Edge Computing Latest Trends 2025 - Distributed Processing Accelerated by 5G/6G Convergence

Edge computing is a technology that significantly reduces latency and enables real-time processing by performing data processing close to users and devices. In 2025, with the spread of 5G/6G and the evolution of AI, edge computing has entered a new phase.

Basic Concepts of Edge Computing

Architecture Comparison

flowchart TB
    subgraph Traditional["Traditional Cloud Architecture"]
        Cloud1["Central Cloud<br/>(100ms+ latency)"]
        D1["Device"]
        D2["Device"]
        D3["Device"]
        D1 --> |Internet| Cloud1
        D2 --> |Internet| Cloud1
        D3 --> |Internet| Cloud1
    end

flowchart TB
    subgraph EdgeArch["Edge Computing Architecture"]
        Cloud2["Central Cloud<br/>(Data aggregation & analysis)"]

        E1["Edge Node<br/>(10-50ms)"]
        E2["Edge Node<br/>(10-50ms)"]
        E3["Edge Node<br/>(10-50ms)"]

        ED1["Device (<5ms)"]
        ED2["Device (<5ms)"]
        ED3["Device (<5ms)"]

        ED1 --> E1
        ED2 --> E2
        ED3 --> E3
        E1 --> Cloud2
        E2 --> Cloud2
        E3 --> Cloud2
    end

Latency Comparison

Key Trends in 2025

1. 5G/6G Convergence

With the full deployment of 5G, MEC (Multi-access Edge Computing) has entered practical use.

# 5G MEC Configuration Example
network:
  5g_core:
    upf:  # User Plane Function
      deployment: edge
      latency_target: 10ms

  mec_platform:
    location: base_station
    capabilities:
      - video_analytics
      - ar_rendering
      - game_streaming

  applications:
    - name: autonomous_driving
      latency_requirement: 1ms
      bandwidth: 1Gbps

    - name: industrial_iot
      latency_requirement: 5ms
      reliability: 99.9999%

2. Proliferation of Edge AI Inference

# Edge AI inference example (ONNX Runtime)
import onnxruntime as ort
import numpy as np

class EdgeAIInference:
    def __init__(self, model_path: str):
        # Edge optimization options
        sess_options = ort.SessionOptions()
        sess_options.graph_optimization_level = (
            ort.GraphOptimizationLevel.ORT_ENABLE_ALL
        )
        sess_options.intra_op_num_threads = 4

        # Select available providers
        providers = ['TensorrtExecutionProvider',  # NVIDIA GPU
                     'CUDAExecutionProvider',
                     'CoreMLExecutionProvider',    # Apple Silicon
                     'CPUExecutionProvider']

        self.session = ort.InferenceSession(
            model_path,
            sess_options=sess_options,
            providers=providers
        )

    def predict(self, input_data: np.ndarray) -> np.ndarray:
        """Low-latency inference"""
        input_name = self.session.get_inputs()[0].name
        return self.session.run(None, {input_name: input_data})[0]

# Usage: Image classification (< 10ms)
edge_model = EdgeAIInference("mobilenet_v3.onnx")
result = edge_model.predict(preprocessed_image)

3. WebAssembly Edge Runtime

// Wasm edge function example (Cloudflare Workers compatible)
use worker::*;

#[event(fetch)]
async fn main(req: Request, env: Env, _ctx: Context) -> Result<Response> {
    // Get request geo information
    let cf = req.cf();
    let country = cf.country().unwrap_or("Unknown");
    let colo = cf.colo();  // Edge location

    // Data processing at edge
    let body = req.text().await?;
    let processed = process_at_edge(&body);

    // Access to KV store (edge cache)
    let kv = env.kv("CACHE")?;
    kv.put(&cache_key, &processed)?
        .expiration_ttl(3600)
        .execute()
        .await?;

    Response::ok(format!(
        "Processed at {} ({})",
        colo, country
    ))
}

fn process_at_edge(data: &str) -> String {
    // Execute lightweight processing at edge
    data.to_uppercase()
}

Major Platform Comparison

Cloud Vendor Edge Services

AWS Lambda@Edge Implementation Example

// Lambda@Edge function (CloudFront integration)
import {
  CloudFrontRequestEvent,
  CloudFrontResponseEvent,
  Context
} from 'aws-lambda';

