
"alert_type": "city_emergency",
"emergency_id": emergency_response.emergency_id,
"emergency_type": emergency_data.emergency_type,
"location": emergency_data.affected_area,
"severity": emergency_data.severity_level,
"timestamp": emergency_response.timestamp,
"response_actions": emergency_data.immediate_actions_required
};
let emergency_channels = ["police_department", "fire_department", "emergency_medical", "city_management"];
for channel in emergency_channels {
iot::publish_message(channel, "emergency_broadcast", emergency_alert);
}
log::info("iot", {
"emergency_response_activated": emergency_response,
"resources_activated_count": 1,
"response_channels_notified": 4
});
}
fn generate_city_performance_report() {
let performance_report = {
"report_type": "city_performance_analysis",
"report_period": "daily",
"generated_at": chain::get_block_timestamp(1),
"edge_nodes_active": self.edge_nodes.length,
"sensors_monitored": self.city_sensors.length,
"data_points_processed": 1500000,
"ai_inferences_performed": 50000,
"alerts_generated": 25,
"optimizations_implemented": 15,
"performance_metrics": {
"average_response_time_ms": 145,
"system_uptime_percentage": 99.7,
"data_processing_throughput": 8500,
"ai_inference_accuracy": 94.2
},
"service_quality": {
"traffic_flow_efficiency": 87.5,
"energy_usage_efficiency": 91.2,
"environmental_quality_index": 82.3,
"public_safety_score": 96.1
},
"cost_savings": {
"energy_costs_usd": 2850.50,
"traffic_congestion_costs_usd": 12500.75,
"emergency_response_costs_usd": 3200.25,
"total_savings_usd": 18551.50
},
"recommendations": []
};
if (performance_report.service_quality.traffic_flow_efficiency < 90) {
performance_report.recommendations.push("Implement advanced traffic prediction algorithms");
}
if (performance_report.performance_metrics.ai_inference_accuracy < 95) {
performance_report.recommendations.push("Update AI models with additional training data");
}
self.bond_ai_to_cloud("city_performance_monitor", performance_report);
log::info("iot", {
"city_performance_report_generated": performance_report.generated_at,
"total_savings": performance_report.cost_savings.total_savings_usd,
"recommendations_count": performance_report.recommendations.length
});
}
}
@trust("hybrid")
@chain("ethereum")
service ManufacturingEdgeController {
production_lines: list<any>;
quality_control_systems: list<any>;
predictive_maintenance: list<any>;
supply_chain_monitoring: list<any>;
edge_processing_nodes: list<any>;
edge_agents: map<string, string>;
fn initialize_manufacturing_edge() {
log::info("iot", { "message": "Initializing Manufacturing Edge Computing System" });
self.production_lines = [];
self.quality_control_systems = [];
self.predictive_maintenance = [];
self.supply_chain_monitoring = [];
self.edge_processing_nodes = [];
self.edge_agents = {};
self.setup_production_monitoring();
self.configure_quality_control();
self.initialize_predictive_maintenance();
self.setup_supply_chain_monitoring();
self.deploy_edge_processing_nodes();
self.spawn_manufacturing_agents();
}
fn spawn_manufacturing_agents() {
let quality_mold = {
"name": "QualityInspector",
"type": "ai",
"role": "quality_assurance",
"capabilities": ["defect_classifier", "dimensional_verifier", "surface_analyzer"]
};
let maintenance_mold = {
"name": "PredictiveMaintenance",
"type": "ai",
"role": "failure_prediction",
"capabilities": ["time_series", "anomaly_detection", "regression"]
};
self.edge_agents["quality"] = ai::spawn_agent(quality_mold);
self.edge_agents["maintenance"] = ai::spawn_agent(maintenance_mold);
log::info("iot", {
"message": "Manufacturing edge agents spawned (mold configs)",
"agents": self.edge_agents
});
}
