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docs/developer/architecture-overview.md

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+# Architecture Overview
+
+## Overview
+
+The WiFi-DensePose system is a distributed, microservices-based architecture that transforms WiFi Channel State Information (CSI) into real-time human pose estimation. This document provides a comprehensive overview of the system architecture, component interactions, and design principles.
+
+## Table of Contents
+
+1. [System Architecture](#system-architecture)
+2. [Core Components](#core-components)
+3. [Data Flow](#data-flow)
+4. [Processing Pipeline](#processing-pipeline)
+5. [API Architecture](#api-architecture)
+6. [Storage Architecture](#storage-architecture)
+7. [Security Architecture](#security-architecture)
+8. [Deployment Architecture](#deployment-architecture)
+9. [Scalability and Performance](#scalability-and-performance)
+10. [Design Principles](#design-principles)
+
+## System Architecture
+
+### High-Level Architecture
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│                        WiFi-DensePose System                    │
+├─────────────────────────────────────────────────────────────────┤
+│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐  │
+│  │   Client Apps   │  │   Web Dashboard │  │  Mobile Apps    │  │
+│  └─────────────────┘  └─────────────────┘  └─────────────────┘  │
+├─────────────────────────────────────────────────────────────────┤
+│                        API Gateway                              │
+├─────────────────────────────────────────────────────────────────┤
+│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐  │
+│  │   REST API      │  │  WebSocket API  │  │   MQTT Broker   │  │
+│  └─────────────────┘  └─────────────────┘  └─────────────────┘  │
+├─────────────────────────────────────────────────────────────────┤
+│                      Processing Layer                           │
+│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐  │
+│  │ Pose Estimation │  │    Tracking     │  │   Analytics     │  │
+│  │    Service      │  │    Service      │  │    Service      │  │
+│  └─────────────────┘  └─────────────────┘  └─────────────────┘  │
+├─────────────────────────────────────────────────────────────────┤
+│                       Data Layer                                │
+│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐  │
+│  │ CSI Processor   │  │  Data Pipeline  │  │  Model Manager  │  │
+│  └─────────────────┘  └─────────────────┘  └─────────────────┘  │
+├─────────────────────────────────────────────────────────────────┤
+│                     Hardware Layer                              │
+│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────┐  │
+│  │  WiFi Routers   │  │ Processing Unit │  │   GPU Cluster   │  │
+│  │   (CSI Data)    │  │   (CPU/Memory)  │  │  (Neural Net)   │  │
+│  └─────────────────┘  └─────────────────┘  └─────────────────┘  │
+└─────────────────────────────────────────────────────────────────┘
+```
+
+### Component Interaction Diagram
+
+```
+┌─────────────┐    CSI Data    ┌─────────────┐    Features    ┌─────────────┐
+│   Router    │ ──────────────▶│ CSI         │ ──────────────▶│ Feature     │
+│   Network   │                │ Processor   │                │ Extractor   │
+└─────────────┘                └─────────────┘                └─────────────┘
+                                       │                              │
+                                       ▼                              ▼
+┌─────────────┐    Poses       ┌─────────────┐    Inference   ┌─────────────┐
+│   Client    │ ◀──────────────│ Pose        │ ◀──────────────│ Neural      │
+│ Applications│                │ Tracker     │                │ Network     │
+└─────────────┘                └─────────────┘                └─────────────┘
+       │                               │                              │
+       ▼                               ▼                              ▼
+┌─────────────┐    Events      ┌─────────────┐    Models      ┌─────────────┐
+│ Alert       │ ◀──────────────│ Analytics   │ ◀──────────────│ Model       │
+│ System      │                │ Engine      │                │ Manager     │
+└─────────────┘                └─────────────┘                └─────────────┘
+```
+
+## Core Components
+
+### 1. CSI Data Processor
+
+**Purpose**: Receives and processes raw Channel State Information from WiFi routers.
+
+**Key Features**:
+- Real-time CSI data ingestion from multiple routers
+- Signal preprocessing and noise reduction
+- Phase sanitization and amplitude normalization
+- Multi-antenna data fusion
+
+**Implementation**: [`src/hardware/csi_processor.py`](../../src/hardware/csi_processor.py)
+
+```python
+class CSIProcessor:
+    """Processes raw CSI data from WiFi routers."""