// Processing on origin request
export const originRequest = async (
  event: CloudFrontRequestEvent,
  context: Context
) => {
  const request = event.Records[0].cf.request;

  // A/B test routing
  const experimentGroup = getExperimentGroup(request);
  if (experimentGroup === 'B') {
    request.origin = {
      custom: {
        domainName: 'experiment-b.example.com',
        port: 443,
        protocol: 'https',
        path: '',
        sslProtocols: ['TLSv1.2'],
        readTimeout: 30,
        keepaliveTimeout: 5,
        customHeaders: {}
      }
    };
  }

  return request;
};

// Processing on origin response
export const originResponse = async (
  event: CloudFrontResponseEvent
) => {
  const response = event.Records[0].cf.response;

  // Add security headers
  response.headers['strict-transport-security'] = [{
    key: 'Strict-Transport-Security',
    value: 'max-age=63072000; includeSubdomains; preload'
  }];

  response.headers['content-security-policy'] = [{
    key: 'Content-Security-Policy',
    value: "default-src 'self'"
  }];

  return response;
};

function getExperimentGroup(request: any): string {
  const cookies = request.headers.cookie || [];
  // Cookie parsing logic
  return Math.random() < 0.5 ? 'A' : 'B';
}

Cloudflare Workers D1 Database

// Edge SQL (D1) usage example
export interface Env {
  DB: D1Database;
}

export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    const url = new URL(request.url);

    if (url.pathname === '/api/products') {
      // SQL execution at edge
      const { results } = await env.DB.prepare(`
        SELECT id, name, price, stock
        FROM products
        WHERE category = ?
        ORDER BY popularity DESC
        LIMIT 20
      `).bind(url.searchParams.get('category'))
        .all();

      return Response.json(results, {
        headers: {
          'Cache-Control': 'public, max-age=60',
        },
      });
    }

    if (url.pathname === '/api/analytics') {
      // Aggregation processing at edge
      const stmt = env.DB.prepare(`
        INSERT INTO page_views (path, timestamp, country)
        VALUES (?, datetime('now'), ?)
      `);

      const cf = request.cf as { country?: string };
      await stmt.bind(url.pathname, cf?.country || 'Unknown').run();

      return new Response('OK');
    }

    return new Response('Not Found', { status: 404 });
  },
};

Edge Computing Use Cases

1. Autonomous Vehicles

flowchart TB
    subgraph Vehicle["In-Vehicle Edge"]
        Sensors["Camera / LiDAR / Radar"]
        AI["AI Module<br/>(<10ms)"]
        Control["Control Module"]
        Output["Steering / Accel / Brake"]
        Sensors --> AI --> Control --> Output
    end

    subgraph Roadside["Roadside Edge Node"]
        R1["Traffic signal optimization"]
        R2["Nearby vehicle information aggregation"]
        R3["Danger prediction & warning distribution"]
    end

    subgraph Cloud["Cloud"]
        CL1["Map data updates"]
        CL2["Machine learning model training"]
        CL3["Large-scale traffic analysis"]
    end

    Vehicle --> |V2X Communication| Roadside --> Cloud

2. Industrial IoT (IIoT)

# Industrial edge gateway implementation example
import asyncio
from dataclasses import dataclass
from typing import List, Optional
import json

@dataclass
class SensorReading:
    sensor_id: str
    value: float
    timestamp: float
    unit: str

class IndustrialEdgeGateway:
    def __init__(self):
        self.alert_thresholds = {
            'temperature': {'min': -10, 'max': 85},
            'vibration': {'max': 10.0},
            'pressure': {'min': 0.5, 'max': 10.0}
        }
        self.local_buffer: List[SensorReading] = []

    async def process_sensor_data(
        self,
        reading: SensorReading
    ) -> Optional[dict]:
        """Real-time processing at edge"""

        # 1. Anomaly detection (immediate judgment)
        alert = self._check_anomaly(reading)
        if alert:
            # Send emergency alert immediately
            await self._send_alert(alert)
            return alert

