Edge Computing - ruvnet/ruv-FANN GitHub Wiki
Edge computing represents a distributed computing paradigm that brings computation and data storage closer to the sources of data. This approach reduces latency, improves response times, and enables real-time processing for applications that require immediate decision-making.
Cloud Layer (Central Processing)
↓ Coordination & Analytics
Regional Edge Nodes (Fog Computing)
↓ Load Balancing & Aggregation
Local Edge Devices (Edge Computing)
↓ Real-time Processing
IoT Sensors/Devices (Data Sources)
- Compute Resources: CPU, GPU, specialized processors (NPU, TPU)
- Storage: Local caching, temporary data storage
- Networking: 5G, WiFi 6, satellite connectivity
- Security: Hardware security modules, encryption
- Protocol translation and data aggregation
- Local decision-making capabilities
- Device management and orchestration
- Security enforcement point
- Centralized monitoring and control
- Application deployment and updates
- Resource allocation and optimization
- Performance analytics
- Device Tier: IoT sensors, smartphones, embedded systems
- Edge Tier: Edge servers, gateways, micro data centers
- Cloud Tier: Central cloud infrastructure
- Peer-to-peer connectivity between edge nodes
- Distributed processing and data sharing
- Fault tolerance through redundancy
- Dynamic resource allocation
- Limited CPU cores and clock speeds
- Reduced memory capacity (2-32GB typical)
- Power consumption constraints
- Thermal management challenges
- Limited local storage (32GB-2TB)
- SSD preferred for durability
- Data retention policies required
- Compression and deduplication needed
- Variable connectivity quality
- Bandwidth limitations (1Mbps-10Gbps)
- Network congestion management
- Intermittent connectivity handling
// Edge resource-aware processing
class EdgeProcessor {
constructor(constraints) {
this.maxMemory = constraints.memory;
this.maxCPU = constraints.cpu;
this.powerBudget = constraints.power;
}
processData(data) {
if (this.getMemoryUsage() > this.maxMemory * 0.8) {
return this.offloadToCloud(data);
}
return this.processLocally(data);
}
offloadToCloud(data) {
// Compress and send to cloud
const compressed = this.compress(data);
return this.cloudAPI.process(compressed);
}
}
- Data tiering (hot, warm, cold)
- Automatic cleanup of old data
- Compression algorithms
- Edge-specific databases (SQLite, RocksDB)
- Mobile Network Operator (MNO) infrastructure
- Radio Access Network (RAN) integration
- Multi-access Edge Computing platforms
- Application enablement services
// 5G network slicing for edge applications
const edgeSliceConfig = {
sliceId: 'edge-computing-001',
requirements: {
latency: '<1ms',
bandwidth: '1Gbps',
reliability: '99.999%',
coverage: 'indoor/outdoor'
},
applications: [
'autonomous-vehicles',
'ar-vr-gaming',
'industrial-automation'
]
};
- Dynamic frequency scaling
- Sleep/wake optimization
- Background processing limits
- Energy-efficient algorithms
- Data compression
- Caching strategies
- Request batching
- Connection pooling
import React, { useState, useEffect } from 'react';
import { View, Text } from 'react-native';
const EdgeComputingComponent = () => {
const [edgeStatus, setEdgeStatus] = useState('connecting');
const [processingMode, setProcessingMode] = useState('local');
useEffect(() => {
const edgeConfig = {
fallbackTimeout: 2000,
localProcessingThreshold: 0.8,
batteryOptimization: true
};
initializeEdgeConnection(edgeConfig);
}, []);
const processDataAtEdge = async (data) => {
try {
// Attempt local edge processing first
if (getDeviceCapacity() < 0.8) {
return await localEdgeProcess(data);
}
// Fallback to nearby edge node
return await nearbyEdgeProcess(data);
} catch (error) {
// Ultimate fallback to cloud
return await cloudProcess(data);
}
};
return (
<View>
<Text>Edge Status: {edgeStatus}</Text>
<Text>Processing Mode: {processingMode}</Text>
</View>
);
};
- Constrained Devices: Sensors, actuators (KB memory)
- Smart Devices: Cameras, controllers (MB memory)
- Edge Gateways: Aggregators, processors (GB memory)
- MQTT: Lightweight pub/sub messaging
- CoAP: Constrained Application Protocol
- LoRaWAN: Long-range, low-power communication
- 5G NR: Ultra-reliable low-latency communication
class IoTEdgePipeline:
def __init__(self):
self.sensors = []
self.processors = []
self.actuators = []
def add_sensor(self, sensor_config):
"""Add IoT sensor to pipeline"""
sensor = IoTSensor(sensor_config)
self.sensors.append(sensor)
def process_stream(self, data_stream):
"""Real-time stream processing at edge"""
for data_point in data_stream:
# Local processing
processed = self.local_ml_inference(data_point)
# Decision making
if processed.confidence > 0.9:
self.execute_local_action(processed)
else:
self.escalate_to_cloud(data_point)
def local_ml_inference(self, data):
"""Run ML model locally on edge device"""
model = self.load_quantized_model()
return model.predict(data)
- Over-the-air (OTA) updates
- Remote configuration management
- Health monitoring and diagnostics
- Security key rotation
- Eventual Consistency: Accept temporary inconsistencies
- Conflict Resolution: Last-write-wins, vector clocks
- Delta Sync: Transfer only changes
- Operational Transform: Real-time collaborative editing
// React Native offline storage
import AsyncStorage from '@react-native-async-storage/async-storage';
import NetInfo from '@react-native-netinfo/netinfo';
class OfflineManager {
constructor() {
this.isOnline = false;
this.pendingOperations = [];