+    
+    def __init__(self, config: CSIConfig):
+        self.routers = self._initialize_routers(config.routers)
+        self.buffer = CircularBuffer(config.buffer_size)
+        self.preprocessor = CSIPreprocessor()
+    
+    async def process_stream(self) -> AsyncGenerator[CSIData, None]:
+        """Process continuous CSI data stream."""
+        async for raw_data in self._receive_csi_data():
+            processed_data = self.preprocessor.process(raw_data)
+            yield processed_data
+```
+
+### 2. Neural Network Service
+
+**Purpose**: Performs pose estimation using deep learning models.
+
+**Key Features**:
+- DensePose model inference
+- Batch processing optimization
+- GPU acceleration support
+- Model versioning and hot-swapping
+
+**Implementation**: [`src/neural_network/inference.py`](../../src/neural_network/inference.py)
+
+```python
+class PoseEstimationService:
+    """Neural network service for pose estimation."""
+    
+    def __init__(self, model_config: ModelConfig):
+        self.model = self._load_model(model_config.model_path)
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.batch_processor = BatchProcessor(model_config.batch_size)
+    
+    async def estimate_poses(self, csi_features: CSIFeatures) -> List[PoseEstimation]:
+        """Estimate human poses from CSI features."""
+        with torch.no_grad():
+            predictions = self.model(csi_features.to(self.device))
+            return self._postprocess_predictions(predictions)
+```
+
+### 3. Tracking Service
+
+**Purpose**: Maintains temporal consistency and person identity across frames.
+
+**Key Features**:
+- Multi-object tracking with Kalman filters
+- Person re-identification
+- Track lifecycle management
+- Trajectory smoothing
+
+**Implementation**: [`src/tracking/tracker.py`](../../src/tracking/tracker.py)
+
+```python
+class PersonTracker:
+    """Tracks multiple persons across time."""
+    
+    def __init__(self, tracking_config: TrackingConfig):
+        self.tracks = {}
+        self.track_id_counter = 0
+        self.kalman_filter = KalmanFilter()
+        self.reid_model = ReIDModel()
+    
+    def update(self, detections: List[PoseDetection]) -> List[TrackedPose]:
+        """Update tracks with new detections."""
+        matched_tracks, unmatched_detections = self._associate_detections(detections)
+        self._update_matched_tracks(matched_tracks)
+        self._create_new_tracks(unmatched_detections)
+        return self._get_active_tracks()
+```
+
+### 4. API Gateway
+
+**Purpose**: Provides unified access to system functionality through REST and WebSocket APIs.
+
+**Key Features**:
+- Authentication and authorization
+- Rate limiting and throttling
+- Request routing and load balancing
+- API versioning
+
+**Implementation**: [`src/api/main.py`](../../src/api/main.py)
+
+```python
+from fastapi import FastAPI, Depends
+from fastapi.middleware.cors import CORSMiddleware
+
+app = FastAPI(
+    title="WiFi-DensePose API",
+    version="1.0.0",
+    description="Privacy-preserving human pose estimation using WiFi signals"
+)
+
+# Middleware
+app.add_middleware(CORSMiddleware, **get_cors_config())
+app.add_middleware(RateLimitMiddleware)
+app.add_middleware(AuthenticationMiddleware)
+
+# Routers
+app.include_router(pose_router, prefix="/api/v1/pose")
+app.include_router(system_router, prefix="/api/v1/system")
+app.include_router(analytics_router, prefix="/api/v1/analytics")
+```
+
+### 5. Analytics Engine
+
+**Purpose**: Processes pose data to generate insights and trigger alerts.
+
+**Key Features**:
+- Real-time event detection (falls, intrusions)
+- Statistical analysis and reporting
+- Domain-specific analytics (healthcare, retail, security)
+- Machine learning-based pattern recognition
+
+**Implementation**: [`src/analytics/engine.py`](../../src/analytics/engine.py)
+
+```python
+class AnalyticsEngine:
+    """Processes pose data for insights and alerts."""
