Here Are Top 10 Edge Computing Trends We Can Expect to See in 2024:

1. Emergence of Edge-as-a-Service

2. Rise of Cloud-Edge Integration

3. Stronger Focus on Data Security

4. Higher 5G Adoption

5. Rise in Deployment of Edge Containers

6. Application of Edge Data Analytics

7. The Edge-AI Integration

8. More IoT Edge Computing Solutions

9. Blockchain-Edge Solutions

10. Growth in Edge Computing Data Centers

Final Thoughts

The post Top 10 Edge Computing Trends Shaping the Digital Landscape in 2024 appeared first on Tech Research Online.

What is Edge Computing?

Edge computing is a distributed information technology (IT) architecture in which client data processing occurs as close to the originating source as possible at the network’s periphery. Simply put, it is a matter of location. Here, data production occurs at a client endpoint in traditional enterprise computing.

For example, consider a user’s computer. In this case, the data transfer occurs across a WAN, such as the internet, using the corporate LAN, where the enterprise application stores and processes it. The results of that work are then communicated back to the client endpoint. For most common business applications, this is still a tried-and-true approach to client-server computing. Let us see how can edge computing be used to improve sustainability.

How can Edge Computing be Used to Improve Sustainability?

In recent years, cloud computing has become popular, with numerous businesses migrating to cloud platforms. However, its rapid growth has serious environmental risks that are very concerning.

Although centralized data centers are important to cloud computing infrastructure in the current times, there lies a bigger problem that they cause. The Cloud now has a greater carbon footprint than the airline industry. A single data center can consume the equivalent electricity of for 50,000 homes. In such a case, edge computing can help improve sustainability.

Here’s how edge computing can be used to improve sustainability. Edge computing can help improve outcomes on each of the three pillars of sustainability, namely: Environmental, Social, and Economic.

1. Reduced Energy Consumption

Edge computing helps reduce the amount of data transmitted to and from the cloud. It also reduces the impact of this traffic on the network. Moreover, it reduces operational costs for cloud service providers (CSPs). This is, especially, important to use IoT devices and other digital transformation technologies for industrial 4.0 efficiently.

Here are some other ways how edge computing can be used to improve sustainability!

• • • Saves money by reducing cooling costs
    • Reduces time for processing requests due to the involvement of fewer servers
    • Eco-friendly by reducing the carbon footprint

2. Reuse Existing Hardware

You can use (or reuse) existing hardware with edge computing. To save money and cut carbon emissions, you can use the servers, switches, storage, and software that are already in use. It’s not necessary to spend time looking for new technology or building entirely new infrastructure. Instead, all you have to do is plug into the existing hardware, which increases sustainability by reducing the demand for new hardware production.

3. Improved Resilience and Offline Working

This is another way where edge computing can improve sustainability. It improves the resilience of industrial systems by allowing them to function offline. Thus, even if you don’t have internet access, your system can still function normally. If you’re wondering which factors have made edge computing cheaper and easier, this is one of them.

4. Reduces Latency and Allows More Efficient Services

By ensuring all processing takes place locally and close to where users live, edge computing reduces latency. Less latency makes it possible to create services that are more efficient while consuming fewer natural resources. We can enhance sustainability by reducing waste and developing more intelligent services.

5. Improved Security and Privacy

Edge Computing improves the security and privacy of your data. Using it, you can store data on the edge of a network. It can help prevent hackers from gaining access or stealing your data as you’re not storing data in one central location. This aspect of edge computing is improving social sustainability by improving the security of personal data.

6. Scalability

Since one can scale edge computing up and down as required, it is able to handle peak traffic times. It is scalable in terms of hardware as well as software.

What Factors have made Edge Computing Cheaper and Easier?

The following factors have made edge computing cheaper and easier:

• • • Edge computing enables operations technology personnel to process data at the network’s edge, instead of processing important data in the cloud or through a centralized data warehouse.

• • • Since some business-critical applications will require real-time data, employment of physical or virtual computer infrastructure at the network’s edge to reduce required bandwidth access data centered centrally is necessary.

• • • Edge computing reduces data transit and allows the filtration of sensitive data locally, allowing businesses to create a security and compliance architecture that matches their needs.

• • • IoT and edge computing are driving the next wave of data center modernization and enhancement. Virtualization and edge computing allow organizations to take benefit of current gadgets without abandoning traditional systems.

