Edge Computing And IOT

Edge computing and IoT move resources from central data centers to the periphery, enabling local data processing before transferring it to higher-level nodes.

Thus, data collection and analysis is carried out not in a centralized computing environment (DC), but where data streams are generated.

How Edge Computing And IOT Works

In some ways, this technology can be compared to a measuring device at an oil field or a remote telecommunications facility: it brings computing resources closer to where the data is collected.

edge computing examples
Edge Computing Implementation Scheme,

The industrial automation market is leading the adoption of a range of new technologies, including augmented reality (AR), 3D printing , robotics , artificial intelligence (AI), cloud-based supervisory control and data acquisition ( SCADA ), and programmable automation controllers (PACs). When it comes to automation technologies, from the manufacturing floor to the supply chain and the heart of the enterprise, the Internet of Things is already connecting various points with intelligent sensors. The Industrial IoT provides information for maintenance, inventory, and transportation of products.

However, simply increasing connectivity and data flows is not enough to truly harness the potential of digital transformation. To gain a competitive advantage, manufacturing companies need to fully integrate industrial automation. Only then will they be able to transform the data collected in the IoT environment into valuable insights to enable faster, more accurate, and more cost-effective decision making. And to do this, they must move computing power to the edge of the network.

Benefits Of Edge Computing And IOT To The Data Center

Edge computing aims to move computing resources from a hyperscale cloud data center, which may be located at a significant distance (in the “core” of the network), closer to the user or device, to the “edge” of the network. This approach focuses on reducing network latency and accumulating computing power to process data near its source. By running on the edge network, mobile applications could make greater use of artificial intelligence and machine learning algorithms , whereas they currently rely entirely on the computing capabilities of mobile processors. In addition, computationally intensive tasks drain phone batteries much faster.

Edge computing is the rocket fuel for the IoT . It offers a number of benefits and potential opportunities:

  • Edge computing enables data to be analyzed and filtered closer to the sensors. Moreover, only relevant data is sent to the cloud;
  • Latency in the production process can be critical, for example, if a production line fails. Fast response times, measured in milliseconds, are critical to ensuring the safety of sensitive and precise operations. In such cases, waiting for the result from the IoT cloud platform is too long;
  • Edge computing means that, if necessary, sensitive data can be processed locally, where it is protected from direct network connections. This provides a higher level of control over the security and privacy of information;
  • Finally, the requirements for cloud storage capacity and network bandwidth are reduced, and the corresponding costs are reduced, since instead of sending large amounts of sensor data to the cloud, it can be processed directly at the edge.

Edge computing architecture has become the hub around which many computing tasks are concentrated. Its advantages include minimal network latency in data processing and the ability to work with large volumes of data, but at the same time it has its weaknesses – insufficient interoperability of the protocol stack and the lack of standardization. As a result, today the devices and applications that operate at the edge of the network are a set of autonomous edge ecosystems.

Edge architecture brings computing resources closer to data and devices. Many market experts view it as a key paradigm beyond cloud computing . There are some digital use cases that require extremely low latency, and this is where it shines over cloud services. However, the current diversity of interfaces and the lack of industry standards significantly slow down progress because they prevent devices and applications from interacting with each other.

2023: Global Edge Computing Market Reaches $15.96 Billion

In 2023, the global edge computing market spending reached $15.96 billion. The industry is showing exponential growth, as stated in a study by Fortune Business Insights, the results of which were published in mid-October 2024.

Edge Computing is the concept of creating computing power and storage resources at the point of data production. Edge computing systems can significantly improve application performance, reduce bandwidth requirements, and quickly extract analytics data in real time. This model allows organizations to improve security and productivity, automate processes, and optimize interactions with users and customers.

edge computing market

Fortune Business Insights analysts highlight several key factors that are driving the rapid growth of the Edge Computing industry. One of them is the growing use of all kinds of devices that generate data: Internet of Things ( IoT ) equipment, smart cameras, industrial PCs, medical sensors, manufacturing systems, etc. According to industry experts, by 2025, 75% of information will be generated outside of centralized data centers . New technologies such as Industry 4.0, artificial intelligence , and IoT will drive demand for edge computing. In addition, the need for Edge Computing platforms is increasing amid the expansion of 5G infrastructure and the emergence of qualitatively new applications related to virtual, augmented and mixed reality, as well as metaverses.

