Data and insights are valuable assets for any company. Today, data-driven decision-making has penetrated into every aspect of our lives.
Traditionally, enterprises collect data from disparate IoT devices and sensors, collect it in a central location such as a data lake or data warehouse, and perform calculations to derive insights. What if organizations could eliminate the data centralization/integration step and jump directly to the calculation phase? This approach, called “edge computing,” enables organizations to:
autonomous machine behavior
Higher level of data security
Reduce data transfer costs
What is edge computing?
Edge computing is a computing method that performs calculations on non-central components of the system, such as sensors, switches and various connected devices. In other words, insights are gained closer to the data source, rather than relying on a central location thousands of kilometers away.
With the emergence of IoT technology, organizations are collecting and calculating ever-increasing amounts of data. But transferring data to a central location and transferring insights back and forth to the edge takes time. Edge computing, on the other hand, provides insights at the edge of the network.
Why is edge computing important?
Edge computing enables faster decision-making, especially in low-bandwidth situations. Edge computing is an important area of investment for tech giants, given that businesses increasingly rely on automated, data-driven decision-making.
Industries such as retail, energy, security, manufacturing, and logistics can benefit from rapid decision-making enabled by edge computing. For example, when a self-driving car encounters an obstacle on the road, it needs to make a split-second decision to brake. In this case, decision-making needs to be faster than any cloud computing solution. Enterprises are deploying sensors and smart devices at the network edge to help compute data faster.
Edge computing is not just about making decisions in milliseconds. Today, the amount of data collected from various devices and sensors is increasing rapidly. With limited bandwidth between servers and edge devices, data transfer speeds may not be sufficient for time-sensitive applications.
Edge computing is an area where tech giants are investing heavily:
In January 2020, Apple acquired Xnor.AI, an edge-focused artificial intelligence startup. Apple plans to run deep learning computing models on edge devices such as mobile phones, IoT devices, cameras, drones, and embedded CPUs.
Google Cloud and AWS both have products focused on edge IoT.
How does edge computing work?
The workflow of edge computing tools generally follows the following pattern:
Sensors or devices at the edge collect data
In-device compute capabilities perform computation at the edge
If the device needs to take action, it does so based on the calculations
Relevant data (but not all data) is transmitted from the edge to the cloud, so enterprises can understand the big picture by aggregating aggregated data from thousands of devices (within bandwidth constraints).
How is edge computing different from regular computing?
Edge computing has similar capabilities to regular computing applications, except where the calculations are performed. One major difference is that edge computing applications need to work on edge devices with limited memory, processing power, or communications. These applications are optimized to work within these constraints.
What are the advantages of edge computing?
Advantages of edge computing include:
Faster, autonomous decisions because insights are identified at the data source, reducing latency
Reduces storage and management costs of centralized data since less data is stored centrally
Data transfer costs are lower as less data is transferred to the central data warehouse
Better security/privacy as granular data (e.g. video clips) is not stored or transmitted
Edge computing use cases
Intelligent monitoring: Enterprises can improve security with real-time intrusion detection edge services. By using raw images from security cameras, edge computing can detect and track any suspicious activity.
Remote monitoring and maintenance: Industries such as energy and manufacturing may require immediate response when any machine breaks down or requires maintenance. Without the need to centralize data calculations, organizations can more quickly identify signs of failure and take action before any bottlenecks occur within the system.
Retail customer behavior computing: Retailers can leverage data from a range of sensors, including parking lot sensors, shopping cart tags and store cameras. By running calculations on the data collected from these devices, retailers can provide personalized services to customers.
Keywords: edge computing gateway
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