Edge computing is revolutionizing the way data is processed. It is like bringing the power of a big, central computer closer to where the action is happening, right at the edge of the network. Imagine you have a smart device, like a security camera, that needs to analyze video footage quickly. Instead of sending all the video data to a distant data center, the camera or a nearby device does the processing. And IoT devices have shortcomings—-there is simply too much data coming and going. The volume is overwhelming, and more expensive and expansive connections to clouds and data centers are needed. 

Distant data processors aren’t always reliable, especially in a time of need. When processing power is placed “on the edge” or closer to the devices, the benefits of edging are seen. This means less latency and downtime, faster connections, and less device damage. Edge computing does not mean that long-term data storage is now meaningless—they work together. 

Read on to learn more about the history behind edge computing, the benefits and applications, and AI’s role in its presence. 


Edge Computing Explained

What is edge computing?

Data processing has become more complex with the rise of AI and the cloud. Edge computing aims to help overwhelmed networks and infrastructure process data more quickly and efficiently. In layperson’s terms, edge computing is computing as close to a data source as possible, which has multiple advantages; therefore, many companies are saying goodbye (sort of) to distant data centers and hello to on-site processing.  

Why is it important?

Getting data to a distant data center, which can take time and use a lot of internet bandwidth, can cause issues depending on the need for the data. Going back to the security camera example, a house owner without an alarm system needs to know ASAP if there is an intruder or suspicious event on their property while they are away. If the data are latent, they might need more time to call the police. There are even more life-threatening scenarios when edge computing will be beneficial, such as aviation safety, emergency response during a natural disaster, or receiving a serious medical diagnosis. 

Aside from these grave scenarios, edge computing has advantages in office settings, corporations, hospitals, etc. According to an IBM article on the topic, the author writes:

“Edge computing offers a more efficient alternative; data is processed and analyzed closer to the point where it’s created. Because data does not traverse over a network to a cloud or data center to be processed, latency is reduced. Edge computing—and mobile edge computing on 5G networks—enables faster and more comprehensive data analysis, creating the opportunity for deeper insights, faster response times and improved customer experiences.”

Some other noted benefits include cost efficiency, better data security and privacy protections, and overall reliability. 

What is the history of edge computing? 

  • Before the 1970s, when big tech companies such as Apple and Microsoft began thriving, computing was basic, and computers took up entire rooms. Individuals soon owned personal computers at home and in the office, and not too long after, the World Wide Web was invented. After that, things swiftly began to change, eventually leading to the rise of edge computing. 
  • 1980s: Client-server architecture became prevalent, marking the beginning of moving computation closer to the data source by distributing tasks between clients and servers.
  • 1990s: Content Delivery Networks (CDNs) were developed to reduce latency by caching content closer to users. Akamai Technologies, founded in 1998, became a prominent player in this field.
  • 2000s: Mobile computing gains traction with the rise of smartphones, necessitating efficient, localized data processing. The popularization of “cloud computing” emphasized centralized data processing, but its latency and bandwidth limitations led to the exploration of edge solutions. Later, Cisco introduced “Fog Computing” to emphasize the need for decentralized infrastructure to handle the growing data from IoT devices.
  • 2010s: In the early 2010s, the Internet of Things (IoT) growth increased the need for edge computing to manage data locally, reducing latency and bandwidth usage. In 2012, the European Telecommunications Standards Institute (ETSI) introduced the concept of Mobile Edge Computing (MEC) to bring cloud capabilities to the edge of mobile networks. By the mid-2010s, major tech companies like Amazon, Microsoft, and Google began offering edge computing services and solutions. In 2016, the OpenFog Consortium was established to develop standards and architectures for fog and edge computing.
  • 2020s: The COVID-19 pandemic accelerated digital transformation and the adoption of edge computing to support remote work, online learning, and telehealth. Soon after, the integration of edge computing with 5G networks enhanced its capabilities, enabling faster and more reliable data processing closer to users. The OpenFog Consortium merges with the Industrial Internet Consortium (IIC) to form the IIC, continuing the push for edge computing standards and innovation.

