What is IOT Edge Computing?
No matter the industry in which you operate — retail, healthcare, manufacturing, education, telecommunications, or anything else — the work you do is increasingly being facilitated by IoT edge computing. This essentially means that you are using Internet of Things (IoT) applications or IoT devices, otherwise known as smart devices, that collect, send, receive, and analyze data. These IOT devices could be anything from sensors on machines or people to smartboards to any piece of hardware in your work environment that is connected to the internet or your IT network.
The large amounts of data being gathered and transmitted by these various IoT devices has to be analyzed and processed somewhere, and that’s where edge computing comes into play. Imagine if all that data had to travel back to a company’s central data center for processing, then to send data back to the devices for consumption. It would take some time. As businesses and people increasingly rely on and expect access to real-time data to make decisions and get work done in the moment, companies must move computing and storage closer to customers, associates, and devices so they can process data locally. This improves the users’ experience and allows people to immediately benefit from what the data is telling them. Thus, the network edge continually grows and expands.
Edge vs. Cloud vs. Fog Computing
Edge computing has a close relationship to cloud computing and fog computing concepts. Although the concepts overlap, they are not interchangeable. It's easy to understand how they differ by simply identifying their common themes: All three concepts refer to distributed computing and concentrate on the physical deployment of compute and storage resources in relation to data produced. The differences lie in their location. While edge computing is closer to the data source leading to higher speeds and lower latency; cloud data centers house more powerful machines than those deployed at the edge thereby offering better scalability on larger datasets; finally fog nodes are able to bridge this gap by combining features from both ends - being device-centric as well as providing strong computational power for analytics purposes.
What Is the Network Edge for Edge Computing?
The network edge is distributed IT deployed where the data is consumed — in retail stores, medical clinics, branch offices, university campuses, manufacturing plants, or home offices, to name just a few examples. With every organization becoming more digitized, the number of these sites is increasing, and their role is changing and becoming more important. As a result, all these smaller compute sites have been grouped into a single category or type of critical facility called edge data centers. This category essentially encompasses anywhere edge computing gets done. The digital transformation we will see in the next decade, including the rise of 5G, will be enabled by edge processing.
The problem is that IoT edge computing presents many challenges for companies and data center managers. When a company’s centralized data centers or core data centers are planned and built, the data processing facilities are specifically constructed around the needs of mission-critical IT equipment and devices. Power, thermal management, security, and access are all fully taken into account well in advance of constructing the data center facility where servers and the foundation of the company’s IT backbone will live.
But that is rarely the case with IoT edge computing sites. Rather, companies use whatever space they have available to serve as their network and edge computing architecture. It could be an unused office, a back room, or even a storage closet. Obviously, these spaces aren’t really set up to accommodate the power and cooling needs of servers and the other edge computing devices needed to process data and ensure companies can reliably and securely meet the increased digital demands being placed on their businesses every day. That’s why companies need an on-prem data center at the edge built for IoT edge computing.
Key Considerations for Data Center Edge Architecture
Making the right physical IT infrastructure choices is critically important for edge data centers given that many deployments are in locations where additional support and protection is required. For an IoT edge computing site to do its job, it must provide storage for the IT equipment, usually in the form of a network rack or enclosure. These spaces must also have a power and power distribution solution that factors in backup power for the equipment should the main utility power go out or be disrupted for any reason. Cooling solutions that keep the operating temperature just right for all sensitive IT equipment are equally important. In addition, because many locations where edge devices exist lack dedicated, on-site IT staff, companies must think about how to give their IT teams remote access to the equipment and visibility to that edge device and IT environment. Beyond physical equipment, they also need the right edge computing infrastructure management software solutions. Of course, they can’t forget about cyber and physical data security.
It’s a lot to consider. The situation is further complicated by the fact that most organizations have many more than just one IoT edge computing site to think about. After all, the whole point of IoT edge computing is to put the compute resources and power closer to the company’s IoT devices, which naturally leads to multiple distributed edge locations. As you can probably guess, no two edge locations are exactly the same.
Benefits of Standardized Edge Computing Architecture
While each edge site's performance, environmental, physical location, and form factor challenges are unique, so are each company’s scale, speed, and IT complexity requirements. Most companies do not have the time or resources to architect a customized plan for every IoT edge computing site in their network. Given the rapid rate of digital transformation today, solid IT infrastructure needs to be quickly deployed in remote locations as efficiently as possible.
Micro data centers are integrated data centers or self-contained rack solutions that power, cool and monitor your system to ensure your business is always up and running. Micro data centers and multi-rack micro modular data centers fully integrate all sub-systems for a turnkey solution. They offer power protection and distribution, cooling and intelligent, outlet-level monitoring where you need it when you need it. And because every situation comes with unique constraints and specifications, many pre-built micro and micro modular data center options can be designed to meet your specific needs and then standardized across multiple edge data center locations. Micro data center designs are also built for rugged industrial conditions like factory floors, which offer a secure enclosure protecting sensitive equipment from particulates and unauthorized access.
IoT Edge Computing: How Do IoT and Edge Computing Work Together?
A typical IoT architecture is that smart devices transmit data to the cloud and analyze it remotely. High amounts of information traveling between and into the device may result in bottlenecks and can be harmful to the device's performance. Modern edge computing brings data processing closer to connected devices. The system is optimized for shortening the data routes and performing instant on-location analysis.