        # 2. Data aggregation (buffering)
        self.local_buffer.append(reading)

        # 3. Periodic cloud sync
        if len(self.local_buffer) >= 100:
            await self._sync_to_cloud()

        return None

    def _check_anomaly(self, reading: SensorReading) -> Optional[dict]:
        """Local anomaly detection"""
        sensor_type = reading.sensor_id.split('_')[0]
        threshold = self.alert_thresholds.get(sensor_type)

        if not threshold:
            return None

        if 'max' in threshold and reading.value > threshold['max']:
            return {
                'type': 'HIGH_ALERT',
                'sensor': reading.sensor_id,
                'value': reading.value,
                'threshold': threshold['max']
            }

        if 'min' in threshold and reading.value < threshold['min']:
            return {
                'type': 'LOW_ALERT',
                'sensor': reading.sensor_id,
                'value': reading.value,
                'threshold': threshold['min']
            }

        return None

    async def _send_alert(self, alert: dict):
        """Immediate alert sending"""
        print(f"ALERT: {json.dumps(alert)}")
        # Actually send via MQTT/WebSocket

    async def _sync_to_cloud(self):
        """Cloud sync of buffered data"""
        data = self.local_buffer.copy()
        self.local_buffer.clear()
        # Compress and send to cloud
        print(f"Syncing {len(data)} readings to cloud")

3. AR/VR Streaming

// Distributed AR rendering processing at edge
interface ARRenderRequest {
  devicePose: {
    position: [number, number, number];
    rotation: [number, number, number, number];
  };
  fov: number;
  resolution: [number, number];
}

interface ARRenderResponse {
  frame: ArrayBuffer;
  timestamp: number;
  latency: number;
}

class EdgeARRenderer {
  private edgeNodes: Map<string, EdgeNode> = new Map();

  async selectOptimalNode(
    userLocation: GeolocationPosition
  ): Promise<EdgeNode> {
    // Latency-based node selection
    const nodes = Array.from(this.edgeNodes.values());
    const latencies = await Promise.all(
      nodes.map(async (node) => ({
        node,
        latency: await this.measureLatency(node)
      }))
    );

    return latencies.reduce((best, current) =>
      current.latency < best.latency ? current : best
    ).node;
  }

  async renderFrame(
    node: EdgeNode,
    request: ARRenderRequest
  ): Promise<ARRenderResponse> {
    const start = performance.now();

    // Render on edge node
    const response = await fetch(`${node.url}/render`, {
      method: 'POST',
      body: JSON.stringify(request),
      headers: {
        'Content-Type': 'application/json',
      },
    });

    const frame = await response.arrayBuffer();
    const latency = performance.now() - start;

    // Maintain under 20ms (threshold for comfortable AR experience)
    if (latency > 20) {
      console.warn(`High latency: ${latency}ms`);
    }

    return {
      frame,
      timestamp: Date.now(),
      latency,
    };
  }

  private async measureLatency(node: EdgeNode): Promise<number> {
    const start = performance.now();
    await fetch(`${node.url}/ping`);
    return performance.now() - start;
  }
}

interface EdgeNode {
  id: string;
  url: string;
  region: string;
  capabilities: string[];
}

Edge Security

Zero Trust Edge

# Edge Security Policy
security:
  authentication:
    method: mTLS
    certificate_rotation: 24h

  authorization:
    model: ABAC  # Attribute-Based Access Control
    policies:
      - name: sensor_data_access
        subjects:
          - type: device
            attributes:
              device_type: sensor
              firmware_version: ">=2.0"
        resources:
          - type: api
            path: /data/*
        actions: [POST]
        conditions:
          - time_range: "00:00-23:59"
          - rate_limit: 1000/hour

  encryption:
    at_rest: AES-256-GCM
    in_transit: TLS 1.3

  monitoring:
    anomaly_detection: enabled
    log_retention: 7d
    alert_channels:
      - slack
      - pagerduty

