The underlying concept of edge computing is decentralization. It is based on moving computation and data storage closer to the sources of data generation-such as IoT devices, sensors, or local edge servers-rather than relying on centralized data centers or cloud infrastructure. This proximity to data sources reduces latency, enables real-time data processing and decision- making, optimizes bandwidth usage by limiting the amount of data sent over the network, and enhances privacy and security by keeping sensitive data local
. Key principles of edge computing include:
- Proximity: Processing data near where it is generated to minimize delay.
- Autonomy: Allowing edge devices or local servers to operate independently or with minimal cloud reliance.
- Distributed architecture: Spreading computing tasks across multiple edge nodes to improve reliability and fault tolerance.
- Real-time decision-making: Enabling immediate responses to local events, critical for applications like autonomous vehicles, industrial automation, and augmented reality.
- Optimized bandwidth: Reducing network congestion by processing data locally and sending only essential information to central data centers.
- Enhanced privacy and security: Keeping sensitive data local reduces exposure during transmission
In essence, edge computing shifts computing power and storage from centralized clouds to the "edge" of the network, enabling faster, more efficient, and more secure data processing tailored for latency-sensitive and bandwidth-intensive applications
