![Top Slides on IOT Edge Computing and its Use Case in Industries [Free PPT and PDF]](https://www1.slideteam.net/wp/wp-content/uploads/2023/10/Banner-5.jpg)
0 Comment
Customer Reviews
Top Slides on IOT Edge Computing and its Use Case in Industries [Free PPT and PDF]
What is IOT Edge Computing?
The Internet of Things (IoT) edge sensors and devices transmit real-time data to a network. IoT edge computing eliminates cloud latency issues by processing data closer to its place of origin. IoT edge design also lessens latency while enhancing security and user experience.
A high-throughput network like 5G can be utilized with IoT edge to analyze massive amounts of data almost instantaneously, providing the user with a more comprehensive, immersive experience.
Contrarily, IoT edge can improve the performance of machinery and other devices that have an impact on human safety, keeping people safe even when only a small amount of data is exchanged.
Why is IOT Edge Computing Important?
Instead of sending data far away to be processed by a distant server, edge computing processes data close to its source. This addresses a number of significant issues, many of which are related to the latency that results from data having to travel great distances.
There could be serious safety concerns, for instance, if a business uses machines on its assembly line and processes their inputs on a remote, cloud-based server. It could take too long for an input to be received by the machine, delivered to the cloud server, processed, and then for the matching command to be sent back to the machine.
Serious damage could happen if the command is to tell the machine to cease working because a human leg is in the way.
The data only needs to travel a few yards instead of many kilometers with IoT edge, saving valuable time and improving safety.
This presentation will help you to share ideas about IoT Edge Computing in an accurate and stylish way. Let me show you some of the slides from this complete presentation deck.
Cover Slide
Cover slide is an important slide as it communicates the main theme which provides context for the viewer's experience.
A well-designed cover slide lures the viewer in captures the audience's attention and piques their curiosity, which encourages them to pay close attention the entire time.
Just add your company’s name and start!
Overview of IoT-Enabled Edge Computing
This slide provides a comprehensive overview of IoT usage in edge computing.
Introduction: IoT has changed data processing by adopting a distributed computing approach, particularly at the edge. This technology provides real-time insights through the analysis and processing of data at the device level and offers quick and reliable data processing with the usage of IoT sensors.
Examples of edge computing in IoT:
Traffic Management: The use of IoT sensors for traffic control is a great example of edge computing in action. It involves real-time management of supplemental lanes, autonomous vehicle traffic, and bus frequency. One of the key advantages is the lack of the need to send data to centralized locations, which leads to a large 48% reduction in bandwidth costs.
Asset Remote Monitoring: Another significant use of edge computing in the IoT is asset remote monitoring. This application uses a technique that is highly common in industries like the oil and gas industry to perform real-time analysis by processing data close to the remote asset. This tactic lessens the need for centralized cloud connectivity, enhancing the efficacy and responsiveness of asset monitoring.
Examples like traffic management and asset monitoring highlight practical advantages that the audience may easily understand, such as cost savings and increased effectiveness. It gives the speaker a simple and effective way to explain the value and promise of IoT in edge computing.
Â
Difference Between Traditional and IoT Edge Computing Capabilities
This slide showcases the difference between traditional and technology-enabled edge computing features that highlight evolution and advancement. It includes features such as:
Consolidated Workloads: Fragmented workloads were a feature of traditional edge computing. Consolidated workloads are now made possible by technology-driven solutions, which also improve resource usage and operational effectiveness.
Data Filtering: Data management was poor in the past. The advanced data filtering made possible by technological advancements allows for more accurate analysis and decision-making while avoiding data overload.
Open Architecture: Modern edge computing utilizes open architecture as a transition from closed, proprietary systems, fostering interoperability and scalability for a more flexible and future-proof ecosystem.
This slide contrasts conventional methods with contemporary technology advancements to clearly illustrate the evolution of edge computing. The important characteristics offer concrete examples that help the presenter illustrate the development and benefits.
Â
Key Components of IoT Edge Computing
This slide exhibits key components associated with IOT and edge computing to facilitate effective integration and data processing. It includes elements such as:
Data Management and Analytics: Enables in-the-moment data analysis at the network edge, enabling quick decision-making in emergency situations.
Edge Storage and Management: This provides local storage for efficient data caching and management in distributed networks.
Connectivity and Communication: Wi-Fi, Bluetooth, and cellular networks are supported for connectivity and communication, ensuring strong connections for interacting with IoT devices.
Edge Devices: These are in charge of utilizing IoT-based sensors and cameras to collect and process data.
This slide assists the presenter in outlining key points by clearly presenting essential elements for combining IoT with edge computing.
The visual presentation of key components enables a concise and well-structured presentation, improving the audience's comprehension of the key components for efficient data processing and decision-making.
Edge Computing Technologies for Network Infrastructure
This slide showcases other edge computing technologies to provide real-time data analysis. It includes elements such as fog computing, cloud of things, cloudlets, and micro data centers.
Fog Computing: Fog computing is a decentralized infrastructure for computing that extends to the network edge and optimizes data processing by bringing processing and storage capabilities closer to the data source. As a result, there is less need to send data to distant clouds, increasing productivity and lowering latency.
Cloudlets: At the network edge, small, mobile-friendly cloud data entities support resource-demanding mobile apps. Cloudlets improve performance for applications that need quick access to data by providing substantial resources with minimal latency.
