Overview of Edge Computing
It brings data storage and processing close to where data is being generated or gathered instead of at the servers located thousands of miles away. It will deploy the services at the edge seamlessly and reliably by maintaining flexibility. It is more secure than cloud computing as there is no need to transfer data; thus no fear of data loss while moving it from an edge to the cloud. Thus it provides faster insights and business benefits, reduces response time, and improves the overall service.
A distributed computing paradigm in which processing and computation are performed mainly on classified device nodes. Click to explore about, The Impact of Edge Computing on IoT
What are the Applications of Edge Computing?
It provides values for smart IoT applications across various industries. It delivers high security, productivity, and improved performance to enterprises.
Edge Computing in Banking and Finance Industry
It makes possible to handle distributed computing with high speed and scale. Some of its applications in Banks and Finance are:
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ATM Security: Edge AI can be used to improve ATMs’ security, such as the video feed can be analyzed at the edge by integrating image recognition on ATMs. There is no need for human intervention. It is also not required first to transfer the data to the cloud. If the ATM tempers anyway, it will automatically shut down as soon as possible before any mishappening occurs. And then it alerts the bank so that they can take action by contacting law enforcement.
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Data Privacy: When using cloud computing to transfer data to a central location, it is mandatory to follow privacy and security guidelines to reduce the chances of stealing data. However, the chances of data loss are always there. Cloud computing makes this task easier. It enables banks and financial institutes to deploy applications across local branch offices and reduces the need for cloud computing and the chance of data loss.
Edge Computing in the Manufacturing Industry
Edge AI in manufacturing provides fast response time to handle on-site accidents and prevent them from happening in the first stage.
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Condition-Based Monitoring: Data generated by machines in manufacturing can help manufacturers track data patterns and make their decisions more precise and valuable, but the primary issue they face is accessing data. Machines are generating a massive amount of raw data that overloads the central server. It helps to clean the data at the edge and transfer only the required data to the cloud. Hence, it reduces the burden on the cloud and makes it fast to move only valuable data. Monitoring the condition of their assets remotely helps manufacturers generate new revenue streams, such as maintenance services according to service conditions, allowing the customer to pay services only for uptime.
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Predictive Maintenance: Predictive maintenance is a process that predicts failure in advance before its occurrence so that maintenance can be conducted before a potential breakdown. There are many challenges to making it happen because there have been challenges to integrating insights from operational technologies into IT systems. It can make it possible to process the data closer to the device where it is generated, at the edge, and thus avoid the cost of transporting data to the cloud and improve data accessibility.
Edge Computing in Retail Industry
Implementing it in Retail extends the lifespan of stores. It enhances and improves retail operations and services.
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Big Data and Analytics: It allows data collection and analysis at the edge that enables real-time data processing and analytics at the source of data generation itself. Hence, it makes it easy for the retailer to use big data and artificial intelligence innovative technologies easily. It provides insights into their site that provide them with insights that help them promote the highest operational efficiency level.
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Inventory Management: To provide a retail customer with a good user experience, it is necessary to provide secure and efficient inventory management services. To provide better services, it is a must to provide a product when a customer wants them, understanding the customer's need and availability. Using in-store intelligent video image recognition, AI can track the inventory system and accordingly can take action. Such as if overstock items are available, then flash sales can be offered for in-shoppers. It can continue to operate in low network availability also.
The Applications powered by Artificial Intelligence raises the data center's technical requirements, which generates high costs. Click to explore about, Artificial Intelligence in Edge Computing
Edge Computing in the Automobile Industry
The automobile has shown some promising results using EdgeAI. A simplified example is a self-driving car. All the decisions are made under the hood, from vehicle speed to collision chances, handling the steering wheel, analyzing engine health, and communicating battery health.
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Driver Assist: AI can recognize dangerous situations. It can alert the driver or take emergency control of the vehicle to prevent an accident. Blind-spot monitoring, emergency braking, cross-traffic detectors, and driver-assist steering can help avoid accidents and save human lives.
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Predictive Maintenance: Connected vehicles can do more than alert with check engine lights, oil lights, and low-battery indicators. AI monitors hundreds of sensors and can detect problems before they affect vehicle operation. AI can spot pending component failure before the failure could occur by monitoring thousands of data points per second.
Driver Monitoring
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Driver Identification: Using IoT sensors, it can detect whether the driver is in the car or not.
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Driver Recognition: Advanced AI facial recognition algorithms help to detect which driver is operating the vehicle. According to an individual’s preferences, the system can automatically adjust the seat, mirrors, and temperature.
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Driver Monitoring: By monitoring eye gaze, eye openness, and head position, AI detects distracted driving and alerts the car driver to keep their eyes on the road.
Edge Computing in the Healthcare Industry
It brings more efficiency, accuracy, and patient output and improves the way the healthcare industry operates.
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Health and Safety: Suppose a person travels from home to the hospital in an ambulance in critical condition. Transmitting patient data to the cloud in that condition is very difficult. Here Edge AI and computing can help process and analyze data on the spot and take recommended actions.
Edge Computing in the Agriculture Sector
Some farms are located where high-speed internet and adequate resources are not available. This can be used to have intelligent and modern agriculture that can process generated data at the edge and help the farmer make a decision.
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Soil Quality: Examining the soil moisture using a mobile device by checking the farm location and the soil color.
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Milch Animals’ Health: It tracks livestock health using sensor data, such as temperature and heart rate, and provides insights into its condition.
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Crop Health Analysis: A predictive computation engine, such as drones, can be used to check the health of leaves based on their color and pores, and whether they have been attacked by insects, pests, or rodents.
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Examining Leaf’s Health: Drones can be used to check the health of leaves based on their color and pores, and whether they have been attacked by insects, pests, or rodents.
Final Thoughts on Applications of Edge Computing
Edge Computing is a trending keyword in the technology sector and gained notice with the rise of the Internet of Things and the sudden glut of data such devices produce. It enables us to assign workloads to various machines rather than relying on a single system to deal with never-ending traffic from numerous devices.
- Discover more about EdgeOps Platform Benefits and Working
- Explore the IoT Use Cases across Industries