+    
+    def __init__(self, domain_config: DomainConfig):
+        self.domain = domain_config.domain
+        self.event_detectors = self._load_event_detectors(domain_config)
+        self.alert_manager = AlertManager(domain_config.alerts)
+    
+    async def process_poses(self, poses: List[TrackedPose]) -> AnalyticsResult:
+        """Process poses and generate analytics."""
+        events = []
+        for detector in self.event_detectors:
+            detected_events = await detector.detect(poses)
+            events.extend(detected_events)
+        
+        await self.alert_manager.process_events(events)
+        return AnalyticsResult(events=events, metrics=self._calculate_metrics(poses))
+```
+
+## Data Flow
+
+### Real-Time Processing Pipeline
+
+```
+1. CSI Data Acquisition
+   ┌─────────────┐
+   │   Router 1  │ ──┐
+   └─────────────┘   │
+   ┌─────────────┐   │    ┌─────────────┐
+   │   Router 2  │ ──┼───▶│ CSI Buffer  │
+   └─────────────┘   │    └─────────────┘
+   ┌─────────────┐   │           │
+   │   Router N  │ ──┘           ▼
+   └─────────────┘       ┌─────────────┐
+                         │ Preprocessor│
+                         └─────────────┘
+                                │
+2. Feature Extraction           ▼
+   ┌─────────────┐       ┌─────────────┐
+   │   Phase     │ ◀─────│ Feature     │
+   │ Sanitizer   │       │ Extractor   │
+   └─────────────┘       └─────────────┘
+          │                     │
+          ▼                     ▼
+   ┌─────────────┐       ┌─────────────┐
+   │ Amplitude   │       │ Frequency   │
+   │ Processor   │       │ Analyzer    │
+   └─────────────┘       └─────────────┘
+          │                     │
+          └──────┬──────────────┘
+                 ▼
+3. Neural Network Inference
+   ┌─────────────┐
+   │ DensePose   │
+   │   Model     │
+   └─────────────┘
+          │
+          ▼
+   ┌─────────────┐
+   │ Pose        │
+   │ Decoder     │
+   └─────────────┘
+          │
+4. Tracking and Analytics      ▼
+   ┌─────────────┐       ┌─────────────┐
+   │ Person      │ ◀─────│ Raw Pose    │
+   │ Tracker     │       │ Detections  │
+   └─────────────┘       └─────────────┘
+          │
+          ▼
+   ┌─────────────┐
+   │ Analytics   │
+   │ Engine      │
+   └─────────────┘
+          │
+5. Output and Storage          ▼
+   ┌─────────────┐       ┌─────────────┐
+   │ WebSocket   │ ◀─────│ Tracked     │
+   │ Streams     │       │ Poses       │
+   └─────────────┘       └─────────────┘
+          │                     │
+          ▼                     ▼
+   ┌─────────────┐       ┌─────────────┐
+   │ Client      │       │ Database    │
+   │ Applications│       │ Storage     │
+   └─────────────┘       └─────────────┘
+```
+
+### Data Models
+
+#### CSI Data Structure
+
+```python
+@dataclass
+class CSIData:
+    """Channel State Information data structure."""
+    timestamp: datetime
+    router_id: str
+    antenna_pairs: List[AntennaPair]
+    subcarriers: List[SubcarrierData]
+    metadata: CSIMetadata
+
+@dataclass
+class SubcarrierData:
+    """Individual subcarrier information."""
+    frequency: float
+    amplitude: complex
+    phase: float
+    snr: float
+```
+
+#### Pose Data Structure
+
+```python
+@dataclass
+class PoseEstimation:
+    """Human pose estimation result."""
+    person_id: Optional[int]
+    confidence: float
+    bounding_box: BoundingBox
+    keypoints: List[Keypoint]
+    dense_pose: Optional[DensePoseResult]
+    timestamp: datetime
+
+@dataclass
+class TrackedPose:
+    """Tracked pose with temporal information."""