Micro Data Centers: Conveniently located, small data centers that may supply small businesses with the basic elements. They are a viable solution for localized data processing and storage because of their compact size, which makes deployment and management simple.
Cloud of Things: Constructing a virtualized cloud architecture to carry out cloud-related functions closer to the data source, enabling cloud services at the edge. The creation of traffic warning alerts, quick rerouting, and traffic flow optimization are a few examples.
Owing to the effective introduction and description of many edge computing technologies on this slide, the presenter can illustrate a few options for real-time data processing.
It helps to organize difficult concepts into simple steps so that the audience can comprehend the advantages and applications of each technology.
Edge Computing in the Banking and Finance Industry
This slide represents an application of IoT and edge computing in the banking industry to improve speed and scale. It manages the distribution of computing with improved speed and security.
Applications:
ATM security: By reducing human participation and doing away with the need to send data to the cloud, ATM Security improves security. System shutdown in unusual circumstances is made possible through automation. Real-time video feed analysis with ATM image recognition integration is one example.
Data privacy: Reduces dependency on cloud storage by using IoT devices for edge computing, which greatly lowers the danger of data theft and leakage by 68%.
This slide is an effective tool for outlining how edge computing and IoT are combined in the banking industry. It gives a concise review of the advantages, especially about ATM security and data privacy, which improves the presenter's capacity to persuade the audience of these benefits.
A seamless and attractive presentation is made possible by the structured layout and clear points, highlighting the possibilities for increased speed, scalability, and security in banking processes.
IoT Edge Computing in the Retail Industry
This slide represents the use of IoT-based devices in edge computing in retail industries to increase operational efficiency. It includes elements such as big data, data analytics, and inventory management.
Deployment in retail expands the tenure of stores by improving operations and processes.
Big data and analytics:
This allows the businessman to optimize their operations and provide customers with higher-quality services.
These are essential because they allow for speedy data collection and analysis at the store's edge, which eliminates delays. Retailers can simply use big data and AI devices to acquire insightful information that facilitates more efficient retail operations.
Inventory management:
IoT makes excellent inventory management possible, enabling businesses to precisely match client expectations.
Utilizing in-store smart video recognition, for instance, to keep an eye on inventory helps quickly spot and address supply bottlenecks, ensuring uninterrupted operations and higher customer satisfaction.
This slide clearly and concisely outlines the advantages of IoT, focusing on increased productivity through big data analytics and efficient inventory management.
The audience is more engaged and more aware of the possibility of improved retail performance due to the concrete example provided about employing smart video recognition for inventory monitoring.
In the Nutshell
This blog explores IoT edge computing's transformational potential and its significant applications in a range of fields. We have looked at how using big data analytics and optimizing inventory management can improve operational efficiency in the retail sector.
Additionally, the banking industry's adoption of edge computing and IoT promises to improve security and data privacy while substantially reshaping current procedures.
IoT edge computing improves traffic management and asset monitoring in the transportation sector, demonstrating how real-time data processing at the edge may optimize these crucial systems.
IoT edge computing fundamentally changes the game by enabling industries to capitalize on the value of data at the point of generation. Its applications are numerous and look promising, from improving security to reinventing inventory management!
CLICK HERE TO GET A FREE PPT & FREE PDF!
For the best PowerPoint presentations and more, visit us at SlideTeam or call us at +1-408-659-4170.
We also offer customization services and pride in our team of amazing #PresentationDesigners. Submit your template request and experience their magic.
FAQs
What is IoT edge computing, and how does it differ from traditional cloud computing?
IoT edge computing involves processing and analyzing data locally, closer to its source, within IoT devices or gateways. This contrasts with traditional cloud computing, where data is sent to centralized cloud servers for processing. Edge computing offers faster response times, reduced latency, and decreased data transfer, making it ideal for real-time or low-latency applications.
How does IoT edge computing enhance data security and privacy in various industries?
IoT edge computing minimizes the need to transmit sensitive data to centralized cloud servers, reducing the potential points of vulnerability. Data processing and analysis at the edge ensure that critical information remains within a secure local environment, enhancing data privacy and mitigating the risks associated with data transfers over networks.
What are the key benefits of implementing IoT edge computing in industrial settings?
IoT edge computing optimizes industrial operations by enabling real-time data analysis, quicker decision-making, and improved operational efficiency. It reduces bandwidth usage, enhances data processing speed, allows for offline functionality, and facilitates autonomous decision-making at the edge, resulting in cost savings and a more resilient operational ecosystem.
How does IoT edge computing contribute to energy efficiency and sustainability?
IoT edge computing aids in energy optimization by enabling localized processing of data from sensors and devices within energy systems. Real-time analysis and decision-making at the edge allow for efficient energy consumption, reduced waste, and enhanced sustainability. This leads to a greener and more eco-friendly approach to energy management.
![Data Mining: A Definitive Guide [Free Template]](https://www1.slideteam.net/wp/wp-content/uploads/2023/09/BANNER-9-1013x441.jpg)
![Everything You Need to Know About 5G [Free Template]](https://www1.slideteam.net/wp/wp-content/uploads/2023/09/BANNER-6-1013x441.jpg)
![TOP 7 SLIDES: Product Launch and Promotional Event Marketing [FREE PPT]](https://www1.slideteam.net/wp/wp-content/uploads/2023/10/BANNER-1-1013x441.jpg)