fn setup_production_monitoring() {
let production_lines = ["assembly_line_1", "assembly_line_2", "packaging_line", "quality_control"];
for line_name in production_lines {
let line_id = "prod_line_" + line_name;
let production_line = {
"line_id": line_id,
"name": "Production Line - " + line_name,
"capabilities": ["real_time_monitoring", "efficiency_tracking", "defect_detection"],
"sensors": ["vibration", "temperature", "pressure", "flow_rate", "power_consumption"],
"actuators": ["conveyor_speed", "robot_arm", "packaging_machine", "quality_gate"],
"target_output_per_hour": 500,
"quality_threshold": 99.5
};
self.production_lines.push(production_line);
for sensor_type in production_line.sensors {
let sensor_id = line_id + "_" + sensor_type + "_sensor";
iot::add_sensor_to_device(line_id, {
"sensor_id": sensor_id,
"name": sensor_type + " Sensor",
"sensor_type": sensor_type,
"sampling_rate": 1000,
"critical_thresholds": {
"vibration": { "warning": 5.0, "critical": 10.0 },
"temperature": { "warning": 80.0, "critical": 100.0 },
"pressure": { "warning": 50.0, "critical": 30.0 }
}
});
}
}
}
fn configure_quality_control() {
for production_line in self.production_lines {
let line_id = production_line.line_id;
let system_id = line_id + "_quality";
let quality_system = {
"system_id": system_id,
"name": "Quality Control - " + line_id,
"capabilities": ["visual_inspection", "dimensional_check", "defect_detection"],
"inspection_points": ["assembly_stage", "packaging_stage", "final_check"],
"ai_models": ["defect_classifier", "dimensional_verifier", "surface_analyzer"],
"accuracy_target": 99.9,
"false_positive_rate": 0.1
};
self.quality_control_systems.push(quality_system);
iot::add_sensor_to_device(system_id, {
"sensor_id": system_id + "_vision_sensor",
"name": "High-Resolution Vision Sensor",
"sensor_type": "camera",
"resolution": "4K",
"frame_rate": 30,
"ai_processing": true
});
iot::add_actuator_to_device(system_id, {
"actuator_id": system_id + "_rejection_arm",
"name": "Product Rejection Arm",
"actuator_type": "robotic_arm",
"supported_commands": ["reject_product", "divert_to_rework", "mark_defective"]
});
}
}
fn initialize_predictive_maintenance() {
for production_line in self.production_lines {
let line_id = production_line.line_id;
let system_id = line_id + "_maintenance";
self.predictive_maintenance.push({
"system_id": system_id,
"name": "Predictive Maintenance - " + line_id,
"capabilities": ["failure_prediction", "maintenance_scheduling", "parts_inventory"],
"monitored_components": ["motors", "conveyors", "robots", "sensors", "actuators"],
"prediction_models": ["time_series", "anomaly_detection", "regression"],
"maintenance_thresholds": {
"failure_probability": 0.8,
"maintenance_cost_threshold": 5000,
"downtime_risk_threshold": 0.7
}
});
}
}
fn setup_supply_chain_monitoring() {
let supply_chain_points = ["raw_materials", "component_assembly", "finished_goods", "shipping"];
for point in supply_chain_points {
self.supply_chain_monitoring.push({
"system_id": "supply_chain_" + point,
"name": "Supply Chain Monitor - " + point,
"capabilities": ["inventory_tracking", "quality_monitoring", "delivery_prediction"],
"tracked_items": ["components", "raw_materials", "finished_products"],
"monitoring_points": ["warehouse", "production_line", "shipping_dock"],
"alert_thresholds": {
"low_inventory": 100,
"quality_rejection_rate": 0.05,
"delivery_delay_risk": 0.7
}
});
}
}
fn deploy_edge_processing_nodes() {
let edge_node_configs = [
{
"type": "production_monitoring",
"capabilities": ["real_time_processing", "production_analytics", "quality_assurance"],
"processing_power": { "cpu_cores": 16, "memory_gb": 32.0, "gpu": true },
"storage_capacity": 1000000000000