+    track_id: int
+    pose: PoseEstimation
+    velocity: Vector2D
+    track_age: int
+    track_confidence: float
+```
+
+## Processing Pipeline
+
+### 1. CSI Preprocessing
+
+```python
+class CSIPreprocessor:
+    """Preprocesses raw CSI data for neural network input."""
+    
+    def __init__(self, config: PreprocessingConfig):
+        self.phase_sanitizer = PhaseSanitizer()
+        self.amplitude_normalizer = AmplitudeNormalizer()
+        self.noise_filter = NoiseFilter(config.filter_params)
+    
+    def process(self, raw_csi: RawCSIData) -> ProcessedCSIData:
+        """Process raw CSI data."""
+        # Phase unwrapping and sanitization
+        sanitized_phase = self.phase_sanitizer.sanitize(raw_csi.phase)
+        
+        # Amplitude normalization
+        normalized_amplitude = self.amplitude_normalizer.normalize(raw_csi.amplitude)
+        
+        # Noise filtering
+        filtered_data = self.noise_filter.filter(sanitized_phase, normalized_amplitude)
+        
+        return ProcessedCSIData(
+            phase=filtered_data.phase,
+            amplitude=filtered_data.amplitude,
+            timestamp=raw_csi.timestamp,
+            metadata=raw_csi.metadata
+        )
+```
+
+### 2. Feature Extraction
+
+```python
+class FeatureExtractor:
+    """Extracts features from processed CSI data."""
+    
+    def __init__(self, config: FeatureConfig):
+        self.window_size = config.window_size
+        self.feature_types = config.feature_types
+        self.pca_reducer = PCAReducer(config.pca_components)
+    
+    def extract_features(self, csi_data: ProcessedCSIData) -> CSIFeatures:
+        """Extract features for neural network input."""
+        features = {}
+        
+        if 'amplitude' in self.feature_types:
+            features['amplitude'] = self._extract_amplitude_features(csi_data)
+        
+        if 'phase' in self.feature_types:
+            features['phase'] = self._extract_phase_features(csi_data)
+        
+        if 'doppler' in self.feature_types:
+            features['doppler'] = self._extract_doppler_features(csi_data)
+        
+        # Dimensionality reduction
+        reduced_features = self.pca_reducer.transform(features)
+        
+        return CSIFeatures(
+            features=reduced_features,
+            timestamp=csi_data.timestamp,
+            feature_types=self.feature_types
+        )
+```
+
+### 3. Neural Network Architecture
+
+```python
+class DensePoseNet(nn.Module):
+    """DensePose neural network for WiFi-based pose estimation."""
+    
+    def __init__(self, config: ModelConfig):
+        super().__init__()
+        self.backbone = self._build_backbone(config.backbone)
+        self.feature_pyramid = FeaturePyramidNetwork(config.fpn)
+        self.pose_head = PoseEstimationHead(config.pose_head)
+        self.dense_pose_head = DensePoseHead(config.dense_pose_head)
+    
+    def forward(self, csi_features: torch.Tensor) -> Dict[str, torch.Tensor]:
+        """Forward pass through the network."""
+        # Feature extraction
+        backbone_features = self.backbone(csi_features)
+        pyramid_features = self.feature_pyramid(backbone_features)
+        
+        # Pose estimation
+        pose_predictions = self.pose_head(pyramid_features)
+        dense_pose_predictions = self.dense_pose_head(pyramid_features)
+        
+        return {
+            'poses': pose_predictions,
+            'dense_poses': dense_pose_predictions
+        }
+```
+
+## API Architecture
+
+### REST API Design
+
+The REST API follows RESTful principles with clear resource hierarchies:
+
+```
+/api/v1/
+├── auth/
+│   ├── token          # POST: Get authentication token
+│   └── verify         # POST: Verify token validity
+├── system/
+│   ├── status         # GET: System health status
+│   ├── start          # POST: Start pose estimation
+│   ├── stop           # POST: Stop pose estimation
+│   └── diagnostics    # GET: System diagnostics
+├── pose/
+│   ├── latest         # GET: Latest pose data
+│   ├── history        # GET: Historical pose data
+│   └── query          # POST: Complex pose queries
+├── config/
+│   └── [resource]     # GET/PUT: Configuration management
+└── analytics/
+    ├── healthcare     # GET: Healthcare analytics
+    ├── retail         # GET: Retail analytics
+    └── security       # GET: Security analytics
+```
+
+### WebSocket API Design
+
+```python
+class WebSocketManager:
+    """Manages WebSocket connections and subscriptions."""