Advantages of Edge Computing

Following are the various advantages of edge computing:

• • • Lower latency: Data travel is reduced or eliminated as a result of edge processing. This can speed up insights for use cases requiring low latency and complex AI models, like fully autonomous vehicles and augmented reality.
    • Cost savings: When compared to cloud computing, local area networks offer businesses greater bandwidth and storage at lower costs. A further benefit of processing at the edge is that fewer data must be transferred to the cloud or data center for additional processing. As a result, both the cost and the amount of data that must travel are reduced.
    • Model accuracy: AI depends on highly accurate models, particularly for edge use cases that call for instantaneous action. Lowering the amount of data fed into a model typically solves the problem of a network with insufficient bandwidth. Reduced image sizes, skipped frames in videos, and lower audio sample rates are the results of this. Data feedback loops can be used to increase the precision of AI models when they are deployed at the edge, and several models can operate at once.
    • Broader reach: For traditional cloud computing, an internet connection is a requirement. However, edge computing can process data locally without an internet connection. As a result, computing can now be used in previously inhospitable or remote locations.
    • Data sovereignty: Edge computing enables organizations to keep all of their sensitive data and compute inside the local area network and company firewall by processing data where it is collected. As a result, the risk of cloud cybersecurity attacks is decreased, and strict data laws are better complied with.

Difference between Edge Computing and Cloud Computing

First and foremost, it’s important to recognize that cloud and edge computing are two distinct, non-replaceable technologies. While cloud computing involves processing data that is not time-driven, edge computing involves processing data that is not time-driven.

In remote areas with poor or no connectivity to a centralized location, people prefer edge computing over cloud computing due to latency. Edge computing offers the ideal solution for the local storage needed at these locations, which functions like a small data center. All in all, there are multiple benefits of cloud computing that one can leverage.

Additionally helpful to specialized and intelligent devices, edge computing. Although these gadgets resemble PCs, they are not typical computing devices with multiple functions. These intelligent, specialized computing devices react to specific machines in a specific way. However, in some sectors where quick responses are necessary, this specialization becomes a disadvantage for edge computing.

How can Edge Computing be Used to Improve Sustainability: Conclusion?

To conclude, the above pointers can well answer how can edge computing be used to improve sustainability. Edge computing can help improve sustainability. In the world of technology, there are numerous benefits of sustainability. For example,

• • • it reduces energy consumption,
    • improves product design and performance,
    • reduces business expenses and overall carbon footprint, and
    • offers economic as well as social benefits.

The post Why do we need Edge Computing for a sustainable future? appeared first on Tech Research Online.

The main advantage of edge computing is that it reduces data transfer time between devices and the cloud by processing, storing, and analyzing at the edge itself instead of sending all data to the cloud for processing.

In this blog post, we explore what edge computing is, why it’s important, how it works and where you can use it in your organization today

Why is Edge Computing Important?

Edge computing is a fundamentally different approach to managing data in the cloud than we’ve been doing for years. Rather than pushing data to remote servers, businesses can tap into the cloud to store and process data themselves, then push data to the cloud whenever it’s ready. This means less overhead, less complicated infrastructure, and lower costs than the centralized cloud approach.

The popularity of edge computing has been growing in recent years, but it’s still in its early days. Edge computing is only just starting to become more mainstream. The promise of a much more flexible and cost-effective approach is why so many businesses are interested in exploring it.

Edge computing is not cloud computing

Edge computing is not cloud computing. Edge computing is a type of computing that exists on the network edge of a computer, while cloud computing is all about moving processing power and data of the network edge to central server farms.

Image Source: Flickr

Edge computing is typically used when you need storage or processing power like cloud services but cannot afford (or are not required) to pay someone else’s bill (e.g., Amazon Web Services). With edge computing, you have full control of your hardware and software. For example, if you have some data you want to be stored in a secure location and need computational power to process it, then you might buy servers and install them at your office or home with an edge switch (this is called local edge).

If you don’t have enough space on-site for all your data — and even if you do — edge switches allow businesses to take full control of what resources they allocate for each task as opposed to being limited by available server space provided by third-party providers like Amazon Web Services.

How Edge Computing Works

Before edge computing, data traveling between the devices in your ecosystem had to go through a centralized server. In the cloud approach, data is stored in a server somewhere and then sent over a network to your devices.

At the most basic level, edge computing is a new way of using the cloud to store that data locally and then push it back to the cloud when it’s ready. The edge device acts as a bridge between the devices in your ecosystem and the cloud. It uses cloud services to store and process data and then pushes it back to the devices that sent it. It’s a distributed system that relies on many different kinds of computing resources, including sensors, video cameras, phones, databases and other devices.

Why you should implement edge Computing in your organization

#Security

One of the advantages of using edge computing is that it increases security. When you use an edge device to process your data, you don’t have to worry about storing all your important information in one location. If your system were compromised, hackers wouldn’t be able to access records from a central server farm because they would need physical access as well.

#Lower bandwidth costs.