},
{
"type": "predictive_maintenance",
"capabilities": ["ai_inference", "time_series_analysis", "anomaly_detection"],
"processing_power": { "cpu_cores": 8, "memory_gb": 16.0, "gpu": true },
"storage_capacity": 500000000000
},
{
"type": "quality_control",
"capabilities": ["computer_vision", "deep_learning", "image_processing"],
"processing_power": { "cpu_cores": 12, "memory_gb": 24.0, "gpu": true },
"storage_capacity": 2000000000000
}
];
for config in edge_node_configs {
let node_id = "manufacturing_edge_" + config.type;
iot::create_edge_node({
"node_id": node_id,
"name": "Manufacturing Edge Node - " + config.type,
"capabilities": config.capabilities,
"processing_power": config.processing_power,
"storage_capacity": config.storage_capacity,
"network_bandwidth": 10000000000,
"power_source": "industrial_grid"
});
self.edge_processing_nodes.push({ "node_id": node_id, "type": config.type });
}
}
// Bond: IoT sensors → AI predictive maintenance
fn bond_iot_to_predictive_maintenance(device_id: string, metric: string) -> any {
let prediction = iot::predict_device_failure(device_id, metric);
let agent_id = self.edge_agents["maintenance"];
let task_id = ai::create_task(agent_id, "analysis", "Analyze failure prediction", "high");
return {
"prediction": prediction,
"agent_id": agent_id,
"task_id": task_id,
"device_id": device_id
};
}
fn monitor_production_efficiency() {
let efficiency_report = {
"timestamp": chain::get_block_timestamp(1),
"production_lines_active": self.production_lines.length,
"overall_efficiency": 0.0,
"quality_rate": 0.0,
"downtime_minutes": 0,
"energy_consumption_kwh": 0.0,
"maintenance_events": 0,
"production_metrics": []
};
for production_line in self.production_lines {
let line_metrics = self.analyze_production_line(production_line.line_id);
efficiency_report.overall_efficiency = efficiency_report.overall_efficiency + line_metrics.efficiency;
efficiency_report.quality_rate = efficiency_report.quality_rate + line_metrics.quality_rate;
efficiency_report.downtime_minutes = efficiency_report.downtime_minutes + line_metrics.downtime;
efficiency_report.energy_consumption_kwh = efficiency_report.energy_consumption_kwh + line_metrics.energy_usage;
efficiency_report.maintenance_events = efficiency_report.maintenance_events + line_metrics.maintenance_count;
efficiency_report.production_metrics.push(line_metrics);
}
efficiency_report.overall_efficiency = efficiency_report.overall_efficiency / efficiency_report.production_lines_active;
efficiency_report.quality_rate = efficiency_report.quality_rate / efficiency_report.production_lines_active;
let optimizations = self.generate_production_optimizations(efficiency_report);
for optimization in optimizations {
self.implement_production_optimization(optimization);
}
iot::sync_device_data_to_cloud("manufacturing_controller", efficiency_report);
log::info("iot", {
"production_efficiency_report": efficiency_report,
"optimizations_applied": optimizations.length,
"quality_improvement_target": 0.5
});
}
fn analyze_production_line(line_id: string) -> any {
return {
"line_id": line_id,
"efficiency": 92.5,
"quality_rate": 99.2,
"downtime": 15,
"energy_usage": 1250.5,
"maintenance_count": 2,
"bottleneck_identified": "packaging_stage",
"optimization_opportunities": ["increase_conveyor_speed", "optimize_robot_paths", "reduce_setup_time"],
"predicted_failures": [
{ "component": "motor_3", "probability": 0.15, "time_to_failure": 168 }
]
};
}
fn generate_production_optimizations(report: any) -> list<any> {
let optimizations = [];
if (report.overall_efficiency < 95) {
optimizations.push({
"optimization_type": "production_efficiency",
"target_lines": ["assembly_line_1", "packaging_line"],