+    
+    def __init__(self):
+        self.connections: Dict[str, WebSocket] = {}
+        self.subscriptions: Dict[str, Set[str]] = {}
+    
+    async def handle_connection(self, websocket: WebSocket, client_id: str):
+        """Handle new WebSocket connection."""
+        await websocket.accept()
+        self.connections[client_id] = websocket
+        
+        try:
+            async for message in websocket.iter_text():
+                await self._handle_message(client_id, json.loads(message))
+        except WebSocketDisconnect:
+            self._cleanup_connection(client_id)
+    
+    async def broadcast_pose_update(self, pose_data: TrackedPose):
+        """Broadcast pose updates to subscribed clients."""
+        message = {
+            'type': 'pose_update',
+            'data': pose_data.to_dict(),
+            'timestamp': datetime.utcnow().isoformat()
+        }
+        
+        for client_id in self.subscriptions.get('pose_updates', set()):
+            if client_id in self.connections:
+                await self.connections[client_id].send_text(json.dumps(message))
+```
+
+## Storage Architecture
+
+### Database Design
+
+#### Time-Series Data (PostgreSQL + TimescaleDB)
+
+```sql
+-- Pose data table with time-series optimization
+CREATE TABLE pose_data (
+    id BIGSERIAL PRIMARY KEY,
+    timestamp TIMESTAMPTZ NOT NULL,
+    frame_id BIGINT NOT NULL,
+    person_id INTEGER,
+    track_id INTEGER,
+    confidence REAL NOT NULL,
+    bounding_box JSONB NOT NULL,
+    keypoints JSONB NOT NULL,
+    dense_pose JSONB,
+    metadata JSONB,
+    environment_id VARCHAR(50) NOT NULL
+);
+
+-- Convert to hypertable for time-series optimization
+SELECT create_hypertable('pose_data', 'timestamp');
+
+-- Create indexes for common queries
+CREATE INDEX idx_pose_data_timestamp ON pose_data (timestamp DESC);
+CREATE INDEX idx_pose_data_person_id ON pose_data (person_id, timestamp DESC);
+CREATE INDEX idx_pose_data_environment ON pose_data (environment_id, timestamp DESC);
+```
+
+#### Configuration Storage (PostgreSQL)
+
+```sql
+-- System configuration
+CREATE TABLE system_config (
+    id SERIAL PRIMARY KEY,
+    domain VARCHAR(50) NOT NULL,
+    environment_id VARCHAR(50) NOT NULL,
+    config_data JSONB NOT NULL,
+    created_at TIMESTAMPTZ DEFAULT NOW(),
+    updated_at TIMESTAMPTZ DEFAULT NOW(),
+    UNIQUE(domain, environment_id)
+);
+
+-- Model metadata
+CREATE TABLE model_metadata (
+    id SERIAL PRIMARY KEY,
+    model_name VARCHAR(100) NOT NULL,
+    model_version VARCHAR(20) NOT NULL,
+    model_path TEXT NOT NULL,
+    config JSONB NOT NULL,
+    performance_metrics JSONB,
+    created_at TIMESTAMPTZ DEFAULT NOW(),
+    UNIQUE(model_name, model_version)
+);
+```
+
+### Caching Strategy (Redis)
+
+```python
+class CacheManager:
+    """Manages Redis caching for frequently accessed data."""
+    
+    def __init__(self, redis_client: Redis):
+        self.redis = redis_client
+        self.default_ttl = 300  # 5 minutes
+    
+    async def cache_pose_data(self, pose_data: TrackedPose, ttl: int = None):
+        """Cache pose data with automatic expiration."""
