What is Edge Computing?
Lately, we’ve perfected cloud computing as per our requirements. It offers us advantages like remote availability of data and services, unlimited storage capacity, backup and restoration of data, etc. However, as the amount of data that needs to be processed keeps increasing, it renders most of these benefits useless. This is where edge computing enters the scene.
Edge computing is basically taking computing to the edge of the network nodes, i.e., to the source of the data. For instance, consider a network in which multiple devices (nodes) are connected to a central server. Usually, in cloud computing, all the data from the nodes is transmitted to the servers and the return signal is again transmitted from the server to the nodes. This is manageable when the data that needs to be transmitted isn’t gigantic and the number of nodes do not overwhelm the server.
However, with the advent of technologies like Internet of Things (IoT), 5G, and Artificial Intelligence (AI), more and more nodes can be attached to a single cloud, and consequently, the data that needs to be processed is exponentially higher. Using conventional cloud computing in these circumstances will cause major lags in transmission and ultimately cripple the network.
This is why edge computing is introduced. It enables the nodes to process the data at their end and significantly reduces the network demand. This improves better performance, higher speeds, and lower latencies.
Scope of Edge Computing
Edge & Cloud
Let’s consider a real-life example to illustrate the concept of edge. In petroleum refinery plants, sensors are set up to detect high pressures in the pipelines, to provide for emergency shutdown instructions to prevent fatal explosions. So, when high pressure is detected in a pipeline, the sensors send a signal to the cloud server located in a remote facility, and in response, the cloud sends a signal to engage the emergency shutdown. However, even in an ideal case, there is a significant time lag that cannot be permitted in emergency situations.
Instead, if we were to use edge computing, we would be able to place the emergency shutdown instructions local to the sensor. This will reduce significant downtime, loss of lives, money, and property. This is just one example of the vast potential of edge computing.
Edge & AI
Another application that can be optimized with edge is visual recognition. Consider the security cameras in your streets, there are many such cameras all over your city. Now, increase the number of these cameras exponentially and imagine just how much data all of them would produce together. If we employ cloud computing in this case, that much data going back and forth between the server and the nodes will result in high transmission time and delay in response time.
However, if we could bring the visual recognition capability of the server to the camera itself, it will be able to process the images by itself and the amount of data that needs to be transmitted to the server will notably decrease. But “how to equip the camera with visual recognition?” is a pertinent question. The answer is Artificial Intelligence.
AI systems can be trained to recognize images and videos and provide a cognitive response. Normally, cloud AI networks rely on Artificial Neural Networks (ANN) that are trained with certain data sets (i.e, if a system is trained with ‘names of animals’ data set, it can identify and name any animal image produced). With edge, we can deploy a copy of the trained ANN onto the camera itself.
To put this into perspective, consider a factory that uses cameras to distinguish defective products from perfect ones. The server to which the camera is connected is trained to recognize faulty ones and working ones. Conventionally, the camera would capture the image of a product, send it to the server, wait for a response, and then categorize it.
However, upon implementing edge computing, the camera can process the image by itself and differentiate the faulty ones. So, now data has to be reported only when defective products are recognized. This reduces network demand and improves response time. The same functioning can also be used in audio recognitions, censors, robot control, etc.
Edge & Mesh
The biggest challenge of implementing IoT, 5G, or any new cutting edge tech is, providing connectivity to numerous devices on a single network. The advanced the technology, the more connected devices it supports. Usually, in a network, all the devices are connected to a single wifi router; this impedes the speed of the network. Edge combined with mesh allows a more optimized approach.
Edge computing allows devices to be interconnected dynamically to facilitate data exchange. For instance, consider a greenhouse whenever temperature and humidity sensors are employed. Normally, each of these sensors must be connected to the server, this would require a lot of infrastructure. However, if the sensors can be interconnected to edge computing devices, then, only one connection needs to be set up with the server. This results in saving a lot of infrastructure and maintenance costs.
Where can Edge Computing be applied?
Edge computing has many use cases owing to its features like High speed, low latency, improved security, scalability, versatility, and reliability.
In today’s world where a wireless network is the backdrop of every innovation and business process, a network with technical glitches is irrelevant. Edge computing offers the best way to navigate and use a network by bringing processing right to the user. Below are a few applications of edge computing that will revolutionize the future:
The mere picture of a car running with no one behind the wheel is just inconceivable. Nevertheless, it is close to becoming a reality. How many times have you experienced sudden, unpredictable encounters while driving? More times than we can count right! If we, with all our reflexes, at times falter in the face of the unprecedented actions of other drivers, how can a machine which is programmed and lacks ‘instincts’ handle roads where so often uncertainty is the only rule?
However, as it stands, there are no bounds to the intelligence of a computer system. The major question is if the network can catch up with the intelligence of the system. Advanced systems monitor multiple aspects in a matter of seconds, even milliseconds or less. So, if the network cannot catch up, automatic cars will never leave the garage. For such a time, edge computing is necessary. Its processing time is unparalleled and with efficient latency, it assists the machine in taking quicker decisions.
Industries: For industries like petroleum, oil, or gas which possess a high explosion risk, monitoring needs to be local and timely. In the unfortunate cases of delay due to the malfunctioning of remote servers, the losses are too high. So, edge is a reliable approach to monitor industries.
Healthcare: Data privacy is a huge metric that is relevant to every technology that is being dispensed in the medical field. To monitor patient health and run diagnostics, much data is compiled in servers and these become an imminent threat to patient privacy. However, edge computing wouldn’t need the information to be stored on a 3rd party cloud; thus promoting security of data.
Everything ‘smart’ about our homes, enterprises, industries and networks can be accessed better with the use of edge computing. It will help monitor energy use and analyze our consumption in real-time with the use of edge-connected sensors and IoT devices. This is often more advantageous for industries as they can measure the peaks and valleys of consumption and plan wisely. With the use of edge computing, smart girds and homes can be designed to function in a ‘green’ way and IoT will become a closer reality.
Nevertheless, edge computing is already being adopted into mainstream networking with ease and it’s going to grow more prevalent in the future. A few drawbacks like increased requirement of hardware and vulnerability to attacks are but hurdles in the way to its greatness. With such potential and more, Edge computing is definitely ‘the next big thing!’