The authors of the study cite high initial investment as the main constraint: deploying and maintaining edge infrastructure can significantly increase companies’ capital expenditures. In addition, there are certain difficulties associated with maintaining the required level of protection. Ensuring the security of the entire computing network leads to huge costs for suppliers, thereby holding back market expansion.

The list of key players in the industry includes:

  • IBM ;
  • Intel ;
  • Amazon ;
  • Google ;
  • Microsoft ;
  • Adlink;
  • Hewlett Packard Enterprise Development;
  • Cisco ;
  • Huawei ;
  • EdgeConneX .

By application, the Edge Computing platform market is divided into IoT applications, robotics and automation, predictive maintenance, remote monitoring, smart cities, and others. In 2023, the first of these segments accounted for 28.1% of all costs. Another 21.3% of revenue came from predictive maintenance services. From a geographical point of view, North America generated the maximum revenue in 2023 – approximately $5.16 billion. This is due to the high concentration of large players, including IBM, Intel, Microsoft, etc. These corporations are strategically expanding their geographic presence and customer base by acquiring small local companies. At the same time, the Asia-Pacific region is demonstrating high growth rates, which is associated with the growing implementation of edge solutions in countries such as India and China .

By the end of 2024, revenue in the global edge computing market is estimated at $21.41 billion. Fortune Business Insights analysts believe that the CAGR (compound annual growth rate) will be 33.6% going forward. As a result, global spending will reach $216.76 billion by 2032.

2022: $84 Billion Invested In Cloud And Edge Computing And IOT Projects Worldwide

In 2022, investments in cloud and edge computing platforms worldwide reached $84 billion. At the same time, the pace of migration of companies and government organizations to the cloud slowed compared to the previous year. This was reported on July 20, 2023, by the international consulting firm McKinsey . More details here .

2021: Integration of the Edge Computing And IOT Internet of Things (IOT)

Initially, organizations simply developed strategies to deploy and manage the Internet of Things at the edge. But the edge is now everywhere. As more companies adopt edge computing, they also face new IoT challenges.

Here are five areas that may create some confusion, along with recommendations for IT teams on what they can do now to be better prepared.

1. Integration of the Internet of Things and peripheral devices

There are several levels of integration where IoT and edge technologies pose challenges. The first level is the integration of IoT and edge with core IT systems deployed in manufacturing, finance, engineering, and other areas. Many of these core systems are legacy. If they do not have APIs that enable integration with IoT technology, then batch ETL (extract, transform, load) software may be required to load data into these systems .

The second area of ​​challenge is the IoT itself. Many IoT devices are built by independent vendors. They use their own proprietary operating systems . This makes it difficult to “mix and match” different IoT devices into a single edge architecture. Governments are now concerned about introducing uniform security and compliance standards for IoT vendors who want to do business with governments, which should encourage IoT vendors to standardize. The next step will likely be greater standardization of IoT operating systems and communication protocols, making integration easier.

The security and compliance landscape for IoT regulators is still evolving, but it will improve over the next few years. In the meantime, IT professionals at organizations can already ask potential IoT vendors what is already available for heterogeneous integration and whether they plan to provide interoperability in future products.

When it comes to integrating legacy systems, ETL is one integration option that can help if APIs for the systems are not available. Another alternative is to write APIs, but this is time-consuming. The good news is that most legacy system vendors are aware of the coming IoT wave and are already developing their own APIs if they haven’t already. IT departments should reach out to major system vendors to see what their plans are for IoT integration.

2. Security

With the passage of new cybersecurity laws, IoT security compliance should become easier. However, this will not solve the problem that monitoring and securing the edge IoT will likely be the responsibility of end users who are inexperienced in security administration.

It is important to train end-user “para-IT” staff in the basics of securing the edge IoT. This training should include training in IoT cybersecurity, as well as securing IoT equipment in locked-down, restricted areas where possible.

Access is another important security issue. It is recommended that only authorized personnel who have undergone internal security training be allowed into IoT security areas.

3. Support

Maintaining IoT equipment and networks, addressing device failures, security, software updates , or adding new equipment, is a daily task.

End users can monitor those parts of the edge IoT that are directly related to their operations, and it makes sense for IT to take on the overall maintenance and support, as both are core areas of IT expertise.