Edge Devices vs. IoT Devices
IoT edge computing relies on both edge devices as well as IoT devices, and some devices may have the same terminology. A computer or other device may become an edge resource if it processes data locally or makes low-latency decisions. Also, edge devices can be considered IoT devices if they have sensors that can generate raw data.
Edge Computing Brings Data Processing as Close to an Internet of Things (IoT) Device as Possible
Edge Computing is redefining the way data processing and analysis happens for Internet of Things (IoT) devices. It allows real-time information to be made available by bringing computation as close to the IoT edge device level as possible, reducing network traffic significantly. This allows a unique benefit for monitoring systems, enabling real-time analysis and action without relying on significant network traffic often associated with large-scale analytics platforms. With edge computing technology in use, IoT sensor data can analyze data locally and selectively send back data to centralized locations depending on the desired results; this significantly optimizes bandwidth usage while still providing efficient access to valuable insights gathered at an IOT device's 'edge'.
Role of Machine Learning in IoT Edge Computing
Edge Machine Learning is a byproduct of the proliferation of Internet of Things connected devices. IoT brought a proliferation of Smart Devices connected to the Cloud, but it quickly became apparent that the network wasn't equipped to handle the spike in demand. Congestion and security challenges plagued Cloud computing. Edge machine learning, however, allows Smart Devices to process data locally through machine and deep learning algorithms, thereby minimizing dependence on Cloud networks. In addition, the Edge Machine Learning approach lessens security concerns as it enhances the privacy of sensitive data. By shifting the emphasis to processing data at the device- or local level, the wider network is ready to cater to the huge demand from Smart Devices. Overall, Edge ML is a significant breakthrough and a boon for companies that require real-time data processing capabilities.
What are IoT Edge Devices?
In the world of the Internet of Things (IoT), data storage and processing are critical components for connected systems. Devices that collect and transmit data can either send it directly to the cloud or an edge device for processing and instruction. This decision is based on parameters that dictate the optimal course of action. M2M systems allow for multiple connected devices to perform tasks as instructed by an edge computing device or a network. A router can facilitate control of data flow through extended access to devices and provide greater visibility and actionable insights, both locally and over larger distances. One of the key components of IoT edge devices is their ability to trigger a response from an actuator. The synergy between sensors, actuators, and edge computing devices provides visibility and the capability to act over long distances, which is critical in IoT systems. This results in an interconnected ecosystem where data is analyzed and actions are taken automatically. Thus, IoT edge devices are critical components that enable efficient communication, processing, and management of data flow in modern connected systems.
What is the Industrial Internet of Things (IIOT)?
The Industrial Internet of Things, or IIoT, is revolutionizing how industries operate. This technology refers to the interconnectivity of sensors, instruments, and other devices that are networked together with computers' industrial applications. By allowing for data collection, exchange, and analysis, IIoT has the potential to facilitate significant improvements in productivity and efficiency, benefiting both manufacturers and energy managers. What sets IIoT apart from previous technologies is its ability to utilize cloud computing, resulting in a higher degree of automation and refinement of process controls. It is an exciting development in the world of industry and holds immense promise for future economic growth and prosperity.
IIoT and Edge Computing Use Cases
Industrial IoT or IIoT describes the use of IoT in industrial contexts, e.g. machines at factories. Imagine how heavy machinery operates in factories. Equipment may be stressed differently over the long term and during operation, breakdowns are normal. IoT sensors can be attached to equipment with a high risk of failure and over-usage. These sensors will enable predictive maintenance, reducing total downtime. Because factories can be dirty, dusty places and IIoT edge computing prioritizes local processing, it may be necessary to have special enclosures for edge devices that block out dust and dirt particles while keeping the equipment at an optimal working temperature.
What are the advantages of Edge Computing for IoT?
Edge computing offers several advantages for businesses implementing IoT applications. Business owners can rely on consistent connectivity even if cloud services are compromised by utilizing local edge data centers for data storage and processing. This is particularly important for IoT applications that require reliable and fast communication in varying levels of connectivity. Data processing near or at their source reduces data transmission time for central locations and reduces server and resource usage resulting in improved network performance – a key pillar of business continuity.
Edge sites share key characteristics that help determine equipment selection and architecture decisions, allowing IT infrastructure solution providers to offer integrated and prefabricated edge architecture solutions.
These options include prefabricated racks, rows, aisles, and modular data centers that can help companies standardize design and systems across multiple edge sites while streamlining deployment times and reducing the costs to implement, manage, and replicate their computing resources. Solutions built specifically for edge deployments often integrate rack space, power, cooling, and monitoring in one package. They are typically available in several configurations, sizes, and capacities, with enough options to give companies the flexibility to meet specific needs. Yet, they still dramatically simplify the infrastructure selection process. Some solutions can be delivered in days and installed in hours. It can be a simple matter of plug and play to realize the many benefits of IoT edge computing anywhere and everywhere your network is expanding.
IoT Edge Computing Solutions Available at Vertiv™
As the Architects of Continuity™, Vertiv delivers a broad portfolio of intelligent infrastructure, edge systems, and software and services that address the reliability, scalability, and management challenges companies in various industries face as their edge evolves.
