Edge computing benefits enterprises by moving computing and storage closer to the data collection point. These benefits include greater speed and reliability.
FREMONT, CA: As the name implies, edge computing is a processing capacity that lives at the periphery of a linked ecosystem. It is physically proximate to the data-generating endpoint devices, such as sensors or mobile phones.
The purpose of edge computing is to ingest data created by adjacent endpoint devices and then evaluate it using machine learning (ML) software before taking action in response to the analysis.
Edge computing is an alternative to transferring data generated by endpoints to centralized servers for processing—whether on-premises or, more commonly, in the cloud.
Although this feature is typically located in purpose-built devices such as IoT gateways, it can occasionally be found on endpoints themselves.
Edge computing moves processes away from an enterprise’s primary data center and closer to the endpoint devices that generate data, resulting in several significant benefits, including the following:
Increased speed/decreased latency: Edge computing, by definition and design, eliminates the need to transport data from endpoints to the cloud and back. Reduced travel time reduces the duration of the entire operation; this time reduction can be measured in seconds and, in some instances, milliseconds. That may not sound like much, but travel time—also known as latency—is crucial in a connected world where endpoint devices require real-time decision-making capabilities to function correctly.
For example, autonomous vehicles, industrial and manufacturing IoT deployments, and medical use cases require robots to interpret data and respond instantly to commands to function securely.
Strengthened security and privacy safeguards: Since edge computing maintains data close to the edge and away from centralized servers, it can increase security and privacy protections. Nevertheless, edge devices remain vulnerable to hacking, especially if they are not sufficiently safeguarded. However, edge devices only save a small amount of data and frequently do not have comprehensive data sets that hackers could exploit.
On the other side, endpoint data housed on centralized servers are frequently merged with other data points, resulting in a more comprehensive collection of information that hackers can exploit. Consider edge computing in a healthcare context, for example. Sensors gather vital signs from patients, processed by an edge computing device. That equipment is solely for storing those readings.
However, if the endpoint sensors transmit the data back to centralized servers where it is stored with other data, including personally identifiable information about the patient, and that data is compromised, the patient’s privacy is jeopardized.