Category: Edge

Edge computing: When is it a Good Fit?

Although the value of edge computing is well understood at this point, it’s still being used to solve problems where it’s not needed. We’re back to ensuring that whatever technology is being hyped right now is still a good choice. So, when is edge a good fit and when is it not? Keep in mind that edge computing can be expensive and harder to secure.

Edge computing: 5 potential pitfalls

Edge computing has many upsides including better near-real-time response and analytics for IoT, but it’s far from plug and play. Challenges include security risks, hiring staff who can support data management and analytics, building out the network to support edge computing, and scaling up without creating too much complexity.

The cutting edge of healthcare: How edge computing will transform medicine

Edge computing will help reshape how, where, and how quickly medical care can be delivered by supporting advanced remote-patient monitoring by processing data from medical devices such as glucose monitors and blood pressure cuffs and then alerting clinicians to problematic readings. It could enable real-time management of medical equipment as the various pieces move through hospital facilities. And it could deliver on-demand content for augmented reality and virtual reality training sessions, with the proximity of the edge computing devices ensuring there’s no lag in the experience.

Edge computing: The architecture of the future

To fully digitize the last mile of business, you need to distribute compute power where it’s needed most — right next to IoT devices that collect data from the real world. Edge computing provides a vital layer of compute and storage physically close to IoT endpoints, so that control devices can respond with low latency – and edge analytics processing can reduce the amount of data that needs to be transferred to the core.

Edge Computing vs Fog Computing: What’s the Difference?

Edge computing and fog computing allow processing data within a local network rather than sending it to the cloud, which decreases latency and increases security. The main difference between the two is processing location. With edge computing, data processing typically occurs directly on a sensor-equipped product that collects the information or a gateway device physically close to those sensors. Fog computing moves edge computing activities to local area network (LAN) hardware or processors connected to it. These may be physically farther from the data-capturing sensors compared to edge computing.

Edge Computing: New Support For Digital Twins

Because simulation requires computational resources and the associated data outputs are large, cloud computing ― with its scalability and relatively low cost ― has traditionally been the technology environment of choice for supporting digital twins. But today, edge computing has emerged as a promising alternative. Edge computing leverages local resources that are close to the physical product’s location, which means reduced latency, while improving responsiveness, agility and privacy.