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The rapid growth of the (IoT) Internet of Things tech and its computational capabilities have resulted in unprecedented amounts of data. Furthermore, this data volume is expected to increase as 5G networks expand the number of connected mobile devices.
Subsequently, the complex data generated by these linked devices has exceeded network and infrastructure capacities, making it challenging to drive actionable insights from it. The sheer volume requires more extensive and expensive connections to data centers and the cloud, causing latency and bandwidth issues. This is where Edge computing comes in. It provides a way in which data is processed and evaluated closer to the point of its creation, thereby reducing latency.
Edge computing and mobile edge computing in 5G networks give way to great opportunities in every industry. It creates opportunities for deeper insights and enables quick and more comprehensive data analysis and better user experiences.
What is Edge Computing?
Edge computing occurs at the edge of corporate networks, with “the edge” being where end devices access the rest of the web, like phones, laptops, and sensors.
At the edge, these devices connect so they can deliver data to, receive instructions and download software updates from a centralized data center or the cloud. As this traffic may not be time-sensitive- slower, less expensive connections, possibly over the internet, can be used.
The advantage of edge computing is the faster response time for applications requiring it and the slowing growth of expensive long-distance connections to storage centers.
The downside factor is security. Since the data is collected and analyzed at the edge, it is essential to include the security of the IoT devices connected to the edge devices and for the edge devices themselves. These devices contain valuable data, but they are also networking elements that, if exploited, could compromise other devices with stores of valuable assets.
How Edge Computing is Different from Other Computing Models?
The first computers were giant machines that could only be run via a computer extension, either directly or through terminals. With the coming of standard personal computers, computing could take place much more flexibly and be considered a supreme computing model. Here, applications ran, and data was stored locally on a user’s device or sometimes within an on-premise data center.
The advent of Cloud computing added benefits to this on-premise computing. Cloud services are centralized in a vendor-managed “cloud” (or collection of data centers) and can be obtained from any Internet device. Due to the distance between users and the cloud services data centers, cloud computing can introduce a delay in data transfer. Handling data on the machine where it’s generated would be of assistance. That’s where the difference between edge and cloud computing comes in. Edge computing minimizes this distance and brings computing closer to end-users, thus solving the latency problem while maintaining the nature of cloud computing.
To outline:-
-Early computing: Centralized applications that run on one giant computer.
-Personal computing: Decentralized applications running locally.
-Cloud computing: Centralized applications that run in data centers.
-Edge computing: Centralized applications run closer to the end-users on the network edge or the device itself.
How is machine learning different from artificial intelligence?
To better grasp RankBrain, you should first comprehend machine learning and artificial intelligence. The problem with both is that they are inextricably linked and, as a result, frequently misinterpreted.
In a nutshell, Artificial Intellect is the ability of machines to do activities that would typically need human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
Machine Learning is an Artificial Intelligence application that can learn independently without being explicitly designed. This is precisely what RankBrain does: it automatically knows and improves depending on previous experience.
Benefits of Edge Computing
Edge computing benefits in alleviating data security and privacy challenges, in addition to the lower latency that enables better immediacy around analytics. Businesses can lessen the effects of having all their data in one area by dispersing computer resources to remote facilities. Additionally, companies can comply with privacy laws in some regulated industries by not sending sensitive data across international boundaries. Another significant advantage for corporations is that edge computing can save costs through decreased bandwidth. When processing data at the edge, an enterprise can ensure that it only pays to transmit and store the valuable, necessary data while rejecting the low-value data.
Use Cases for Edge Computing:
Autonomous Vehicle
Self-driving vehicles need to make fast data-based decisions. For example, there is hardly any time to send an urgent request to the cloud data centers and have them return to the local network when a pedestrian is running in front of a car.
With edge services, decisions can be made much faster as it never sends requests back to the cloud. Thus, vehicles using edge technology can interact more efficiently because they can communicate with each other first, instead of sending information about traffic, detours, or accidents to remote servers.
Healthcare Equipment
Healthcare monitors help track a patient’s chronic condition and can save lives by instant alerts to caregivers in times of need. They also provide immediate medical access to the patient’s medical history without error. In addition, robots that assist in surgery must quickly analyze the data to help accurately. The consequences can be fatal if these devices rely on sending data to the cloud first. Thus, quick decisions can be made at the right time with edge computing solutions.
Smart Speakers
Smart speakers gain the ability to interpret voice commands locally and execute basic commands. Even if the internet connection goes down, one can adjust the thermostat settings and turn the lights on or off. Even civic authorities have implemented intelligent traffic controls, to run their roadways and create smart communities. Edge supports authorities such as private transportation, traffic agencies and public transformation departments, to manage the traffic flow by providing real-time information.
Security Alerts
Surveillance systems need to immediately alert any potential threats and activities
due to which they can benefit from edge computing technology. Through edge, requests are processed directly at the network allowing security providers to take prompt actions.
Management of Traffic
With Edge Computing, city traffic management can become more effective. For example, it allows optimizing bus frequency according to the demand fluctuations, managing autonomous car flows, and opening and closing of extra lanes.
Smart Homes
IoT devices are essential to smart homes as they gather and process data from all over the house. However, for further processing and data storage, it would be sent to centralized remote servers, which further causes latency, backhaul cost, and security issues. With edge computing, the data is placed much closer to the edge. Thus, voice-based assistant devices like Amazon’s Alexa would take a lot less time to respond.
Remote Asset Monitoring in the Oil and Gas Industry
Oil and gas facilities are usually placed in remote areas as failure in oil and gas industries can be disastrous. Therefore, it’s essential to keep a close check on their assets. Edge computing provides real-time information much closer to the asset, implying less dependency on high-quality connectivity to a centralized cloud. With the constant monitoring of assets and collecting reliable data, Algorithms can then analyze patterns in the working of the asset and predict what is likely to happen to an asset long before an event occurs.
Thus, businesses can use the insights acquired from edge analytics to minimize risks and use their machinery optimally.
Conclusion
With edge computing becoming essential, it’s also important to make sure that the edge devices themselves don’t become a single point of failure. Network architects need to build redundancy and provide failover contingencies to provide crippling downtime in the event of a primary node failure. The industry has come a long way to meet the demands of edge computing, and it is becoming mainstream. Moreover, its importance is likely to grow even more as real-time application usage becomes more prevalent.
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