Another advantage of edge computing is that it helps business owners save money on bandwidth costs. This is because traditional cloud-based servers require an internet connection and a lot more bandwidth than an edge device does. Edge devices are simply connected via Wi-Fi or other local means, and they don’t rely on expensive internet connections like traditional computers do.

#Increased accuracy

Edge devices also offer accurate monitoring of what’s occurring on their respective networks, letting business owners see what’s happening with their systems at all times and make changes accordingly.

#Process data at lower costs

The move towards edge computing solves two pressing problems we face in today’s technology-driven world: physical distance and performance limitations. This decreases the need for remote servers by allowing users to access applications without having to go beyond their own networks. Some companies use edge computing simply as an efficient way of processing information while others see it as an opportunity to reduce costs by eliminating the need for employees who do not have access to cloud-based tools at their desks or offices.

#Simplify workflows

If you think about your typical workflow, you probably send a lot of emails on your own personal device. With edge computing, you may no longer need certain apps like Microsoft Outlook installed on your laptop because all of your communication can now take place via email from any device — all you have to do is check your email from your phone and save attachments directly from there instead of uploading them first into Dropbox or Google Drive.

How to Implement Edge Computing in Your Organization

As you can see, edge computing is about turning part of your existing hardware into an extension of the internet. You need sensors and other devices that can connect to the internet. And you need a device that can store sensor data and send it to the cloud when you want it. The device that stores data and sends it to the cloud can be a computer, server or even a smartphone. And the device that receives data from the cloud can be a sensor, a video camera or even a virtual assistant. You can use device-to-device communication, such as Bluetooth or Wi-Fi, to connect the devices. You can also use a cable to connect sensors and other devices to a computer.

Challenges to Implementing Edge Computing

#Cost of new infrastructure

Edge computing is a relatively new concept, and it can be difficult to implement. It requires your business to move from its current infrastructure to new networking hardware, which could put pressure on your company’s IT department.

#Limited internet speeds might interfere with the efficiency of your edge system

In addition, edge computing is limited by the internet speed at each location. So, if you have several locations in different countries that need to access cloud applications or other services from the same cloud provider, you’ll only be able to do so if the provider has a presence in all regions where you operate.

#Scalability

Edge is only limited to the specific location where it is installed. It would be difficult to scale it to every area of your business if you have a large organization or if your operations are spread across multiple physical locations.

In comparison, Cloud computing offers scalability and flexibility. Cloud providers offer more than just remote apps; they also offer a wide variety of other services such as data storage and backup, online document sharing and collaboration tools, video conferencing, CRM software modules, and many more. This gives businesses not only the technology they need but also the resources they want. It’s easy for them to build their own bespoke solutions that work best for their business and their customers.

This is not to say, however, that you should not implement cloud computing. Like any new innovation, edge computing suits some use cases more than others.

Let’s look at some of those use cases below.

Edge computing use cases

Edge computing solves many cloud computing challenges. It does this by bringing cloud computing to the edge, or the physical environment where your data is collected. And it brings cloud computing to specific devices, such as a car or smart cities.

The most common use case for edge computing is to store sensor data. Sensors are an important part of the modern Internet of Things ecosystem, and they generate a lot of data that might be useful for companies and other organizations. But sensors are usually connected to the internet by a wireless network. Sending sensor data to the cloud takes a few minutes. Storing sensor data in the cloud takes several days, even though it might only be used for a few hours. And then sending data back to the sensors can take several hours again. That’s not practical for businesses that need to react to sensor data as soon as possible.

Edge computing is a great solution. It allows you to store sensor data on the edge device and send that data to the cloud when you need it, so it’s immediately available when you need it. Plus, the edge device doesn’t have to be connected to a wireless network. That means it can stay in the field longer, generating more data.

The Future of Edge Computing

Edge computing is still in its early stages. Today, only 10% of companies use Edge computing. That number will rise to 50% in 2025. (Source). The popularity of this concept will increase as more people and organizations realize how it can make their lives easier in various ways.

It will also become more prevalent as IoT devices continue to flood the market. Eventually, most data will be generated at the edge. That’s why we expect edge computing to become an important part of the internet of things over the next few years and beyond.

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It will be more profound than any industrial or digital revolutions combined. Today, we can see how these technologies might shape the business ecosystem as the future unfolds before our eyes. In this blog, let’s understand how AI-driven IoT will get implemented effectively and profitably.

The most critical factor in this digitalized world is where the intelligence resides and how it influences IoT architecture.

AI and the IoT both hold futuristic visions as they both are one of the kind innovations; however, what makes them intriguing is the point where they converge.

The applications of AI and IoT are independently interesting, but their joined use holds even more potential. The IoT is getting more brilliant and smart with the fusing of AI.