this.syncQueue = [];
}
async storeOffline(key, data) {
try {
const timestamp = Date.now();
const offlineData = {
data,
timestamp,
synced: false
};
await AsyncStorage.setItem(key, JSON.stringify(offlineData));
this.syncQueue.push(key);
if (this.isOnline) {
this.processSyncQueue();
}
} catch (error) {
console.error('Offline storage error:', error);
}
}
async processSyncQueue() {
while (this.syncQueue.length > 0) {
const key = this.syncQueue.shift();
await this.syncToEdge(key);
}
}
}
- Quantization: Reduce model precision (FP32 → INT8)
- Pruning: Remove unnecessary connections
- Knowledge Distillation: Create smaller student models
- Mobile-optimized formats: TensorFlow Lite, ONNX Runtime
import tensorflow as tf
class EdgeMLProcessor:
def __init__(self, model_path):
# Load optimized model for edge inference
self.interpreter = tf.lite.Interpreter(model_path=model_path)
self.interpreter.allocate_tensors()
def predict_offline(self, input_data):
"""Run inference without network connectivity"""
input_details = self.interpreter.get_input_details()
output_details = self.interpreter.get_output_details()
# Preprocess input
input_data = self.preprocess(input_data)
# Set input tensor
self.interpreter.set_tensor(input_details[0]['index'], input_data)
# Run inference
self.interpreter.invoke()
# Get output
output_data = self.interpreter.get_tensor(output_details[0]['index'])
return self.postprocess(output_data)
IoT Devices → Edge Nodes → Regional Edges → Cloud
- Upstream: Sensor data, alerts, aggregated metrics
- Downstream: Model updates, configuration changes, commands
- Bidirectional: Real-time collaboration, distributed databases
class EdgeCloudSync {
constructor(edgeId, cloudEndpoint) {
this.edgeId = edgeId;
this.cloudEndpoint = cloudEndpoint;
this.syncInterval = 30000; // 30 seconds
this.eventQueue = [];
}
async syncToCloud() {
const pendingEvents = this.getPendingEvents();
if (pendingEvents.length === 0) return;
try {
const response = await fetch(`${this.cloudEndpoint}/sync`, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
edgeId: this.edgeId,
events: pendingEvents,
timestamp: Date.now()
})
});
if (response.ok) {
this.markEventsSynced(pendingEvents);
}
} catch (error) {
console.error('Sync failed:', error);
// Implement exponential backoff
this.scheduleRetry();
}
}
async receiveFromCloud() {
try {
const response = await fetch(`${this.cloudEndpoint}/updates/${this.edgeId}`);
const updates = await response.json();
for (const update of updates) {
await this.applyUpdate(update);
}
} catch (error) {
console.error('Receive updates failed:', error);
}
}
}
class ConflictResolver {
static resolveConflict(localData, cloudData) {
// Vector clock comparison
if (this.isNewerVersion(localData.vectorClock, cloudData.vectorClock)) {
return localData;
} else if (this.isNewerVersion(cloudData.vectorClock, localData.vectorClock)) {
return cloudData;
} else {
// Concurrent updates - merge
return this.mergeData(localData, cloudData);
}
}
static mergeData(data1, data2) {
// Application-specific merge logic
return {
...data1,
...data2,
mergedAt: Date.now(),
conflicts: this.identifyConflicts(data1, data2)
};
}
}
- Accepts temporary inconsistencies
- Guarantees eventual convergence
- Suitable for non-critical data
- Immediate consistency across all nodes
- Higher latency and complexity
- Required for critical operations
const dataClassification = {
critical: {
consistency: 'strong',
replication: 'synchronous',
validation: 'immediate'
},
normal: {
consistency: 'eventual',
replication: 'asynchronous',
validation: 'periodic'
},
cached: {
consistency: 'weak',
replication: 'none',
validation: 'none'
}
};
- Physical device access
- Network communication security
- Data privacy and compliance
- Identity and access management
- Hardware-based root of trust
- Encrypted communication (TLS 1.3)
- Zero-trust architecture
- Regular security updates
- Latency measurements
- Throughput monitoring
- Resource utilization
- Error rates and reliability
class EdgeMonitor {
constructor() {
this.metrics = {
latency: [],
throughput: 0,
cpu: 0,
memory: 0,
network: 0
};
}
recordMetric(type, value) {
const timestamp = Date.now();
if (type === 'latency') {
this.metrics.latency.push({ value, timestamp });
// Keep only last 1000 measurements
if (this.metrics.latency.length > 1000) {
this.metrics.latency.shift();
}
} else {
this.metrics[type] = value;
}
// Send to monitoring service periodically
if (timestamp % 60000 === 0) { // Every minute
this.sendMetrics();
}
}
}
- Real-time object detection
- Navigation and path planning
- Vehicle-to-vehicle communication
- Emergency response
- Predictive maintenance
- Quality control automation
- Supply chain optimization
- Worker safety monitoring
- Remote patient monitoring
- Medical device integration
- Emergency response systems
- Health data analytics
- Traffic management
- Environmental monitoring
- Public safety systems
- Energy optimization
- Edge AI Chips: Specialized processors for ML inference
- 6G Networks: Ultra-low latency, massive connectivity
- Quantum Edge: Quantum computing at the edge
- Digital Twins: Real-time virtual representations
- Standardization efforts (ETSI MEC, Open Edge Computing)
- Edge-as-a-Service platforms
- Multi-cloud edge deployments
- Sustainable edge computing
Edge computing continues to evolve as a critical technology for enabling real-time, distributed applications across various industries, providing the foundation for the next generation of intelligent systems.