+        key = f"pose:latest:{pose_data.track_id}"
+        value = json.dumps(pose_data.to_dict(), default=str)
+        await self.redis.setex(key, ttl or self.default_ttl, value)
+    
+    async def get_cached_poses(self, track_ids: List[int]) -> List[TrackedPose]:
+        """Retrieve cached pose data for multiple tracks."""
+        keys = [f"pose:latest:{track_id}" for track_id in track_ids]
+        cached_data = await self.redis.mget(keys)
+        
+        poses = []
+        for data in cached_data:
+            if data:
+                pose_dict = json.loads(data)
+                poses.append(TrackedPose.from_dict(pose_dict))
+        
+        return poses
+```
+
+## Security Architecture
+
+### Authentication and Authorization
+
+```python
+class SecurityManager:
+    """Handles authentication and authorization."""
+    
+    def __init__(self, config: SecurityConfig):
+        self.jwt_secret = config.jwt_secret
+        self.jwt_algorithm = config.jwt_algorithm
+        self.token_expiry = config.token_expiry
+    
+    def create_access_token(self, user_data: dict) -> str:
+        """Create JWT access token."""
+        payload = {
+            'sub': user_data['username'],
+            'exp': datetime.utcnow() + timedelta(hours=self.token_expiry),
+            'iat': datetime.utcnow(),
+            'permissions': user_data.get('permissions', [])
+        }
+        return jwt.encode(payload, self.jwt_secret, algorithm=self.jwt_algorithm)
+    
+    def verify_token(self, token: str) -> dict:
+        """Verify and decode JWT token."""
+        try:
+            payload = jwt.decode(token, self.jwt_secret, algorithms=[self.jwt_algorithm])
+            return payload
+        except jwt.ExpiredSignatureError:
+            raise HTTPException(status_code=401, detail="Token expired")
+        except jwt.InvalidTokenError:
+            raise HTTPException(status_code=401, detail="Invalid token")
+```
+
+### Data Privacy
+
+```python
+class PrivacyManager:
+    """Manages data privacy and anonymization."""
+    
+    def __init__(self, config: PrivacyConfig):
+        self.anonymization_enabled = config.anonymization_enabled
+        self.data_retention_days = config.data_retention_days
+        self.encryption_key = config.encryption_key
+    
+    def anonymize_pose_data(self, pose_data: TrackedPose) -> TrackedPose:
+        """Anonymize pose data for privacy protection."""
+        if not self.anonymization_enabled:
+            return pose_data
+        
+        # Remove or hash identifying information
+        anonymized_data = pose_data.copy()
+        anonymized_data.track_id = self._hash_track_id(pose_data.track_id)
+        
+        # Apply differential privacy to keypoints
+        anonymized_data.pose.keypoints = self._add_noise_to_keypoints(
+            pose_data.pose.keypoints
+        )
+        
+        return anonymized_data
+```
+
+## Deployment Architecture
+
+### Container Architecture
+
+```yaml
+# docker-compose.yml
+version: '3.8'
+services:
+  wifi-densepose-api:
+    build: .
+    ports:
+      - "8000:8000"
+    environment:
+      - DATABASE_URL=postgresql://user:pass@postgres:5432/wifi_densepose
+      - REDIS_URL=redis://redis:6379/0
+    depends_on:
+      - postgres
+      - redis
+      - neural-network
+    volumes:
+      - ./data:/app/data
+      - ./models:/app/models
+  
+  neural-network:
+    build: ./neural_network
+    runtime: nvidia
+    environment:
+      - CUDA_VISIBLE_DEVICES=0
+    volumes:
+      - ./models:/app/models
+  
+  postgres:
+    image: timescale/timescaledb:latest-pg14
+    environment:
+      - POSTGRES_DB=wifi_densepose
+      - POSTGRES_USER=user
+      - POSTGRES_PASSWORD=password
+    volumes:
+      - postgres_data:/var/lib/postgresql/data
+  
+  redis:
+    image: redis:7-alpine
+    volumes:
+      - redis_data:/data
+
+volumes:
+  postgres_data:
+  redis_data:
+```
+
+### Kubernetes Deployment
+
+```yaml
+# k8s/deployment.yaml
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: wifi-densepose-api
+spec:
+  replicas: 3
+  selector:
+    matchLabels:
+      app: wifi-densepose-api
+  template:
+    metadata:
+      labels:
+        app: wifi-densepose-api
+    spec:
+      containers:
+      - name: api
+        image: wifi-densepose:latest
+        ports:
+        - containerPort: 8000
+        env:
+        - name: DATABASE_URL
+          valueFrom:
+            secretKeyRef:
+              name: database-secret
+              key: url
+        resources:
+          requests:
+            memory: "2Gi"
+            cpu: "1000m"
+          limits:
+            memory: "4Gi"
+            cpu: "2000m"
+```
+
+## Scalability and Performance
+
+### Horizontal Scaling
+
+```python
+class LoadBalancer:
+    """Distributes processing load across multiple instances."""