Specifically, due to machine learning which a discovering insight in data with the help of IoT. AI is making a splash in IoT with a pile of the new product, an influx of investment due to the ever-rising tide of big business corporations.

Businesses that are currently working on their IoT strategy, assessing a potential IoT project, or trying to get value from a current IoT deployment should explore a role for AI

. The union of AI and IoT technologies is an amazing tool, AIoT, either at the Edge or in the Cloud. The objective for the Artificial Intelligence of Things is innovation to accomplish progressively proficient IoT operations, upgrade data management, and analytics to improve human-machine interactions.

If AI analytics is actualized appropriately it can change IoT data into valuable data for a better decision-making process. AI at the edge utilizes a compact architecture to drive local data-informed decision-making.

The more brilliant an edge gadget is it can process and store colossal amounts of data locally. This reduces the need for a business to do so somewhere else.

Some basic AI-empowered edge devices for unit volumes include head-mounted displays, consumer and business robots, smart car sensors, drones, surveillance cameras, and others.

Likewise, edge computing can reach out to include the processing intensity of PCs, tablets, mobile phones, smart speakers, and others.

The biggest players in the market including Google, Microsoft, Amazon, and others are effectively put resources into exploring different solutions for AI-empowered edge computing.

1. Improved Business Operational Efficiency

AI forecasts have proven to be profoundly effective and helpful for businesses to increase their operational productivity.

Furthermore, the in-depth insights acquired through AI can be helpful to improve business processes to expand operational productivity and diminished expenses.

Businesses use precise predictions about time and cost expending tasks and automate them to increase effectiveness levels.

Additionally, for businesses that are dealing with big-scale planes and ships, the insights acquired through AI can assist them with modifying their procedures, updating the stock on time, and improve equipment settings to save money on superfluous costs.

2. Avoiding Latency Problems

In the case of edge computing, there is no reason for businesses to move data to the cloud for processing as the issue of latency doesn’t exist.

This can quicken the real-time decision-making of businesses for applications such as plane monitoring, autonomous driving, medical imaging, and others. Real-time response is the essence of AI applications that are made by the continuous performance of IoT devices.

3. Predictive Analysis

Predictive analytics implies that the analysis is performed by understanding the existing information to predict the conceivable future.

Presently, IoT devices are being utilized by businesses to accurately predict any incidents or concerns such as equipment failure, and so on, without human intercession. Hence, IoT and AI are the establishments of predictive maintenance.

In any case, businesses that include artificial intelligence in their strategy will enable machines to perform better predictive analysis.

Such businesses will also have an option to identify and work on potential disasters and failures ahead of time.

This will diminish the odds of misfortunes exceptionally as conditions can be identified even before failures. This can prove as tremendous advantages for businesses in sparing expenses and dodge difficulties in their operations.

4. Greater Security

Businesses can maintain a strategic distance from the security dangers of the public cloud. This can be implemented by edge computing as it keeps sensitive information in the nearby IT ecosystem.

Also, an AI-empowered solution will be able to help the business identify inconsistencies at the edge of the system.

In the case of cyber-attacks, that attempts to target the system by attacking IoT devices can be quickly safeguarded by executing mitigation strategies.

AI-driven risk analysis can distinguish all the places that can be entry points for cyber attackers. Then is can proactively make plans all by itself to mitigate security issues.

Today, businesses that have already implemented automation such as AI and ML are creating and storing loads of data than ever.

This has created a need for businesses to sophisticated analytics enriched tools that are agile, flexible, and secure platforms to respond to the changing business needs.

Morden and traditionally business need to invest in an intuitive technology that can sense the needs of the business, users, and employees. It needs to simultaneously respond quickly and seamlessly.

IoT-based platforms are a perfect solution for smart technologies and industry-specific analytics. They not only help businesses expand while focusing on customer experiences.

Analytics and data services are powerful tools that can be used for automation by combined with business intelligence such as AI, ML, and IoT to meet the current market trends. Other advantages of implementing a real-time data management process are:

• • It enables data storage and boundary fewer business possibilities
  • Businesses can get fast actionable outcomes to scale right
  • Its self-serve data discovery helps gain rapid insights
  • It improves customer experience by optimizing and automating data stream engines
  • It can enable unparalleled visibility and transparency in the systems
  • It can analyze and act on important data from anywhere
  • It can provide real-time alerts on compliance and helps in fraud risk management
  • It gains actionable insights from an ecosystem of a self-learning platform

IoT and AI will change the existing business procedures for good. It will increase automation and in-depth analysis work in industries and organizations.

These enterprises will receive rewards of growth while making enormous profits. Hence, today, the need for importance is to make better strategies for business using AI and IoT or a better future.

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