Edge computing continues to evolve with technological advancements, especially AI. We can only imagine what heights it will soar to within the next decade. 

The applications of edge computing

According to the research, the global market for edge computing is projected to reach USD 116.5 billion by 2030, expanding at a compound annual growth rate (CAGR) of 12.46% from 2022 to 2030. Edge computing provides realistic and effective applications across various industries, and it’s not going anywhere. 

An article from Xenonstack lists six industry-specific applications that ultimately make work in these areas run more smoothly and securely. 

  • The banking and finance industry uses edging computing for ATM security and data privacy. 
  • The manufacturing industry uses it for condition-based monitoring and predictive maintenance. 
  • The retail industry utilizes edge computing for big data analytics and inventory management. 
  • The automobile industry uses it for driver assist, predictive maintenance, and driver monitoring. 
  • The healthcare industry uses edge computing to ensure higher patient care and overall health and safety. 
  • The agricultural sector utilizes it to monitor soil quality, animal health, and crop analysis. 

While this isn’t a comprehensive list of edge computing applications, these are clear examples of how they can help an industry be more productive and efficient. 

Edge computing’s role in data processing 

Edge computing plays a significant role by bringing computation and data storage closer to where it is needed, rather than relying solely on a centralized data-processing warehouse or cloud. Here are some key roles it plays:

Reduced Latency

Edge computing reduces latency by processing data closer to its source, crucial for real-time applications like autonomous vehicles, industrial automation, and augmented reality.

Bandwidth Optimization

Edge computing reduces bandwidth usage and costs by locally processing data, minimizing the need for transmission to central servers or the cloud.

Improved Reliability

Edge computing enhances system reliability and continuous operation in critical applications by allowing local data processing even without cloud connectivity, which is crucial in healthcare industries.

Enhanced Security and Privacy 

Edge computing enhances security and privacy by processing sensitive data locally, reducing transmission risks, and aiding compliance with privacy regulations.


Edge computing efficiently handles large IoT data volumes by distributing the processing load across multiple devices, allowing scalable solutions.

Contextual Processing

Edge computing enables immediate, localized decisions in context-aware applications like smart homes, predictive maintenance, and environmental monitoring.

Cost Efficiency

Edge computing reduces the need for costly centralized infrastructure and lowers operational expenses, making it economically beneficial.

Support for AI and Machine Learning

Edge computing devices run local AI models, which are crucial in real-time decision-making in facial recognition, predictive analytics, and anomaly detection.

It is clear that edge computing benefits individual companies, organizations, and entire industries. 

Are you interested in learning how the tech company Verizon uses edge computing? Their site promotes several real-world case studies that provide interactive learning with specifics, diagrams, and more. 

What is the role of AI in edge computing? 

Artificial Intelligence continues to experience exponential growth, leading to innovations and discoveries that allow for new, beneficial applications across various industries. Edge computing and AI are like synchronized dancers, seamlessly coordinating their movements to deliver precise performances in real time. 

In an article by IBM, the author describes AI on the edge: “Simply stated, edge AI, or “AI on the edge,” refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection.”

Edge computing is monumental on its own, and adding AI will take it to the next level. 

In a piece by CIO, the author touts Dell as innovative and forward-thinking regarding AI and edge computing. Dell’s NativeEdge, edge servers, gateways, and storage solutions each incorporate AI on the edge. 

Cisco Systems, Intel, Hewlett Packard Enterprise, and Cohesity are just a few other large companies that integrate AI and edging on another level. 


Edge computing is revolutionizing data processing by bringing computation and storage closer to where they’re needed. It helps reduce latency and enhance reliability across critical applications like healthcare and autonomous vehicles. Its integration with AI facilitates real-time decision-making and deeper insights, driving innovation in industries from healthcare to manufacturing and beyond. As the market grows and technologies evolve, edge computing, along with AI, continues to demonstrate its pivotal role in shaping the future of digital infrastructure. 

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