Edge Computing Architecture and Layers

Introduction

Edge computing is a new paradigm that moves computational processing nearer to where data is generated in an attempt to reduce latency and bandwidth usage. In contrast to conventional cloud-based systems with centralized data centers for processing data, edge computing disperses computational capacity among devices and local nodes in the network at the edge. The architectural change is essential in real-time applications that require stringent high-speed data processing demands like autonomous cars, health monitoring, and industrial automation.

The edge computing infrastructure is composed of multiple layers, with each layer playing its role. At the ground level, there are edge devices such as sensors and actuators that gather data and do the primary processing. Moving upwards, fog computing is an intermediary layer between multiple edge nodes, which consolidates data, pre-processes, and sends it to the cloud. The cloud at the top does the heavy computation and long-term storage of data.

The confluence of IoT, mobile computing, digital twins, and cloud infrastructure creates a robust edge computing architecture. We present in this chapter the basic building blocks and layers of edge computing, such as IoT and digital twins, cloud infrastructure, fog computing, and the edge-cloud continuum.

1.1 IoT, Mobile, and Digital Twins

Internet of Things (IoT)

The Internet of Things (IoT) is networked physical objects that communicate using the internet. Examples are everyday consumer devices like smart thermostats, industrial equipment, wearable health trackers, and components of urban infrastructure. IoT offers greater connectivity and automation via continuous data generation and analysis.

Key Features of IoT:

Connectivity: Utilizes protocols such as Wi-Fi, Bluetooth, Zigbee, and LoRaWAN to transmit

data.

Data Processing: Uses both cloud and edge computing systems to analyze data collected from

sensors.

Automation: Employs AI-driven or rule-based systems for autonomous decision-making.
Examples: Smart homes, healthcare wearables, industrial IoT (IIoT), smart cities, and connected

vehicles.

Mobile Cloud Computing (MCC)

Mobile Cloud Computing (MCC) integrates mobile devices and cloud computing to improve processing capability and storage. The paradigm enables computation-intensive processing to be offloaded from mobile devices to cloud servers, thereby conserving battery life while improving performance.

Benefits of MCC:

Extended Battery Life: Offloading processing tasks to the cloud reduces local energy

consumption.

Higher Processing Power: Supports complex applications like AI and machine learning.
Scalability: Dynamically scales to accommodate fluctuating user demand.
Real-Time Access: Facilitates access to cloud-hosted applications from anywhere.
Applications: Mobile AI assistants (like Google Assistant), cloud gaming, and AR/VR

applications.

Digital Twins

Digital twins are computerized replicas of physical assets, updated regularly with real-time data in order to simulate, predict, and improve performance. Digital twins are being used more in manufacturing, healthcare, and smart city initiatives, where they can provide predictive maintenance and system optimization.

Digital twins connect physical devices with virtual environments. Digital twins function inside edge nodes in edge computing to analyze information locally, limiting the necessity of sending huge volumes of data to the cloud. The arrangement is specifically valuable in applications requiring real-time analytics, for example, industrial automation and medical monitoring.

Diagram 1: Digital Twin Edge Network Architecture [[File:|500px|thumb|center|Real-Time Edge Adoption]]