Edge computing brings data storage and processing close to where data is being generated or gathered instead of at the servers located at thousands of miles. It will deploy the services at the edge seamlessly and reliably by maintaining flexibility. Edge computing 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?
Edge computing 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
Edge computing makes it possible to handle distributed computing with high speed and scale. Some of its applications in Banks and Finance are:
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.
Data Privacy: While using cloud computing to transfer data to a central location, it is mandatory to follow the privacy and security guidelines to reduce the chances of stealing data. But still, the chances of data loss are always there. Edge computing makes this task easier. It enables the banks and financial institutes to deploy applications across local branch offices and reduce the need for cloud computing and also the chance of loss of data.
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Edge Computing in Manufacturing Industry
Edge AI in manufacturing provides fast response time to handle on-site accidents and prevent them from happening in the first stage.
Condition Based Monitoring: Data generated by the machine in manufacturing can help the manufacturer to track data patterns and make their decision more precise and valuable, but the primary issue that they are facing is in accessing data. Machines are generating a massive amount of raw data that overload the central server. Edge computing helps to clean the data at the edge and transfer only required data to the cloud. Hence it reduces that burden on the cloud and makes it fast to move the only valuable data. Monitoring the condition of their assets remotely helps manufacturers generate new revenue streams, such as maintenance service according to the service condition allowing the customer to pay services only for uptime.
Predictive Maintenance: Predictive maintenance is a process to predict the failure in advance before its occurrence so that maintenance can be conducted in advance of 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. Edge computing 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
Edge Computing in Retail extends the lifespan of stores. It enhances and improves retail operations and services.
Big Data and Analytics: Edge Computing allows the data collection and analysis at the edge that enables real-time data processing and analytics at the source of data generation itself. Hence make it easy for the retailer to use big data and artificial intelligence innovative technologies easily. It provides insights at their site that provide them with insights that help them promote the highest operational efficiency level.
Inventory Management: To provide a retail customer with a good user experience, it is necessary to provide secure and efficient inventory management services. For providing better services, it is a must to provide a product when a customer wants them, understanding the customer 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. Edge computing can continue to operate in low network availability also.
The automobile has shown some promising results using EdgeAI. A simplified example is a self-driving car. All the decisions are taken under a hood. From vehicle speed to collision chances, handling the steering wheel, analyzing engine health, and communicating the battery health.
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.
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 Identification: Using IoT sensors, it can detect whether the driver is in the car or not.
Driver Recognition: Advanced AI facial recognition algorithms helps to detect which driver is operating the vehicle. According to an individual’s preferences, the system can automatically adjust the seat, mirrors, and temperature.
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 brings more efficiency, accuracy, and patient output and improves the way the healthcare industry operates.
Health and Safety: Suppose a person travels from home to 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 at the spot and take recommended actions.
Edge Computing in Agriculture Sector
Some farms are located where the availability of high-speed internet and adequate resources are not available. Edge Computing can be used to have intelligent and modern agriculture that can process generated data at the edge and help the farmer make a decision.
Soil Quality: Examining the soil moisture using a mobile device by checking the farm location and the soil color.
Milch Animals’ Health: It tracks livestock’s health using sensor data such as temperature, heart rate, etc., and provides insights about the health condition.
Crop Health Analysis: A predictive computation engine, such as drones, can be used to check the health of leaves based on color and the pores it has, whether attacked by insects, pests, or rodents.
Examining Leaf’s Health: Drones can be used to check the health of leaves based on color and the pores it has, whether attacked by insects, pests, or rodents.
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.