The internet, its growth, and its relevance in our lives have increased by leaps and bounds in the last few decades. With the exponential growth of IoT, we realize the need for real-time computing. We are living in the era of gadgets such as the Amazon Echo, Google Chrome cast, and the Apple TV with an intent to relax and relieve ourselves and take assistance from them. But as it is said, everything comes at a cost (Not solely talking about the money you spent), and here it is the privacy, access, and control that we need to give them to allow them to function as desired by us. 

Allow us to introduce edge computing, the next buzzword that we shall pay close attention to.

With the onset of 5G, almost everything will be centralized, and that’s is going to be powered by edge computing systems. The scope is immense, varying from self-driving cars, video processing to artificial intelligence and robotics.

Let’s reach straight to the point now!

What is Edge Computing?

Edge computing is a distributed computing paradigm that optimizes Internet devices and web applications by bringing computing closer to the data source or the location where it is required. This reduces the need for long-distance communications between client and server, which means improved response times. In technical terms, it reduces latency and bandwidth usage.

Gartner defines edge computing as “a part of a distributed computing topology in which information processing is located close to the edge – where things and people produce or consume that information.”

“With enhanced interconnectivity enabling improved edge access to more core applications, and with new IoT and industry-specific business use cases, edge infrastructure is poised to be one of the main growth engines in the server and storage market for the next decade and beyond” says Kuba Stolarski, a research director at IDC, in the “Worldwide Edge Infrastructure (Compute and Storage) Forecast, 2019-2023” report.

What is the need for Edge Computing?

The best advantage companies can draw from edge computing is the ability to process and store data faster. This improves the real-time efficiency of applications that are crucial for the business. With edge computing, the tasks that were once slow, such as facial recognition on the smartphone, can get faster due to the local processing of related algorithms on an edge server. 

With edge computing, hardware and services can process and store locally. An edge gateway then sends only the relevant data back through the cloud, reducing bandwidth needs. It can also send data back to the edge device in the case of real-time application needs.

The edge devices may include smartphones, laptops, IoT sensors, security cameras, or any other IoT device such as your smart vacuum cleaner, for that matter.

Usage of Edge Computing 

• Autonomous vehicles
• Smart Cities
• Predictive maintenance
•  In-hospital patient monitoring
• Cloud gaming
• Content delivery
• Traffic management
• Smart homes

Use Cases for AI & Edge Computing

• Precision monitoring and control of manufacturing machinery is one example that would be well suited to using AI at the edge. This leverages AI because it requires very large amounts of sensor data to be collected and analyzed. Simultaneously, we need the back-end processes such as changes in the machinery, including fine-tuning of movements, temperature maintenance, vibration control, etc. It is worth noticing that all of this needs to be done in real-time. In a high-speed production line, latency must be kept to a minimum, and therefore doing the data processing closer to the manufacturing plant is highly valuable.
• Video analytics – video surveillance, facial recognition and flow analysis. In almost all industries and sectors, video analytics is used to track customer footfall and analyze their buying patterns. For surveillance, also we need advanced pattern and facial recognition. All of this can achieve swiftness and precision only with significant computing power. And edge makes it possible.
• AI Virtual Assistants we are enjoying this phase of integration of smartphones and AI-powered virtual assistants like Amazon’s Alexa and Google’s Assistant. Our household has modernized and is becoming a full-fledged integrated network in itself. We can imagine what service level shall be expected from our network providers for fast and accurate processing of these systems. With edge computing architecture embedded in their networks, companies can improve performance significantly and reduce latency. With edge computing, AI virtual assistants won’t have to send processing and data requests to a centralized server. Instead, they can distribute the burden among edge data centers while performing some computing functions locally.

This definitely seems to have immense potential with AI. There is no way we can say no to the convenience and lifestyle we all have chosen with these IoT-based devices. Edge computing is going to make it better for us and shall be able to handle the immense budder it is going to undergo with all of us getting smart with our smart devices.

“Edge computing has evolved significantly from the days of isolated IT at ROBO [Remote Office Branch Office] locations,” says Kuba Stolarski, a research director at IDC, in the “Worldwide Edge Infrastructure (Compute and Storage) Forecast, 2019-2023” report.