Exploring Edge Technology: Real-Life Applications and Benefits
Edge computing refers to the practice of transferring operations from the cloud to local locations, such as an IOT device, a user’s computer, or any other nearby device. Data processing, analysis, and transfer at a network’s edge are made possible by edge computing. Instead of transferring the data far away to a centralized data server, the goal is to analyze it locally, closer to where it is stored, in real-time, without delay. It can handle massive amounts of data processing quickly and efficiently. All of the computations that the IOT systems do are done in the cloud using data centers. Users are never even aware that they have reliable connectivity. Edge computing is to take advantage of artificial intelligence and 5G wireless technologies to offer quicker response times, less latency, and easier computer maintenance. It takes cloud computing to the next level by using remote servers housed on the internet to store and process data instead of local servers or personal computers, which comes into play in this situation. As a result, there is less of a communication gap and less use of cloud resources.
We are all quite familiar with the Internet of Things (IoT), which is a network of connected objects with interconnectedness and communication capabilities. These gadgets share and gather data, which gives them the ability to carry out particular tasks. IoT devices include wearables, industrial machinery, smart appliances, sensors, and more. For processing and analysis, they typically send data to cloud platforms or centralized servers. However, delays brought on by data transmission to centralized servers, security issues, and dependence on internet connectivity are the main issues with it. Edge computing was developed as a solution to this problem, putting processing power as near as feasible to the data source and the action points. There are several cutting-edge prospects in the healthcare industry. Currently, a lot of raw data from monitoring devices (such as glucose monitors, health tools, and other sensors) need to be kept on a third-party cloud because they are either not connected at all or are not connected at all. Healthcare professionals have security worries about this. To preserve data privacy, an edge at the hospital site processes data locally. Edge also makes it possible for practitioners to create 360-degree view patient dashboards for complete visibility and receive real-time notifications about odd patient patterns or behaviors via analytics and AI.
By processing data locally, close to the data source, edge computing differs from purely centralized server systems. Additionally, it is perfect for situations where real-time processing is essential, including industrial automation and driverless cars. Edge computing minimizes latency by reducing the distance over which data must travel. This enhances IoT by resolving latency and facilitating real-time activities. However, this has security flaws, which is the issue.
Let’s use the installation of a smart security camera outside a house as an example. Video footage of any movement or action is captured by this camera. Previously, all of the video data from this camera would be sent to a central server that was probably situated somewhere in the cloud or a data center. After processing and analyzing the data, the server would notify the camera with pertinent information, such as identifying a person or a bike. This is fixed by edge computing, which positions a tiny computer—similar to a mini-server—directly in front of the camera. Without sending the video data far away, this mini-server can handle it locally. Thus, the mini-server doesn’t need to wait for a remote server to process footage to identify a human walking past the camera and sound an alarm. Since local processing happens at the edge, it saves bandwidth and lowers costs while also improving reaction time and requiring less data to be sent over the internet. Real-time decision-making is vital for applications such as industrial robots, self-driving cars, and critical infrastructure monitoring. These operations are made possible instantly by edge computing. Let’s look at a couple more instances:
When it comes to streetlights, those that have sensors integrated into them can change their brightness without the need for external servers in response to local factors like traffic or weather. Wearable medical technology, such as heart rate monitors, can interpret data locally and transmit alerts to medical professionals or emergency services. Furthermore, when it comes to retail establishments, in-store cameras can instantaneously assess customer behavior and offer tailored recommendations. These systems guarantee quick reactions to the surroundings by eschewing cloud-based analysis, which causes delays. Locally processed data also guarantees smooth experiences, which is especially important for real-time communications. It’s similar to having an intelligent brain at the information source. If we talk about cloud gaming the delay between a player’s activity and the game’s reaction is known as latency, and it is introduced by data transmission across large distances and is overcome by Edge Computing. It enhances players’ experience by building edge servers as close to players as feasible is what cloud gaming businesses want to do to minimize latency and offer a completely responsive and immersive gaming experience.
