Official definition and framework for edge computing from the National Institute of Standards and Technology, establishing terminology and architectural models.

Key Definitions:

• Edge Computing: Processing data near the source rather than in centralized cloud
• Latency Reduction: Minimizing round-trip time for time-sensitive applications
• Bandwidth Optimization: Reducing data transmission costs and network congestion
• Edge vs Cloud vs Fog: Clarifying the spectrum of distributed computing

For Architects:

Authoritative reference for understanding edge computing architecture and terminology.

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Comprehensive survey defining edge computing landscape, identifying challenges, and outlining research directions.

Core Concepts:

• Computing at the Edge: Moving computation from cloud to network edge
• Benefits: Reduced latency, bandwidth savings, privacy, reliability
• Challenges: Resource constraints, heterogeneity, security
• Use Cases: IoT, mobile computing, content delivery, smart cities

For Product Managers:

Framework for evaluating when edge computing provides strategic advantage.

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Introduction to fog computing architecture extending cloud computing to the edge, focusing on IoT applications.

Fog Computing Model:

• Distributed Architecture: Hierarchical compute between devices and cloud
• Location Awareness: Processing near data sources for context
• Low Latency: Real-time response for interactive applications
• IoT Integration: Supporting massive numbers of connected devices

For System Architects:

Understanding the fog computing paradigm and its relationship to edge and cloud.

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Industry whitepaper from European Telecommunications Standards Institute documenting practical edge computing deployments across industries.

Industry Applications:

• Smart Cities: Traffic management, public safety, energy optimization
• Industrial IoT: Predictive maintenance, quality control, automation
• Retail: In-store analytics, inventory management, customer experience
• Healthcare: Patient monitoring, telemedicine, medical imaging

For Business Leaders:

Real-world examples demonstrating edge computing business value.

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Accessible introduction to edge computing from Carnegie Mellon pioneer, covering history, motivations, and future directions.

Historical Context:

• From Cloud to Edge: Evolution of distributed computing architectures
• Mobile Computing: How smartphones drove edge computing needs
• Cloudlets: Early edge computing infrastructure concepts
• Future Vision: Pervasive edge computing and its implications

For Technologists:

Historical perspective informing current edge computing design decisions.

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Practical guide to IoT architecture and implementation covering device connectivity, data management, and business applications.

Key Topics:

• IoT Architecture: Device, gateway, cloud, and application layers
• Connectivity: Selecting appropriate communication protocols
• Data Flow: Managing data from devices to insights
• Business Models: Creating value from IoT deployments

For IoT Developers:

End-to-end perspective on building practical IoT systems.

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Official specification for MQTT protocol, the lightweight publish-subscribe messaging protocol designed for IoT and edge applications.

Protocol Features:

• Publish-Subscribe: Decoupled messaging pattern for IoT devices
• Lightweight: Minimal overhead for constrained devices and networks
• Quality of Service: Configurable delivery guarantees
• Last Will: Automatic notification of device disconnection

For Developers:

Essential protocol for IoT device communication and edge architectures.

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Comprehensive survey of LPWAN technologies including LoRaWAN, NB-IoT, and Sigfox for long-range, low-power IoT connectivity.

LPWAN Technologies:

• LoRaWAN: Long-range wireless for battery-powered sensors
• NB-IoT: Cellular-based narrow-band IoT
• Sigfox: Ultra-narrow band communication
• Trade-offs: Range, power, bandwidth, and cost comparisons

For IoT Architects:

Selecting appropriate connectivity for distributed sensor networks.

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Foundational survey of wireless sensor network principles, architectures, and protocols that underpin modern IoT systems.

WSN Fundamentals:

• Network Topology: Mesh, star, and hierarchical sensor networks
• Routing Protocols: Energy-efficient data transmission
• Data Aggregation: Reducing network traffic through in-network processing
• Energy Management: Maximizing battery life in sensor nodes

For Network Designers:

Foundational knowledge for distributed sensor network design.

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Industry reference architecture for Industrial IoT (IIoT) covering manufacturing, energy, and infrastructure applications.

IIoT Architecture:

• Edge Devices: Industrial sensors, PLCs, and gateways
• Connectivity: Industrial protocols (OPC UA, Modbus, etc.)
• Edge Analytics: Real-time processing for industrial automation
• Security: Industrial-grade security and safety requirements

For Industrial Engineers:

Standards and best practices for industrial edge computing deployments.

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Comprehensive guide to distributed systems concepts including consistency, replication, and partitioning essential for edge architectures.

Key Concepts:

• Consistency Models: CAP theorem and eventual consistency
• Replication: Strategies for data distribution across edge nodes
• Partitioning: Sharding data for distributed processing
• Fault Tolerance: Designing for unreliable networks and devices

For System Architects:

Essential distributed systems knowledge for edge computing design.

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Authoritative textbook on distributed systems covering fundamental principles applicable to edge computing architectures.

Distributed Computing Topics:

• Communication: RPC, message-passing, and streaming
• Synchronization: Clock synchronization and distributed coordination
• Consistency: Data consistency models and protocols
• Fault Tolerance: Handling failures in distributed systems

For Computer Scientists:

Theoretical foundation for distributed edge computing systems.

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Documentation and case studies of lightweight Kubernetes distributions for edge orchestration and container management.