Edge Firewall

// Edge WAF implementation example
interface WAFRule {
  id: string;
  pattern: RegExp;
  action: 'block' | 'log' | 'challenge';
  category: string;
}

const wafRules: WAFRule[] = [
  {
    id: 'sqli-001',
    pattern: /(\b(SELECT|INSERT|UPDATE|DELETE|DROP|UNION)\b.*\b(FROM|INTO|WHERE)\b)/i,
    action: 'block',
    category: 'SQL Injection'
  },
  {
    id: 'xss-001',
    pattern: /<script[^>]*>[\s\S]*?<\/script>/i,
    action: 'block',
    category: 'XSS'
  },
  {
    id: 'path-001',
    pattern: /\.\.[\/\\]/,
    action: 'block',
    category: 'Path Traversal'
  }
];

async function edgeWAF(request: Request): Promise<Response | null> {
  const url = new URL(request.url);
  const body = await request.text();

  const checkTargets = [
    url.pathname,
    url.search,
    body,
    ...Array.from(request.headers.values())
  ];

  for (const rule of wafRules) {
    for (const target of checkTargets) {
      if (rule.pattern.test(target)) {
        console.log(`WAF blocked: ${rule.category} (${rule.id})`);

        if (rule.action === 'block') {
          return new Response('Forbidden', { status: 403 });
        }
      }
    }
  }

  return null;  // Pass request through
}

Implementation Best Practices

1. Proper Workload Placement

flowchart TB
    Start["Latency requirement < 50ms?"]
    RealTime["Real-time processing needed?"]
    LargeData["Large data volume?"]
    DeviceEdge["Process on device/edge"]
    EdgeCache["Utilize edge cache"]
    EdgeCloud["Pre-process at edge → Analyze in cloud"]
    CloudOnly["Process entirely in cloud"]

    Start -->|YES| RealTime
    Start -->|NO| LargeData
    RealTime -->|YES| DeviceEdge
    RealTime -->|NO| EdgeCache
    LargeData -->|YES| EdgeCloud
    LargeData -->|NO| CloudOnly

Recommended Placement:

2. Failure Handling and Fallback

// Edge fault tolerance implementation
class ResilientEdgeClient {
  private primaryEdge: string;
  private fallbackEdges: string[];
  private cloudEndpoint: string;

  constructor(config: EdgeConfig) {
    this.primaryEdge = config.primaryEdge;
    this.fallbackEdges = config.fallbackEdges;
    this.cloudEndpoint = config.cloudEndpoint;
  }

  async request<T>(path: string, options: RequestInit): Promise<T> {
    // 1. Try primary edge
    try {
      return await this.fetchWithTimeout(
        `${this.primaryEdge}${path}`,
        options,
        5000  // 5 second timeout
      );
    } catch (e) {
      console.warn('Primary edge failed, trying fallback');
    }

    // 2. Try fallback edges
    for (const edge of this.fallbackEdges) {
      try {
        return await this.fetchWithTimeout(
          `${edge}${path}`,
          options,
          5000
        );
      } catch (e) {
        continue;
      }
    }

    // 3. Fallback to cloud as last resort
    console.warn('All edges failed, falling back to cloud');
    return await this.fetchWithTimeout(
      `${this.cloudEndpoint}${path}`,
      options,
      30000  // Longer timeout for cloud
    );
  }

  private async fetchWithTimeout<T>(
    url: string,
    options: RequestInit,
    timeout: number
  ): Promise<T> {
    const controller = new AbortController();
    const timeoutId = setTimeout(() => controller.abort(), timeout);

    try {
      const response = await fetch(url, {
        ...options,
        signal: controller.signal,
      });
      return await response.json();
    } finally {
      clearTimeout(timeoutId);
    }
  }
}

Summary

Edge computing is rapidly developing in the following areas in 2025:

1. 5G/6G Integration: Achieving ultra-low latency through MEC
2. Edge AI: Proliferation of on-device inference processing
3. WebAssembly: Cross-platform edge runtime
4. Security: Application of zero trust architecture

Points to Consider for Implementation

• Clarify latency requirements
• Consider data confidentiality and regulatory requirements
• Design appropriate role division with cloud
• Prepare fallback strategy for failures

Edge computing has become an essential architecture for applications requiring real-time processing and distributed computing.

Reference Links

• AWS Edge Services
• Cloudflare Workers Documentation
• ETSI MEC Standards
• Linux Foundation Edge