+    
+    def __init__(self, config: LoadBalancerConfig):
+        self.processing_nodes = config.processing_nodes
+        self.load_balancing_strategy = config.strategy
+        self.health_checker = HealthChecker()
+    
+    async def distribute_csi_data(self, csi_data: CSIData) -> str:
+        """Distribute CSI data to available processing nodes."""
+        available_nodes = await self.health_checker.get_healthy_nodes()
+        
+        if self.load_balancing_strategy == 'round_robin':
+            node = self._round_robin_selection(available_nodes)
+        elif self.load_balancing_strategy == 'least_loaded':
+            node = await self._least_loaded_selection(available_nodes)
+        else:
+            node = random.choice(available_nodes)
+        
+        await self._send_to_node(node, csi_data)
+        return node.id
+```
+
+### Performance Optimization
+
+```python
+class PerformanceOptimizer:
+    """Optimizes system performance based on runtime metrics."""
+    
+    def __init__(self, config: OptimizationConfig):
+        self.metrics_collector = MetricsCollector()
+        self.auto_scaling_enabled = config.auto_scaling_enabled
+        self.optimization_interval = config.optimization_interval
+    
+    async def optimize_processing_pipeline(self):
+        """Optimize processing pipeline based on current metrics."""
+        metrics = await self.metrics_collector.get_current_metrics()
+        
+        # Adjust batch size based on GPU utilization
+        if metrics.gpu_utilization < 0.7:
+            await self._increase_batch_size()
+        elif metrics.gpu_utilization > 0.9:
+            await self._decrease_batch_size()
+        
+        # Scale processing nodes based on queue length
+        if metrics.processing_queue_length > 100:
+            await self._scale_up_processing_nodes()
+        elif metrics.processing_queue_length < 10:
+            await self._scale_down_processing_nodes()
+```
+
+## Design Principles
+
+### 1. Modularity and Separation of Concerns
+
+- Each component has a single, well-defined responsibility
+- Clear interfaces between components
+- Pluggable architecture for easy component replacement
+
+### 2. Scalability
+
+- Horizontal scaling support through microservices
+- Stateless service design where possible
+- Efficient resource utilization and load balancing
+
+### 3. Reliability and Fault Tolerance
+
+- Graceful degradation under failure conditions
+- Circuit breaker patterns for external dependencies
+- Comprehensive error handling and recovery mechanisms
+
+### 4. Performance
+
+- Optimized data structures and algorithms
+- Efficient memory management and garbage collection
+- GPU acceleration for compute-intensive operations
+
+### 5. Security and Privacy
+
+- Defense in depth security model
+- Data encryption at rest and in transit
+- Privacy-preserving data processing techniques
+
+### 6. Observability
+
+- Comprehensive logging and monitoring
+- Distributed tracing for request flow analysis
+- Performance metrics and alerting
+
+### 7. Maintainability
+
+- Clean code principles and consistent coding standards
+- Comprehensive documentation and API specifications
+- Automated testing and continuous integration
+
+---
+
+This architecture overview provides the foundation for understanding the WiFi-DensePose system. For implementation details, see:
+
+- [API Architecture](../api/rest-endpoints.md)
+- [Neural Network Architecture](../../plans/phase2-architecture/neural-network-architecture.md)
+- [Hardware Integration](../../plans/phase2-architecture/hardware-integration.md)
+- [Deployment Guide](deployment-guide.md)