The first step is to make sure you are aware of what is happening. With the growth of shadow IT, end users are turning directly to vendors to purchase and install IoT for their operations. To detect these new additions, IT can use corporate networks and asset discovery software that identifies any new IoT additions or changes. However, IT, senior management, and end users can agree on the types of on-site support and when IT should take over these functions.

4. Vitality

When IoT is deployed in hazardous or hard to reach areas, it is important to use solutions that can be self-sufficient and require minimal maintenance over long periods of time.

These requirements must be met by IoT that operates in harsh conditions of extreme heat or cold, or harsh working conditions. Many off-the-shelf IoT devices may not meet these requirements.

It may also be important to find IoT solutions that can sustain themselves for long periods of time without the need for replacement or ongoing maintenance. It is not uncommon for IoT devices to have a life cycle of 10 to 20 years. The frequency of maintenance for such devices and sensors can be increased if they can be powered by solar energy (and therefore rely less on batteries), or if they are only awakened from sleep mode when movements or other events are detected (for monitoring).

To minimize maintenance and field inspections, survivability needs to be included as one of the requirements for edge IoT solutions.

5. Bandwidth

Most IoT devices are bandwidth efficient, but as more devices and sensors are deployed and more data is collected and transmitted, bandwidth availability can become a serious (and costly) issue that can compromise network performance and the ability to deal with real-time data.

Many organizations choose to deploy distributed IoT systems at the edge, where they can use local communications links. Payload data can be sent to more centralized data collection points across the enterprise at the end of the day, or perhaps periodically throughout the day, whether those points are in an on-premises or cloud environment. This approach to distributed computing minimizes long-distance bandwidth use and also allows data transfers to be scheduled for less congested (and less expensive) times of day.

Another element to consider is what IoT data do you really need to collect? This is a question that data architects and the end business should be involved in answering. By agreeing on and eliminating data that the business does not need, you can reduce the data workload and save on bandwidth, as well as data processing and storage.

2020: MEC Edge Computing Investments to Triple by 2025

Juniper Research experts predict that by the end of 2020, telecom operators will spend $ 2.7 billion on edge computing technologies with multiple access , and by 2025 the amount will grow to $8.3 billion.

The number of MEC nodes (i.e. access points, base stations, routers) used worldwide should reach 2 million by 2025. This year, their number is about 230 thousand. The equipment will allow for the efficient management of data arrays generated by smart city systems, transport, and other services.

Experts suggest that increasing the number of MEC nodes will lead to an increase in the quality of services such as music streaming, digital TV, and cloud gaming. The benefits of MEC implementation will be felt by more than 920 million users in 2025.

2019

Forrester Research: 2020 Will Be the Year of Edge Computing Breakthrough

In early November 2019, Forrester Research published a study that said 2020 would be the year of edge computing breakthrough.

While the phenomenon is primarily associated with the rise of the Internet of Things, experts say the need for fast, on-demand computing and real-time application execution will also drive the growth of edge computing.

future of edge computing
Forrester Research has published a study that says 2020 will be the year of edge computing breakthrough.

Ultimately, this intense development of the edge will lead to the fact that in 2020, traditional servers will no longer play such a big role. For example, a self-driving car will no longer be able to use them, which means it will need an alternative. As a result, telecommunications companies will become more important in the cloud and distributed computing markets.

Forrester analysts believe that large telecom companies, especially those that were late to the cloud market for one reason or another, will soon begin to aggressively acquire data delivery network operators to catch up on edge computing. In addition, telecom operators will invest in open-source projects such as Akraino, a software stack using edge computing.

But the biggest impact telcos will have on edge computing in 2020 will come from the rise of 5G networks , according to Forrester analysts. While these networks will initially be limited to major cities, that’s enough to make companies reconsider their approach to edge computing.

The rise of edge computing in 2020 driven by the spread of 5G networks.

If companies are interested in this area, they will undoubtedly be attracted by such capabilities as intelligent processing of real-time video, 3D mapping to improve worker productivity, and the use of special scenarios for autonomous control of robots or drones . CDN vendors such as Ericsson , Fastly, Limelight, and Akamai have launched edge computing solutions or are preparing to do so in the near future, according to a November 2019 report.

While most enterprises still look at CDNs as a solution for caching content in their web and mobile applications, the network’s capabilities can be used for much broader purposes.

