Technology and Applications

Today, the collaboration between edge computing and cloud computing has become a core technology in IoT and industrial applications. This synergy combines the distributed computing power of edge devices with the robust analytics and storage capabilities of the cloud, offering flexible and efficient solutions across industries. At the same time, data synchronization serves as a vital link in achieving such …

Edge computing devices are widely used in high-reliability data processing scenarios, including industrial automation, smart cities, and IoT device networks. However, network downtime is often unavoidable, posing challenges to real-time data transmission and operational continuity. To address this, modern edge devices are equipped with robust offline buffering and delayed uploading mechanisms to ensure uninterrupted operation and prevent data loss during …

In today’s era of IoT, AI, and Industry 4.0, edge computing devices are widely deployed for tasks requiring continuous operation. However, situations where networks become unstable or even temporarily offline are inevitable. Can edge computing devices independently handle data processing during such disruptions? The answer is a resounding yes, as one of the core principles of edge computing is equipping …

In modern IoT, AI, and industrial automation applications, real-time performance is critical for task success. However, high-latency network environments can significantly impact data transmission and processing speeds. Edge computing, with its unique distributed architecture and local processing capabilities, emerges as a key technology to address this challenge. How does edge computing maintain real-time performance in high-latency networks? This article delves …

Edge computing devices play a crucial role in modern IoT, AI, and Industry 4.0 applications. Supporting advanced communication technologies such as 5G, WiFi 6, and LoRa is especially critical. These technologies offer distinct advantages in diverse scenarios, ranging from real-time data processing to remote sensor networks. Do edge computing devices support these latest communication technologies, and what are their respective …

With the growing adoption of edge computing in the Internet of Things (IoT) and Artificial Intelligence (AI) domains, the need for data sharing among devices within a system has become more pressing. Distributed storage systems offer an efficient solution for seamless data sharing across multiple edge devices. This article explores whether edge computing devices support distributed storage systems and the …

As edge computing devices are widely utilized in Artificial Intelligence (AI), Internet of Things (IoT), and industrial automation, efficient data read and write operations have become critical. Slow data read/write speeds often result in significant impacts on real-time task processing, thus reducing overall system performance. How significantly do slow data speeds affect edge computing performance, and what strategies can optimize …

As AI models become increasingly complex, many edge computing devices may encounter insufficient memory issues when running deep learning tasks. In such cases, the support for expandable storage becomes crucial. Efficient storage expansion not only meets the demand for running complex AI models but also enhances the flexibility and longevity of the devices. So, what types of storage expansion are …

As edge computing devices are widely adopted in IoT, AI, and industrial automation, local storage capacity has become a critical factor affecting device performance. When processing large-scale data like high-definition video streams in real-time, storage capacity directly determines the device’s usability and efficiency. So, what is the typical local storage capacity of edge computing devices, and is it sufficient for …