Edge Orchestration:

• K3s: Lightweight Kubernetes for resource-constrained edge devices
• KubeEdge: Extending Kubernetes to edge nodes with offline autonomy
• Container Management: Deploying and updating edge workloads
• Service Mesh: Managing communication between edge and cloud

For DevOps Engineers:

Practical tools for managing distributed edge infrastructure.

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Survey of data synchronization strategies for hybrid edge-cloud systems, addressing consistency and conflict resolution.

Synchronization Patterns:

• Eventual Consistency: Asynchronous updates between edge and cloud
• Conflict Resolution: Handling concurrent updates from multiple edges
• Bandwidth Optimization: Selective synchronization strategies
• Offline Operation: Maintaining functionality during network outages

For Backend Developers:

Strategies for keeping edge and cloud data synchronized.

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Comprehensive survey of security challenges and solutions for edge computing including device security, network security, and data privacy.

Security Challenges:

• Device Security: Securing resource-constrained edge devices
• Network Security: Protecting edge-to-cloud communication
• Data Privacy: Processing sensitive data at the edge
• Trust Models: Establishing trust in distributed edge environments

For Security Engineers:

Security considerations specific to edge computing architectures.

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Comprehensive guide to deploying machine learning on microcontrollers and embedded devices with minimal power consumption.

TinyML Concepts:

• Model Optimization: Quantization, pruning, and compression techniques
• TensorFlow Lite Micro: ML framework for microcontrollers
• Audio Recognition: Wake word detection and sound classification
• Vision Applications: Person detection on resource-constrained devices

For Embedded ML Engineers:

Practical guide to running ML on ultra-low-power edge devices.

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Foundational paper on neural network compression techniques essential for deploying models on edge devices.

Compression Techniques:

• Pruning: Removing redundant weights and connections
• Quantization: Reducing precision from 32-bit to 8-bit or lower
• Huffman Coding: Further compression through encoding
• Performance: 35-49× compression without accuracy loss

For ML Engineers:

Techniques for making deep learning models edge-compatible.

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Introduction to federated learning, enabling model training across distributed edge devices without centralizing data.

Federated Learning:

• Decentralized Training: Learning from data on edge devices
• Privacy Preservation: Data remains on device, only model updates shared
• Communication Efficiency: Minimizing data transfer between edge and cloud
• Applications: Mobile keyboards, healthcare, IoT sensor networks

For ML Researchers:

Privacy-preserving machine learning for distributed edge systems.

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Introduction to MobileNet architecture family designed specifically for efficient inference on mobile and edge devices.

MobileNet Features:

• Depthwise Separable Convolutions: Efficient CNN building blocks
• Width Multiplier: Tunable model size for different devices
• Resolution Multiplier: Trading accuracy for speed
• Applications: Object detection, classification, segmentation on edge

For Computer Vision Engineers:

Efficient neural network architectures for edge vision applications.

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Survey of real-time computer vision on edge devices covering YOLO, SSD, and other efficient detection models.

Real-Time Vision:

• YOLO Family: Single-shot object detection for real-time applications
• Edge TPUs: Specialized hardware accelerators for vision models
• Model Selection: Choosing models for different edge hardware
• Applications: Surveillance, autonomous systems, industrial inspection

For Edge AI Developers:

Deploying state-of-the-art computer vision models on edge devices.

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Comprehensive survey of edge computing applications in smart city infrastructure including traffic, energy, and public safety.

Smart City Applications:

• Intelligent Transportation: Traffic management with edge-based video analytics
• Smart Lighting: Adaptive street lighting based on real-time conditions
• Public Safety: Surveillance and emergency response systems
• Environmental Monitoring: Air quality and noise pollution sensing

For Urban Planners:

Understanding how edge computing enables smart city services.

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Analysis of edge computing deployments in retail environments covering customer analytics, inventory management, and checkout-free stores.

Retail Edge Applications:

• Computer Vision: Shelf monitoring, customer flow analysis, loss prevention
• Inventory Management: Real-time stock tracking with RFID and vision
• Personalization: In-store product recommendations
• Checkout-Free: Amazon Go-style automated checkout systems

For Retail Technologists:

ROI and implementation strategies for retail edge computing.

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Survey of edge computing applications in healthcare including remote patient monitoring, medical imaging, and telemedicine.

Healthcare Edge:

• Patient Monitoring: Real-time vital signs analysis at edge
• Medical Imaging: Edge-based preliminary diagnosis and image enhancement
• Telemedicine: Low-latency remote consultations
• Privacy & Compliance: Processing sensitive data locally for HIPAA compliance

For Healthcare IT:

Edge computing enabling new healthcare delivery models.

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Analysis of edge computing requirements and architectures for autonomous driving including V2X communication and real-time decision making.

Autonomous Driving Edge:

• Sensor Fusion: Processing lidar, radar, and camera data in real-time
• V2X Communication: Vehicle-to-everything edge coordination
• Path Planning: Low-latency trajectory computation
• Edge Infrastructure: Roadside units supporting autonomous vehicles

For Automotive Engineers:

Edge computing as critical infrastructure for autonomous driving.

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Overview of Multi-Access Edge Computing (MEC) architecture integrated with 5G networks enabling ultra-low latency applications.

5G + MEC:

• Network Edge: Compute resources at cellular network edge
• Ultra-Low Latency: Sub-10ms response times for applications
• Network Slicing: Dedicated edge resources for specific applications
• Use Cases: AR/VR, cloud gaming, industrial automation, smart cities

For Telecom Engineers:

Understanding the convergence of 5G and edge computing.

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