Beyond telecommunications companies, there are many players in the computing industry interested in edge computing. Recently, commercial entities have had an urgent need to interact with customers in real time, regardless of where they are located. This is due to vendors’ desire to maintain consumer loyalty.

As a result, software makers in all sectors, from healthcare to utilities to heavy industry, will need customizable edge devices to enable communication and control, remote patient care, or remote maintenance. In addition, large cloud providers will seek to consolidate their market share, and AI startups will seek to add new functionality to their applications.

According to experts, solutions created by several manufacturers will be the most popular in the market, since few vendors have their own products that are designed for all areas of IoT and edge computing. Therefore, in 2020, integrators that can combine the delivery of products and services from many different suppliers into a single system will be in particular demand.

Linux Foundation: Edge Computing Will Become More Important Than Cloud

Speaking at the Open Networking Summit in Belgium in September 2019, Linux Foundation networking project manager Arpit Joshipura said that edge computing will become more important than cloud computing by 2025.

By edge computing, he meant computer resources and data storage technologies that are located at a distance from each other where information can be transmitted in 5 to 20 milliseconds.

edge computing applications
Linux Foundation Says Edge Computing Will Become More Important Than Cloud Computing by 2025

According to Arpit Joshipura, edge computing has the potential to be an open environment that can seamlessly interact with others. It should be independent of hardware, silicon, cloud, or operating system .

Open edge computing should also work with any adjacent projects that use it: Internet of Things ,telecoms, cloud or enterprise solutions.

LF Edge partners are building a suite of software tools that unite the fragmented edge computing market around a common, open concept that will underpin the market of the future.

Tom Arthur, co-founder and CEO of Dianomic Systems (part of the development of LF Edge), believes that an open, ready-to-interoperate platform is needed in edge computing, especially in industrial, manufacturing and resource industries , where “almost every field system, piece of equipment or sensor uses its own proprietary protocols and data definitions.”

The Linux Foundation sees video content delivery systems, gaming, mobile edge computing 5g networks, driverless cars , and virtual and augmented reality technologies as the main catalysts for growth in demand for edge computing .

Transworld Data: Edge Computing Requires Reworking Disaster Recovery Plans

With information systems and applications scattered across enterprises and clouds, IT leaders are having to rethink their disaster recovery plans, writes Mary Shacklett, president of consulting firm Transworld Data, on InformationWeek.

For years, it has been the responsibility of IT departments to create disaster recovery (DR) plans. But now those plans must be redesigned to account for edge and cloud outages. What’s new, and how are organizations rethinking their plans?

1. IT departments do not control the periphery

With the rise of edge and other distributed computing, IT departments can no longer rely on standard DR plans designed for data centers. For example, if robots and automation are used in manufacturing, workers and line managers are the ones managing them. They must also ensure that these assets are kept in a safe place when not in use. Often, they install, monitor, and maintain them themselves, or contact manufacturers.

These employees do not have experience in securing or protecting assets and maintaining/monitoring them. At the same time, the emergence of new edge networks and solutions without IT involvement multiplies the number of assets that can fail. To cover these assets, DR and failover plans must be documented and staff trained to act in accordance with these plans. The most logical place to do this is within the framework of the IT department’s existing DR and business continuity plan.

When reviewing the plan, IT should collaborate with those using the various types of edge computing. It is important to involve each of them in documenting the appropriate DR and failover plan and testing the plan regularly.

2. Cloud applications are an additional burden

In 2018, Rightscale surveyed nearly 1,000 IT professionals and found that the average company uses 4.8 clouds.

It would be interesting to know how many people at these companies have documented DR procedures in case of cloud outages. A survey of cloud providers found that almost all of them have a clause in their contracts that exempts them from liability in the event of a disaster.

The takeaway here is that you should include each cloud provider you use in your DR plan. What are the SLAs for backup and recovery? Do you (or your provider) have a plan in place to deal with cloud outages? Do you have an agreement with your provider to test the applications you use in the cloud annually for failover in the event of a DR?

3. The importance of physical security

As IT moves more and more toward the periphery, making its way into manufacturing or branch offices, physical security becomes more intertwined with DR. What happens if a server in a remote office overheats and fails? Or if an unauthorized employee enters a fenced-off area on the shop floor and sabotages a robot ? A DR plan should include regular inspection and testing of equipment and assets in remote locations, not just in the main data center.