Autonomous vehicles rely heavily on robust computers. They gather information from cameras and sensors in the car, interpret it locally, and make snap judgments to avoid crashes. Fleet car computers equipped with rugged edge PCs connect to the CANBus network. They collect rich data, including mileage, vehicle speed, engine condition, and other details. Fleet managers use this data to save operating expenses and improve fleet performance. Additionally, it enhances security by averting terrorist attacks; real-time analysis of surveillance data facilitates the rapid identification and response to threats. Truck convoy autonomous platooning will be probably one of the earliest applications of autonomous vehicles. To cut down on traffic and conserve fuel, a group of trucks travels in close formation behind one another in this instance. All vehicles, except the front one, will be able to communicate with each other with very little delay thanks to edge computing, which will eliminate the need for drivers.
The Internet of Things (IoT) is used by smart homes to gather and process data from all over the house. This data is frequently transferred to a centralized remote server for processing and archiving. Nevertheless, there are issues with backhaul cost, latency, and security with the current architecture.
Sensitive data can be processed at the edge and backhaul and roundtrip times are decreased by utilizing edge computing and moving the processing and storage closer to the smart home. For instance, voice-activated assistants, like Alexa from Amazon, would answer far more quickly. Different gadgets in a smart home interact with one another and carry out user commands by moving data processing closer to the devices, edge computing reduces latency. The response time of a smart light switch or thermostat becomes nearly instantaneous. Say your smart doorbell depends only on a cloud server located far away. As the internet connection fails, your doorbell will also malfunction. Important features, such as doorbell notifications, can operate even in the absence of internet connectivity thanks to edge computing.
Manufacturers want to be able to analyze and spot changes in their manufacturing lines before a failure occurs. By moving data processing and storage closer to the equipment, edge computing is helpful. As a result, real-time analytics and low-latency machine health monitoring are made possible by IoT sensors. Edge computing can assist businesses in more effectively managing their energy usage and will be a key component of smart grids as they become more widely implemented. Energy use in factories, plants, and offices is being tracked and analyzed in real time through the use of sensors and Internet of Things devices linked to an edge platform. Businesses and energy providers can make new agreements, such as running powerful machines during off-peak hours to reduce electricity consumption, with real-time visibility. This may result in a company using more green energy (such as wind power). Production line optimization, predictive maintenance, equipment health monitoring, and overall efficiency are all made possible by it in the industrial sector.
Edge computing is also tremendously helpful in Remote asset monitoring for the oil and gas sector. Oil and gas failures can be disastrous. Thus, it is necessary to keep a close eye on their assets. But gas and oil facilities are frequently situated in isolated areas thus real-time analytics with processing much closer to the asset is made possible by edge computing, which reduces the need for reliable access to a centralized cloud.
Consider that you are streaming music or downloading a video from the internet. It is stored near you rather than being retrieved from a central server that is far away. The edge is the term for this adjacent storage, which is similar to having a mini-library in your neighborhood. Delivering material can be considerably enhanced by caching content, such as web pages, audio, and video streams, at the edge. There is a significant reduction in latency. To ensure network flexibility and customization in response to user traffic demands, content providers are striving to distribute content delivery networks (CDNs) even farther to the edge. CDNs provide a seamless experience by adjusting to user demands. To put it briefly, edge caching makes the internet feel more like a nearby neighborhood than a far-off city by bringing your favorite stuff closer.
Envision a busy metropolis where the need for public transit varies. Real-time analysis of passenger data, traffic patterns, and historical trends is made possible via edge computing. Bus frequencies can be dynamically changed using this information. It results in more efficient bus timetables that correspond with demand, cutting down on commuter wait times. More efficient municipal traffic control may be possible with edge computing. Managing the opening and closure of additional lanes, adjusting bus frequency in response to demand variations, and eventually controlling the flow of autonomous vehicles are a few examples of this. By eliminating the need to transfer substantial amounts of traffic data to the central cloud, edge computing lowers latency and bandwidth costs. Edge devices handle data locally rather than transmitting all sensor readings to the cloud. By transmitting only pertinent data, bandwidth is conserved and network congestion is decreased. Edge computing strengthens the resilience and efficiency of the interconnected world by enabling localized decision-making.
Author
Professor, Dr.Kalpana Agrawal
Management, NRI Group of Institutions