Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, rather than relying on a centralized data processing facility or cloud-based data centers. In edge computing, data processing occurs near the source of data generation, typically at or near the “edge” of the network, such as IoT devices, sensors, or local servers.
Edge computing complements traditional cloud computing by extending computing capabilities closer to the point of data generation, enabling faster processing, improved efficiency, and greater flexibility in deploying and managing distributed applications and services.
Edge computing aims to reduce latency and bandwidth usage by processing data closer to where it is generated. This proximity to the data source enables faster response times and more efficient data handling, particularly for time-sensitive applications.
Unlike traditional centralized computing models where data is sent to a remote data center or cloud for processing, edge computing distributes computing resources across a network of edge devices or servers. This decentralized architecture improves scalability, reliability, and resilience by minimizing single points of failure.
SCADA systems acquire data from various sensors, meters, and instruments distributed throughout the industrial environment. This data can include parameters such as temperature, pressure, flow rates, and other relevant variables.
Edge computing enables real-time or near-real-time data processing, analysis, and decision-making at the edge of the network. This capability is crucial for applications that require immediate insights or actions, such as industrial automation, autonomous vehicles, and augmented reality.
By processing and filtering data locally, edge computing helps optimize network bandwidth usage and reduce the volume of data transmitted to centralized servers or the cloud. This can lead to cost savings and improved network efficiency, especially in bandwidth-constrained environments.
Edge computing can enhance data privacy and security by keeping sensitive data local and reducing the need to transmit it over external networks. This is particularly important for applications handling sensitive or regulated data, such as healthcare or finance.
Edge computing is often deployed in conjunction with cloud computing in hybrid architectures. In such setups, data processing tasks are distributed between edge devices and centralized cloud infrastructure based on factors such as workload requirements, latency constraints, and resource availability.
Edge Computing Device
We offer industrial IoT, smart cities, autonomous vehicles, healthcare, retail, and telecommunications. Examples of edge computing use cases include predictive maintenance, real-time monitoring, video analytics, and personalized content delivery.