4. In the event of a disaster, it is necessary to maintain a stable exchange of information

A few years ago, an earthquake caused little damage to one bank’s data center , but destroyed networks throughout the disaster area. Cashiers at branches had to manually enter customer transactions into registers so they could be entered into the system after the earthquake had cleared up.

One of the customers asked the cashier what had happened. And she answered: “All our computers are out of order.” This information spread like wildfire among customers and was disseminated in the media. As a result, many customers rushed to close their accounts.

These situations are exacerbated when IT assets are managed by multiple people, as is the case with edge computing. That is why it is so important to create a “ tree ” of information exchange that shows who communicates what to whom in the event of a disaster. This procedure must be strictly followed by all employees.

In a normal case, the “voice” of a company is its PR department, which coordinates with the company’s management and makes statements about the disaster to the community and the media. If this information channel is unreliable and if employees are not aware of its existence, more time can be spent dealing with the consequences of incorrect information than the disaster itself.

5. The DR plan should cover various geographic locations

Given the growing adoption of edge computing and remote offices, it goes without saying that a DR plan can no longer be limited to a single location or data center. Especially if you are using clouds for disaster recovery, you should choose cloud providers that have multiple data centers in different regions. This will allow you to failover to a healthy geographic location in the event of a primary data center or cloud storage outage. This scenario should be included in the DR plan and tested.

6. The DR plan testing methodology should be revised

If you plan to move more functions to the cloud and deploy more edge computing, you need to consider additional test scenarios for your DR plan and ensure that you have documentation and testing for both the cloud and the edge. You need to be sure that your DR plan will work in any scenario that you decide to put it into action.

7. Management must support the DR plan not only in words

Cloud and edge computing make disaster recovery more complex. Therefore, most organizations must review and revise their DR plans. This will take time to address a task that is no longer a top priority for most organizations and is far down the list of their long list of priority projects.

With the changes in IT brought about by the advent of cloud and edge computing, CIOs must explain to management and the board how these changes affect the DR plan and convince them to invest the time and effort into revising it.

8. Ensure that edge IT vendors and cloud providers are involved in the implementation of the DR plan

As mentioned, most cloud providers do not provide DR and failover guarantees in their contracts. Therefore, the provider’s DR obligations should be included in the RFP and should be an important point of discussion before signing a contract.

9. Network redundancy is of great importance

Many organizations focus on restoring systems and data in the event of a disaster, with less attention to networks. However, given the role of the Internet and global networks today, disaster failover and network redundancy should also be included in a DR plan.

Edge Computing Vs Cloud Computing

Cloud computing and edge computing are two paradigms for processing and storing data, each with its own characteristics and use cases. Cloud computing refers to the centralized storage and processing of data on remote servers accessed over the Internet, allowing users to leverage powerful resources without the need for on-premises infrastructure. This model is ideal for applications that require significant processing power and scalability, such as big data analytics and machine learning. In contrast, edge computing involves processing data closer to the source, such as IoT devices or on-premises servers, which reduces latency and bandwidth usage by minimizing the distance the data must travel. This approach is especially useful for real-time applications, such as autonomous vehicles or smart manufacturing, where immediate data processing is critical. Ultimately, while cloud computing excels in scalability and resource availability, edge computing provides speed and efficiency for time-sensitive tasks. **Short answer:** Cloud computing centralizes data processing on remote servers, which is ideal for scalability, while edge computing processes data closer to the source, reducing latency and improving the performance of real-time applications.

Advantages and disadvantages of cloud and edge computing?

Cloud computing and edge computing offer their own advantages and disadvantages. Cloud computing provides centralized data storage and processing, providing scalability, cost efficiency, and ease of management for large data sets. However, it can suffer from latency issues due to its reliance on internet connectivity and can pose security risks if sensitive data is transmitted over networks. In contrast, edge computing processes data closer to the source, reducing latency and improving real-time decision making, which is critical for applications such as IoT and autonomous vehicles. However, edge computing can introduce increased complexity in managing distributed systems and may require a larger initial investment in infrastructure. Ultimately, the choice between cloud and edge computing depends on specific use cases, performance requirements, and budgetary considerations.

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