"actions": ["optimize_workflows", "reduce_changeover_time"],
"expected_improvement": 3.5,
"implementation_cost": 15000,
"roi_months": 4
});
}
if (report.quality_rate < 99.5) {
optimizations.push({
"optimization_type": "quality_improvement",
"target_lines": ["quality_control"],
"actions": ["upgrade_vision_systems", "implement_ai_inspection"],
"expected_improvement": 1.2,
"implementation_cost": 75000,
"roi_months": 8
});
}
if (report.energy_consumption_kwh > 2000) {
optimizations.push({
"optimization_type": "energy_efficiency",
"target_lines": ["all_lines"],
"actions": ["implement_energy_monitoring", "optimize_motor_usage"],
"expected_savings": 850,
"implementation_cost": 25000,
"roi_months": 3
});
}
return optimizations;
}
fn implement_production_optimization(optimization: any) {
if (optimization.optimization_type == "production_efficiency") {
for target_line in optimization.target_lines {
let cmd = {
"command": "optimize_workflow",
"parameters": {
"optimization_type": "bottleneck_elimination",
"target_stages": ["assembly", "packaging"],
"expected_efficiency_gain": optimization.expected_improvement
}
};
iot::send_actuator_command(target_line, "workflow_optimization", cmd);
}
} else if (optimization.optimization_type == "quality_improvement") {
let cmd = {
"command": "upgrade_system",
"parameters": {
"upgrade_type": "ai_enhanced_inspection",
"components": ["vision_sensors", "processing_algorithms"],
"expected_accuracy_improvement": optimization.expected_improvement
}
};
iot::send_actuator_command("quality_control_system", "system_upgrade", cmd);
} else if (optimization.optimization_type == "energy_efficiency") {
let cmd = {
"command": "enable_energy_optimization",
"parameters": {
"monitoring_enabled": true,
"optimization_algorithm": "predictive_control",
"target_savings": optimization.expected_savings
}
};
for target_line in optimization.target_lines {
iot::send_actuator_command(target_line, "energy_optimization", cmd);
}
}
log::info("iot", {
"optimization_implemented": optimization.optimization_type,
"target_lines": optimization.target_lines,
"implementation_cost": optimization.implementation_cost
});
}
fn perform_quality_assurance() {
let quality_report = {
"timestamp": chain::get_block_timestamp(1),
"products_inspected": 10000,
"defects_detected": 80,
"defect_rate": 0.8,
"quality_score": 99.1,
"inspection_accuracy": 99.2,
"false_positive_rate": 0.3,
"defect_categories": {},
"quality_trends": [],
"recommendations": []
};
if (quality_report.defect_rate > 1.0) {
quality_report.recommendations.push("Increase inspection frequency for high-defect components");
}
if (quality_report.false_positive_rate > 0.5) {
quality_report.recommendations.push("Retrain AI models to reduce false positives");
}
iot::sync_device_data_to_cloud("quality_monitor", quality_report);
log::info("iot", {
"quality_assurance_report": quality_report,
"defect_rate": quality_report.defect_rate,
"quality_score": quality_report.quality_score,
"recommendations_count": quality_report.recommendations.length
});
}
fn manage_supply_chain() {
let supply_chain_report = {
"timestamp": chain::get_block_timestamp(1),
"suppliers_active": 25,
"components_available": 4500,
"inventory_levels": {},
"delivery_delays": 2,
"quality_issues": 1,
"cost_variances": 1250.50,
"supply_risk_score": 0.35,
"recommendations": []
};
if (supply_chain_report.supply_risk_score > 0.7) {
supply_chain_report.recommendations.push("Diversify supplier base to reduce risk");
}
if (supply_chain_report.delivery_delays > 5) {
supply_chain_report.recommendations.push("Implement supplier performance monitoring");
}
log::info("iot", {
"supply_chain_report": supply_chain_report,
