In principle, edge computing technology is used to collect, filter, process and analyze data “in situ” at or near the edge of the network. This is a powerful way to use data that cannot first be moved to a centralized location—usually because the sheer volume of data makes such a move costly, technically impractical, or might breach compliance obligations such as data sovereignty. This definition has generated countless real-world examples and cases:
Edge gateway application scenarios
manufacturing
An industrial manufacturer deployed edge computing to monitor manufacturing, enabling real-time analytics and machine learning at the edge to identify production errors and improve product manufacturing quality. Edge computing enables the addition of environmental sensors throughout manufacturing plants, providing insights into how each product component is assembled and stored—and how long those components remain in stock. Manufacturers can now make faster, more accurate business decisions about factory facilities and manufacturing operations.
agriculture. Consider a business that grows crops indoors without sunlight, soil or pesticides. This process reduces growing time by more than 60%. Using sensors allows companies to track water usage, nutrient density and determine optimal harvests. Collect and analyze data to uncover the impact of environmental factors and continuously improve crop growth algorithms and ensure crops are harvested under peak conditions.
Network Optimization. Edge computing can help optimize network performance by measuring the performance of users on the Internet and then using analytics to determine the most reliable, low-latency network path for each user’s traffic. In effect, edge computing is used to “direct” traffic across the network for optimal performance for time-sensitive traffic.
Workplace safety. Edge computing can combine and analyze data from on-site cameras, employee safety equipment, and a variety of other sensors to help businesses monitor workplace conditions or ensure employees are following established safety protocols—especially when the workplace is remote or unusually dangerous, such as Construction site or oil rig.
Improve health care. The healthcare industry has dramatically increased the amount of patient data collected from devices, sensors and other medical equipment. Such vast amounts of data require edge computing to apply automation and machine learning to access the data, ignore “normal” data and identify problem data so that clinicians can take immediate action to help patients avoid health events in real time.
transportation. Self-driving cars require and produce 5 terabytes to 20 terabytes of data per day, collecting information about location, speed, vehicle condition, road conditions, traffic conditions and other vehicles. And the data must be aggregated and analyzed in real time while the vehicle is in motion. This requires massive amounts of on-board computing — making each self-driving car an “edge.” Additionally, the data can help authorities and businesses manage their fleets based on local realities.
retail. Retail businesses can also generate large amounts of data from monitoring, inventory tracking, sales data and other real-time business details. Edge computing can help analyze this diverse data and identify business opportunities such as effective end caps or campaigns, forecast sales and optimize supplier ordering. Since retail operations can vary greatly in local environments, edge computing can be an effective solution for local processing in each store.
What benefits do edge gateways bring?
Edge computing solves important infrastructure challenges—such as bandwidth limitations, excessive latency, and network congestion—but edge computing has several potential additional benefits that could make the approach attractive in other contexts.
autonomy. Edge computing is useful in situations where connectivity is unreliable or bandwidth is limited due to the environmental characteristics of the site. Examples include oil rigs, offshore vessels, remote farms, or other remote locations such as tropical rainforests or deserts. Edge computing does the computing work on-site – sometimes on the edge device itself – such as water quality sensors on water purifiers in remote villages, and saves the data for transmission to a central point only when a connection is available. By processing the data locally, the amount of data to be sent can be significantly reduced, requiring much less bandwidth or connection time than might otherwise be required.
Data sovereignty. Moving large amounts of data is not just a technical issue. The journey of data across national and regional borders can create additional problems with data security, privacy and other legal issues. Edge computing can be used to bring data close to its source and within the confines of existing data sovereignty laws, such as the EU’s GDPR, which defines how data is stored, processed and disclosed. This can allow raw data to be processed locally, hiding or protecting any sensitive data before sending anything to the cloud or major data centers that may be located in other jurisdictions.
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