"supply_risk_score": supply_chain_report.supply_risk_score,
"total_inventory": supply_chain_report.components_available
});
}
fn generate_manufacturing_report() {
let manufacturing_report = {
"report_type": "manufacturing_performance",
"report_period": "daily",
"generated_at": chain::get_block_timestamp(1),
"production_lines": self.production_lines.length,
"quality_systems": self.quality_control_systems.length,
"edge_nodes": self.edge_processing_nodes.length,
"production_metrics": {
"total_output": 12500,
"efficiency_rate": 94.2,
"quality_rate": 98.7,
"downtime_hours": 2.5,
"energy_consumption": 18500.5
},
"quality_metrics": {
"inspection_accuracy": 99.1,
"defect_rate": 0.8,
"false_positive_rate": 0.3,
"quality_improvements": 12
},
"maintenance_metrics": {
"predictive_maintenance_accuracy": 92.5,
"preventive_maintenance_events": 8,
"emergency_repairs": 1,
"maintenance_cost_savings": 25000.0
},
"supply_chain_metrics": {
"supplier_performance": 89.2,
"inventory_efficiency": 87.5,
"supply_risk_score": 0.35,
"cost_savings": 18500.0
},
"cost_analysis": {
"production_cost_per_unit": 12.50,
"energy_cost_savings": 1250.75,
"maintenance_cost_savings": 25000.0,
"quality_improvement_savings": 8750.0,
"total_cost_savings": 44750.75
},
"recommendations": []
};
if (manufacturing_report.production_metrics.efficiency_rate < 95) {
manufacturing_report.recommendations.push("Implement lean manufacturing principles");
}
if (manufacturing_report.quality_metrics.defect_rate > 1.0) {
manufacturing_report.recommendations.push("Enhance quality control processes");
}
if (manufacturing_report.maintenance_metrics.predictive_maintenance_accuracy < 90) {
manufacturing_report.recommendations.push("Upgrade predictive maintenance algorithms");
}
iot::sync_device_data_to_cloud("manufacturing_controller", manufacturing_report);
log::info("iot", {
"manufacturing_report_generated": manufacturing_report.generated_at,
"total_output": manufacturing_report.production_metrics.total_output,
"efficiency_rate": manufacturing_report.production_metrics.efficiency_rate,
"total_savings": manufacturing_report.cost_analysis.total_cost_savings,
"recommendations_count": manufacturing_report.recommendations.length
});
}
}
fn main() {
log::info("main", { "message": "Starting Phase 6: IoT & Edge Computing (bond, ai, iot, mold)" });
let smart_city = SmartCityEdgeManager::new();
smart_city.initialize_smart_city_infrastructure();
let manufacturing = ManufacturingEdgeController::new();
manufacturing.initialize_manufacturing_edge();
smart_city.process_real_time_city_data();
smart_city.optimize_city_operations();
smart_city.handle_city_emergencies();
smart_city.generate_city_performance_report();
manufacturing.monitor_production_efficiency();
manufacturing.perform_quality_assurance();
manufacturing.manage_supply_chain();
manufacturing.generate_manufacturing_report();
log::info("main", { "message": "Phase 6 IoT & Edge Computing examples completed successfully!" });
log::info("summary", {
"message": "IoT & Edge Computing Integration Summary",
"bond_patterns": ["iot_to_ai", "ai_to_cloud"],
"ai_agents": ["traffic", "environmental", "safety", "quality", "maintenance"],
"capabilities": [
"Device registration and management",
"Real-time sensor data processing",
"Edge AI inference and analytics",
"Predictive maintenance systems",
"Quality control automation",
"Traffic and transportation optimization",
"Environmental monitoring networks",
"Emergency response coordination",
"Supply chain optimization",
"Energy management systems",
"Cloud integration and data synchronization",
"Mold-style agent configs for edge AI",
"Bond orchestration for IoT-AI-DB flows"
]
});
}